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Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications

4ae30d49c8cc07e42d5a871efb9bcfba?s=47 Gregory Kapfhammer
January 17, 2012
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Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications

Interested in learning more about this topic? Visit this web site to read a related paper: https://www.gregorykapfhammer.com/research/papers/Cobb2011/

4ae30d49c8cc07e42d5a871efb9bcfba?s=128

Gregory Kapfhammer

January 17, 2012
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  1. Using Dynamic Invariant Detection to Support the Testing and Analysis

    of Database Applications Gregory M. Kapfhammer† Department of Computer Science Allegheny College http://www.cs.allegheny.edu/∼gkapfham/ University of Ulm – January 17, 2012 †Joint with Mary Jean Harrold and Jake Cobb (GA Tech), James A. Jones (UC Irvine), Jonathan Miller Kauffman (Allegheny)
  2. Introduction Database Applications Dynamic Invariants Conclusion Important Points Accessing the

    Presentation Scan this QR Code with your smartphone! ... or, visit this Web site: http://www.cs.allegheny.edu/˜gkapfham/ulm2012.pdf ... or, ask me for a USB drive! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  3. Introduction Database Applications Dynamic Invariants Conclusion Important Points Presenter Introduction:

    Gregory M. Kapfhammer test testing software suites prioritization components coverage empirically regression suite applications evaluating algorithm analysis data effectiveness empirical genetic performance understanding using approach commercialofftheshelf comparison comprehensive constrained creation databasecentric environments execution finding framework identifying interactive java methods multiplots mutation party prioritized reduction relational study third timeaware towards 105 adequacy approaches array automatically building call challenges chapter communication compare compressing computer conditional cost cots covering creating criteria database databaseaware databasedriven databases declarative dependable detection devices distributed distributing duringsearchbased dynamic efficiency efficient engineering environment evaluate examination executing experimental family flow forward frameworks free generation greedy gui hamiltonian handbook heaps implementation improve incorporating increase information initial intranode invariant javaspace javaspacebased kernel knapsack linux measurement memory method monitoring operators paths poster potential preliminary primitives prioritizations prioritizers prioritizing problematic receive remote reports resource resourceconstrained results role runtime science searchbased selection solutions solvers space studies studying supported synthetic techniques transmission transparently trees tuple unstructured wrappers Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  4. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  5. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  6. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  7. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  8. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Household Appliance Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  9. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Household Appliance Program Network Router Program Scientific Device Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  10. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Household Appliance Program Network Router Program Scientific Device Program Computer Server Program Desktop Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Network Router Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  11. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Household Appliance Program Network Router Program Scientific Device Program Computer Server Program Desktop Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Network Router Program Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  12. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Household Appliance Program Network Router Program Scientific Device Program Computer Server Program Desktop Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Network Router Program Program Program Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  13. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Household Appliance Program Network Router Program Scientific Device Program Computer Server Program Desktop Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Network Router Program Program Program Program Program Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  14. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Household Appliance Program Network Router Program Scientific Device Program Computer Server Program Desktop Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Network Router Program Program Program Program Program Program Program Program Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  15. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Everywhere Computer Server Program Computer Server Program Desktop Computer Program Desktop Computer Program Mobile Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Household Appliance Program Network Router Program Scientific Device Program Computer Server Program Desktop Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Network Router Program Program Program Program Program Program Program Program Program Computer Server Program Desktop Computer Program Mobile Computer Program Household Appliance Program Scientific Device Program Network Router Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  16. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software Complexity and Data Enormity Computer Software Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  17. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software Complexity and Data Enormity Computer Software Lines of Code Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  18. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software Complexity and Data Enormity Computer Software Lines of Code Numerous Features Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  19. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software Complexity and Data Enormity Computer Software Lines of Code Numerous Features Feature Interactions Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  20. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software Complexity and Data Enormity Computer Software Lines of Code Numerous Features Feature Interactions Execution Environments Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  21. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software Complexity and Data Enormity Computer Software Lines of Code Numerous Features Feature Interactions Execution Environments Software entities are more complex for their size than per- haps any other human construct - Frederick P. Brooks, Jr. Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  22. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software Complexity and Data Enormity Computer Software Lines of Code Numerous Features Feature Interactions Execution Environments Software entities are more complex for their size than per- haps any other human construct - Frederick P. Brooks, Jr. Prediction: in 2011, 1.8 zettabytes (i.e., 1.8 trillion giga- bytes) of data will be created - IDC Digital Universe Study Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  23. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Evolving Execution Environment Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  24. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Evolving Execution Environment Program Execution Environment Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  25. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Evolving Execution Environment Program Execution Environment Program Program Changed because of the addition of a new feature or the correction of a defect Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  26. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Evolving Execution Environment Program Execution Environment Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  27. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Evolving Execution Environment Program Execution Environment Program Execution Environment Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  28. Introduction Database Applications Dynamic Invariants Conclusion Software and Data Challenges

    Software and Data are Evolving Execution Environment Program Execution Environment Program Execution Environment Program Execution Environment Changed due to modifica- tion of a kernel, device driver, or relational database Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  29. Introduction Database Applications Dynamic Invariants Conclusion Relational Database Challenges An

    Interesting Defect Report Database Server Crashes Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  30. Introduction Database Applications Dynamic Invariants Conclusion Relational Database Challenges An

    Interesting Defect Report Database Server Crashes When you run a complex query against Microsoft SQL Server 2000, the SQL Server scheduler may stop respond- ing. Additionally, you receive an error message that resem- bles the following: Date Time server Error: 17883 Sever- ity: 1, State: 0 Date Time server Process 52:0 (94c) ... Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  31. Introduction Database Applications Dynamic Invariants Conclusion Relational Database Challenges An

    Interesting Defect Report Input-Dependent Defect Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  32. Introduction Database Applications Dynamic Invariants Conclusion Relational Database Challenges An

    Interesting Defect Report Input-Dependent Defect This problem occurs when one or more of the following con- ditions are true: The query contains a UNION clause or a UNIONALL clause that affects many columns. The query contains several JOIN statements. The query has a large estimated cost. BUG 473858 (SQL Server 8.0) Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  33. Introduction Database Applications Dynamic Invariants Conclusion Relational Database Challenges Real-World

    Defective Database Application The Risks Digest, Volume 22, Issue 64, 2003 Jeppesen reports airspace boundary problems About 350 airspace boundaries contained in Jeppesen Nav- Data are incorrect, the FAA has warned. The error occurred at Jeppesen after a software upgrade when information was pulled from a database containing 20,000 airspace bound- aries worldwide for the March NavData update, which takes effect March 20. Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  34. Introduction Database Applications Dynamic Invariants Conclusion Relational Database Challenges Real-World

    Defective Database Application The Risks Digest, Volume 22, Issue 64, 2003 Jeppesen reports airspace boundary problems About 350 airspace boundaries contained in Jeppesen Nav- Data are incorrect, the FAA has warned. The error occurred at Jeppesen after a software upgrade when information was pulled from a database containing 20,000 airspace bound- aries worldwide for the March NavData update, which takes effect March 20. Practically all use of databases occurs from within applica- tion programs [Silberschatz et al., 2006, pg. 311] Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  35. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Structured Query

    Language The structured query language (SQL) is an established stan- dard and a query and manipulation language for relational database management systems (RDBMS) Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  36. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Structured Query

    Language The structured query language (SQL) is an established stan- dard and a query and manipulation language for relational database management systems (RDBMS) A schema is a collection of table definitions: CREATE TABLE person ( id INT, name VARCHAR(100) NOT NULL, age INT(3), PRIMARY KEY (id) ) Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  37. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Structured Query

    Language The structured query language (SQL) is an established stan- dard and a query and manipulation language for relational database management systems (RDBMS) The data manipulation language supports several operations: SELECT name FROM person WHERE age >= 30 AND age <= 40 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  38. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Structured Query

    Language The structured query language (SQL) is an established stan- dard and a query and manipulation language for relational database management systems (RDBMS) The data manipulation language supports several operations: UPDATE person SET name = Jan WHERE id = 2 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  39. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Structured Query

    Language The structured query language (SQL) is an established stan- dard and a query and manipulation language for relational database management systems (RDBMS) The data manipulation language supports several operations: INSERT INTO person (id, name, age) VALUES (1, John, 38) Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  40. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Structured Query

    Language The structured query language (SQL) is an established stan- dard and a query and manipulation language for relational database management systems (RDBMS) The data manipulation language supports several operations: DELETE FROM person WHERE id = 2 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  41. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Relational Database

    Tables id name age 1 Chalker Conrad 12 2 Abby Clulow 14 3 David Rogan 18 4 Stacie Reckling 32 5 Megan Hartnup 29 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  42. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Relational Database

    Tables id name age 1 Chalker Conrad 12 2 Abby Clulow 14 3 David Rogan 18 4 Stacie Reckling 32 5 Megan Hartnup 29 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  43. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Relational Database

    Tables id name age 1 Chalker Conrad 12 2 Abby Clulow 14 3 David Rogan 18 4 Stacie Reckling 32 5 Megan Hartnup 29 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  44. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Relational Database

    Tables id name age 1 Chalker Conrad 12 2 Abby Clulow 14 3 David Rogan 18 4 Stacie Reckling 32 5 Megan Hartnup 29 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  45. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Relational Database

    Tables id name age 1 Chalker Conrad 12 2 Abby Clulow 14 3 David Rogan 18 4 Stacie Reckling 32 5 Megan Hartnup 29 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  46. Introduction Database Applications Dynamic Invariants Conclusion Relational Databases Relational Database

    Tables id name age 1 Chalker Conrad 12 2 Abby Clulow 14 3 David Rogan 18 4 Stacie Reckling 32 5 Megan Hartnup 29 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  47. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Database

    Applications Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  48. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Database

    Applications Program Relational Database Management System Database State Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  49. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Database

    Applications Program Relational Database Management System Relational Database Management System select Database State query Data Manipulation Language (DML) Statements Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  50. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Database

    Applications Program Relational Database Management System Relational Database Management System update Database State modify Data Manipulation Language (DML) Statements Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  51. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Database

    Applications Program Relational Database Management System Data Manipulation Language (DML) Statements Relational Database Management System insert Database State modify Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  52. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Database

    Applications Program Relational Database Management System Data Manipulation Language (DML) Statements Relational Database Management System delete Database State modify Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  53. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Database

    Applications Program Relational Database Management System Relational Database Management System create table Database Structure modify Data Definition Language (DDL) Statements Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  54. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Database

    Applications Program Relational Database Management System Relational Database Management System create table Database Structure modify Data Definition Language (DDL) Statements Can we categorize the dif- ferent ways of implementing database applications? Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  55. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  56. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Interaction Approach Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  57. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Interaction Approach Program Location Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  58. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Interaction Approach Program Location Interface Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  59. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Interaction Approach Program Location Interface Embedded Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  60. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Interaction Approach Program Location Interface Embedded Outside RDBMS Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  61. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Interaction Approach Program Location Interface Embedded Outside RDBMS Inside RDBMS Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  62. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Interaction Approach Program Location Interface Embedded Outside RDBMS Inside RDBMS Interface Outside RDBMS Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  63. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Categorizing

    Database Applications Database Applications Interaction Approach Program Location Interface Embedded Outside RDBMS Inside RDBMS Interface Outside RDBMS Java application that submits SQL strings to HSQLDB using JDBC Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  64. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  65. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Database State Database Structure Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  66. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Database State Database Structure Only the database administrator can add new constraints to the schema! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  67. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Database State Database Structure The programmers encode the constraints in the program’s source code! Constraint Ci Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  68. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Database State Database Structure The programmers encode the constraints in the program’s source code! Constraint Ci Constraint Cj Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  69. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Database State Database Structure The programmers encode the constraints in the program’s source code! Constraint Ci Constraint Cj Constraint Ck Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  70. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Database State Database Structure Constraint Ci Constraint Cj Constraint Ck Constraints Ci, Cj, Ck should be encoded in the schema! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  71. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Database State Database Structure Constraint Ci Constraint Cj Constraint Ck Goal: extract Ci, Cj, Ck from the source code of the program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  72. Introduction Database Applications Dynamic Invariants Conclusion Programs and Databases Evolution

    of Database Applications Program Relational Database Management System Relational Database Management System DML Command Database State Database Structure Goal: extract Ci, Cj, Ck from the source code of the program Database Structure Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  73. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariants

    Definition An invariant is a mathematical property that holds through some set of transformations Motivating Examples • 0 × y = 0 • |x| ≥ 0 • C d = π Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  74. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariants

    Definition An invariant is a mathematical property that holds through some set of transformations Motivating Examples • 0 × y = 0 • |x| ≥ 0 • C d = π Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  75. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariants

    Definition An invariant is a mathematical property that holds through some set of transformations Motivating Examples • 0 × y = 0 • |x| ≥ 0 • C d = π Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  76. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariants

    Definition An invariant is a mathematical property that holds through some set of transformations Motivating Examples • 0 × y = 0 • |x| ≥ 0 • C d = π Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  77. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Program

    Invariants Invariant with respect to: • State • Input/Output Simple Examples • 0 ≤ x ≤ 10 • 1 ≤ nextX() ≤ 11 • nextX() = (x + 1) mod 11 1 class Invariant { 2 static int x = 0; 3 public static int nextX() { 4 if( ++x > 10 ) 5 x = 0; 6 return x + 1; 7 } 8 } Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  78. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Program

    Invariants Invariant with respect to: • State • Input/Output Simple Examples • 0 ≤ x ≤ 10 • 1 ≤ nextX() ≤ 11 • nextX() = (x + 1) mod 11 1 class Invariant { 2 static int x = 0; 3 public static int nextX() { 4 if( ++x > 10 ) 5 x = 0; 6 return x + 1; 7 } 8 } Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  79. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Program

    Invariants Invariant with respect to: • State • Input/Output Simple Examples • 0 ≤ x ≤ 10 • 1 ≤ nextX() ≤ 11 • nextX() = (x + 1) mod 11 1 class Invariant { 2 static int x = 0; 3 public static int nextX() { 4 if( ++x > 10 ) 5 x = 0; 6 return x + 1; 7 } 8 } Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  80. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Program

    Invariants Invariant with respect to: • State • Input/Output Simple Examples • 0 ≤ x ≤ 10 • 1 ≤ nextX() ≤ 11 • nextX() = (x + 1) mod 11 1 class Invariant { 2 static int x = 0; 3 public static int nextX() { 4 if( ++x > 10 ) 5 x = 0; 6 return x + 1; 7 } 8 } Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  81. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Dynamic

    Invariants Definition A dynamic invariant is a property that is observed to hold during a series of executions • Not guaranteed for all possible executions • May reflect property of: • Program • Inputs Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  82. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Dynamic

    Invariants Definition A dynamic invariant is a property that is observed to hold during a series of executions • Not guaranteed for all possible executions • May reflect property of: • Program • Inputs Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  83. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Daikon

    Invariant Detector Daikon [Ernst et al. 2001] is a dynamic invariant detection engine originally designed for traditional C and Java programs Detection Process • Collect data traces for variables at program points • Compare to pool of potential invariants • Output remaining invariants that meet confidence threshold Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  84. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Daikon

    Invariant Detector Daikon [Ernst et al. 2001] is a dynamic invariant detection engine originally designed for traditional C and Java programs Detection Process • Collect data traces for variables at program points • Compare to pool of potential invariants • Output remaining invariants that meet confidence threshold Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  85. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  86. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Program Instrument Program’ Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  87. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Program Instrument Program’ Execute Test Suite Trace File Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  88. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Program Instrument Program’ Execute Test Suite Trace File Daikon Potential Invariants Dynamic Invariants Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  89. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Program Instrument Program’ Execute Test Suite Trace File Daikon Potential Invariants Dynamic Invariants Supporting Techniques Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  90. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Program Instrument Program’ Execute Test Suite Trace File Daikon Potential Invariants Dynamic Invariants Supporting Techniques Instrumentation Instrument Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  91. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Program Instrument Program’ Execute Test Suite Trace File Daikon Potential Invariants Dynamic Invariants Supporting Techniques Instrumentation Instrument Test Suite Execution Execute Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  92. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Program Instrument Program’ Execute Test Suite Trace File Daikon Potential Invariants Dynamic Invariants Supporting Techniques Instrumentation Instrument Test Suite Execution Execute Statistical Analysis Daikon Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  93. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Invariant

    Detection Process Program Instrument Program’ Execute Test Suite Trace File Daikon Potential Invariants Dynamic Invariants Supporting Techniques Instrumentation Instrument Test Suite Execution Execute Statistical Analysis Daikon Refer to [Ernst et al. 2001] for additional details about these techniques Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  94. Introduction Database Applications Dynamic Invariants Conclusion Traditional Invariant Detection Applications

    of Daikon Applications of dynamic invariants in software engineering: • Programmer understanding • Run-time checking • Integration testing • Interface discovery • Test-input generation • Test-suite reduction • Many additional techniques Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  95. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Structural

    Mapping Program Element Database Element Program Point Table Variable Column Occurence Row Detect invariants for: • Individual columns • Between columns in a given row id name age employed . . . 1 ’John Smith’ 38 5 . . . 2 ’Jan Downing’ 22 2 . . . Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  96. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Structural

    Mapping Program Element Database Element Program Point Table Variable Column Occurence Row Detect invariants for: • Individual columns • Between columns in a given row id name age employed . . . 1 ’John Smith’ 38 5 . . . 2 ’Jan Downing’ 22 2 . . . Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  97. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Structural

    Mapping Program Element Database Element Program Point Table Variable Column Occurence Row Detect invariants for: • Individual columns • Between columns in a given row id name age employed . . . 1 ’John Smith’ 38 5 . . . 2 ’Jan Downing’ 22 2 . . . Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  98. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Data

    Mapping Daikon Concepts • Representation type • int • double • String • int[] • Comparability Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  99. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Data

    Mapping Group Name SQL Types Java Type 1 Text CHAR String VARCHAR TEXT 2 Integer INTEGER int NUMERIC BIT 3 Decimal FLOAT double DOUBLE REAL DECIMAL 4 Binary BLOB byte[] BIT 5 Text Set SET String[] 6 Datetime DATETIME String TIMESTAMP 7 Date DATE String 8 Time TIME String 9 Interval INTERVAL int 10 Primary Key INTEGER reference Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  100. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Data

    Mapping Handling NULL Values • NULL is a possible value for any SQL type • Daikon does not accept null for primitive representation types such as int • Introduce synthetic variable for each NULL-able column • Representation type is hashcode (reference) • Value is either null or a constant Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  101. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Data

    Mapping Handling NULL Values • NULL is a possible value for any SQL type • Daikon does not accept null for primitive representation types such as int • Introduce synthetic variable for each NULL-able column • Representation type is hashcode (reference) • Value is either null or a constant Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  102. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  103. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Relational Database Management System Schema Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  104. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Relational Database Management System Schema Scan Database State All Rows, Columns Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  105. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Relational Database Management System Schema Scan Database State All Rows, Columns Instrumented Program All Modified Rows Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  106. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Relational Database Management System Schema Scan Database State All Rows, Columns Instrumented Program All Modified Rows Collect Trace Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  107. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Relational Database Management System Schema Scan Database State All Rows, Columns Instrumented Program All Modified Rows Collect Trace Infer Invariants Data Trace Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  108. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Relational Database Management System Schema Scan Database State All Rows, Columns Instrumented Program All Modified Rows Collect Trace Infer Invariants Data Trace Relevant Invariants Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  109. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Relational Database Management System Schema Scan Database State All Rows, Columns Instrumented Program All Modified Rows Collect Trace Infer Invariants Data Trace Relevant Invariants Present the Invariants to the Database Administrator Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  110. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Database-Aware

    Procedure Read Schema Relational Database Management System Schema Scan Database State All Rows, Columns Instrumented Program All Modified Rows Collect Trace Infer Invariants Data Trace Relevant Invariants Present the Invariants to the Database Administrator Place Ci, Cj, Ck into the database schema Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  111. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Implementation

    Trace Collector • Pythona program: • Input: Database connection information • Output: Daikon declarations and data trace files • Process: 1 Read schema metadata to determine tables, columns, and data mapping 2 Write declarations file and serialize mapping info for reuse 3 SELECT table contents, transform data by mapping, write to GZip’d trace file • Supports various RDBMS with the SQLAlchemy toolkit a. . . plus a small amount of Cython Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  112. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Implementation

    Trace Collector • Pythona program: • Input: Database connection information • Output: Daikon declarations and data trace files • Process: 1 Read schema metadata to determine tables, columns, and data mapping 2 Write declarations file and serialize mapping info for reuse 3 SELECT table contents, transform data by mapping, write to GZip’d trace file • Supports various RDBMS with the SQLAlchemy toolkit a. . . plus a small amount of Cython Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  113. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Implementation

    Trace Collector • Pythona program: • Input: Database connection information • Output: Daikon declarations and data trace files • Process: 1 Read schema metadata to determine tables, columns, and data mapping 2 Write declarations file and serialize mapping info for reuse 3 SELECT table contents, transform data by mapping, write to GZip’d trace file • Supports various RDBMS with the SQLAlchemy toolkit a. . . plus a small amount of Cython Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  114. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Implementation

    Instrumentation Wrapper • Modified P6Spy JDBC driver wrapper • On connection, capture information and initiate initial metadata read and trace • On statement execution, append the trace file if the database could be modified • INSERT statement • UPDATE statement • Unknown (e.g., a stored procedure call) • Ignore others, including DELETE and TRUNCATE Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  115. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Implementation

    Instrumentation Wrapper • Modified P6Spy JDBC driver wrapper • On connection, capture information and initiate initial metadata read and trace • On statement execution, append the trace file if the database could be modified • INSERT statement • UPDATE statement • Unknown (e.g., a stored procedure call) • Ignore others, including DELETE and TRUNCATE Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  116. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Invariant Detection Implementation

    Instrumentation Wrapper • Modified P6Spy JDBC driver wrapper • On connection, capture information and initiate initial metadata read and trace • On statement execution, append the trace file if the database could be modified • INSERT statement • UPDATE statement • Unknown (e.g., a stored procedure call) • Ignore others, including DELETE and TRUNCATE Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  117. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Objects of

    Analysis Fixed Data Sets Database Tables Columns Rows world 3 24 5302 sakila 23 131 50,086 menagerie 2 10 19 employees 6 24 3,919,015 • MySQL sample databases commonly used for training, certification, and testing • Trace the entire dataset during invariant detection Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  118. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Objects of

    Analysis Database Applications Program iTrust JWhoisServer JTrac Tables 30 7 13 Columns 177 57 126 KLOC 25.5 (Java), 8.6 (JSP) 6.7 12 Test Cases 787 67 41 • Java applications that interact with a relational database • Wrap real database driver in a modified P6Spy driver • Execute the entire test suite during invariant detection Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  119. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Meaningful Invariants Invariants that capture a semantic relationship • dept_emp.from_date <= dept_emp.to_date • employees.gender one of { "F", "M" } • employees.birth_date < employees.hire_date • country.Population >= 0 • icdcodes.Chronic one of { "no", "yes" } All of these invariants were automatically generated! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  120. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Meaningful Invariants Invariants that capture a semantic relationship • dept_emp.from_date <= dept_emp.to_date • employees.gender one of { "F", "M" } • employees.birth_date < employees.hire_date • country.Population >= 0 • icdcodes.Chronic one of { "no", "yes" } All of these invariants were automatically generated! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  121. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Meaningful Invariants Invariants that capture a semantic relationship • dept_emp.from_date <= dept_emp.to_date • employees.gender one of { "F", "M" } • employees.birth_date < employees.hire_date • country.Population >= 0 • icdcodes.Chronic one of { "no", "yes" } All of these invariants were automatically generated! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  122. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Meaningful Invariants Invariants that capture a semantic relationship • dept_emp.from_date <= dept_emp.to_date • employees.gender one of { "F", "M" } • employees.birth_date < employees.hire_date • country.Population >= 0 • icdcodes.Chronic one of { "no", "yes" } All of these invariants were automatically generated! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  123. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Meaningful Invariants Invariants that capture a semantic relationship • dept_emp.from_date <= dept_emp.to_date • employees.gender one of { "F", "M" } • employees.birth_date < employees.hire_date • country.Population >= 0 • icdcodes.Chronic one of { "no", "yes" } All of these invariants were automatically generated! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  124. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Meaningful Invariants Invariants that capture a semantic relationship • dept_emp.from_date <= dept_emp.to_date • employees.gender one of { "F", "M" } • employees.birth_date < employees.hire_date • country.Population >= 0 • icdcodes.Chronic one of { "no", "yes" } All of these invariants were automatically generated! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  125. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Meaningful Invariants Invariants that capture a semantic relationship • dept_emp.from_date <= dept_emp.to_date • employees.gender one of { "F", "M" } • employees.birth_date < employees.hire_date • country.Population >= 0 • icdcodes.Chronic one of { "no", "yes" } All of these invariants were automatically generated! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  126. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Spurious Invariants

    Spurious Invariants • Vacuous invariants reflect a meaningless relationship. • patients.phone1 <= patients.BloodType • patients.lastName >= patients.address1 • cptcodes.Description != cptcodes.Attribute • Lack-of-data invariants result from limited data samples. • mntnr.login == "mntnt" • inetnum.changed == "2006-10-14 16:21:09" • person.name one of { "no name company", "persona non grata"} Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  127. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Spurious Invariants

    Spurious Invariants • Vacuous invariants reflect a meaningless relationship. • patients.phone1 <= patients.BloodType • patients.lastName >= patients.address1 • cptcodes.Description != cptcodes.Attribute • Lack-of-data invariants result from limited data samples. • mntnr.login == "mntnt" • inetnum.changed == "2006-10-14 16:21:09" • person.name one of { "no name company", "persona non grata"} Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  128. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Spurious Invariants

    Spurious Invariants • Vacuous invariants reflect a meaningless relationship. • patients.phone1 <= patients.BloodType • patients.lastName >= patients.address1 • cptcodes.Description != cptcodes.Attribute • Lack-of-data invariants result from limited data samples. • mntnr.login == "mntnt" • inetnum.changed == "2006-10-14 16:21:09" • person.name one of { "no name company", "persona non grata"} Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  129. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Number of Invariants employees world menagerie JWhoisServer JTrac sakila iTrust 0 50 100 150 Type of Invariant Vacuous Lack−of−data Meaningful The majority of detected dynamic invariants are not spurious Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  130. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Number of Invariants employees world menagerie JWhoisServer JTrac sakila iTrust 0 50 100 150 Type of Invariant Vacuous Lack−of−data Meaningful employees’ invariants are meaningful due to wealth of data Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  131. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Invariant Quality

    Number of Invariants employees world menagerie JWhoisServer JTrac sakila iTrust 0 50 100 150 Type of Invariant Vacuous Lack−of−data Meaningful JWhoisServer’s few meaningful invariants suggests poor tests Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  132. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Using Dynamic Invariants • Some invariants can be enforced by the schema definition • Schema enforcement: • Provides a stronger assurance of data integrity than application enforcement • Enables easy long-term maintenance of the program and the relational database • Analyze enforceable invariants: • Already enforced by the schema • Suggest modification to enforce the invariant Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  133. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Using Dynamic Invariants • Some invariants can be enforced by the schema definition • Schema enforcement: • Provides a stronger assurance of data integrity than application enforcement • Enables easy long-term maintenance of the program and the relational database • Analyze enforceable invariants: • Already enforced by the schema • Suggest modification to enforce the invariant Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  134. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Schema Enforced Invariant Schema Definition employees.gender one of { "F", "M" } ENUM(’F’,’M’) countrylanguage.IsOfficial one of { "F", "T" } ENUM(’F’,’T’) customer.active one of { 0, 1 } TINYINT(1) inventory.film id >= 1 SMALLINT(5) UNSIGNED spaces.guest allowed one of { 0, 1 } BIT(1) Reverse engineered many constraints already enforced by the schema Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  135. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Schema Enforced Invariant Schema Definition employees.gender one of { "F", "M" } ENUM(’F’,’M’) countrylanguage.IsOfficial one of { "F", "T" } ENUM(’F’,’T’) customer.active one of { 0, 1 } TINYINT(1) inventory.film id >= 1 SMALLINT(5) UNSIGNED spaces.guest allowed one of { 0, 1 } BIT(1) Reverse engineered many constraints already enforced by the schema Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  136. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Schema Enforceable Invariant Schema Modification isnull(message.message) != null TEXT NOT NULL isnull(film text.description) != null TEXT NOT NULL isnull(history.time stamp) != null DATETIME NOT NULL user space roles.user id >= 1 BIGINT(20) UNSIGNED pet.sex one of { "f", "m" } CHAR(1) ENUM(’m’,’f’) country.Population >= 0 INT(11) UNSIGNED isnull(titles.to date) != null DATE NOT NULL Many detected constraints can easily be added to the schema Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  137. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Schema Enforceable Invariant Schema Modification isnull(message.message) != null TEXT NOT NULL isnull(film text.description) != null TEXT NOT NULL isnull(history.time stamp) != null DATETIME NOT NULL user space roles.user id >= 1 BIGINT(20) UNSIGNED pet.sex one of { "f", "m" } CHAR(1) ENUM(’m’,’f’) country.Population >= 0 INT(11) UNSIGNED isnull(titles.to date) != null DATE NOT NULL Many detected constraints can easily be added to the schema Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  138. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Percentage of Meaningful Invariants JWhoisServer menagerie JTrac employees iTrust world sakila 0.0 0.2 0.4 0.6 0.8 1.0 Type of Meaningful Invariant Enforceable with Standards−Compliant Database Enforceable with Current Database Already Enforced 0 0 0 Percentages relative to the total number of non-spurious invariants Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  139. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Percentage of Meaningful Invariants JWhoisServer menagerie JTrac employees iTrust world sakila 0.0 0.2 0.4 0.6 0.8 1.0 Type of Meaningful Invariant Enforceable with Standards−Compliant Database Enforceable with Current Database Already Enforced 0 0 0 All constraints enforceable by a standards-compliant database Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  140. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Percentage of Meaningful Invariants JWhoisServer menagerie JTrac employees iTrust world sakila 0.0 0.2 0.4 0.6 0.8 1.0 Type of Meaningful Invariant Enforceable with Standards−Compliant Database Enforceable with Current Database Already Enforced 0 0 0 Three schemas can be enhanced with many new constraints Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  141. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Percentage of Meaningful Invariants JWhoisServer menagerie JTrac employees iTrust world sakila 0.0 0.2 0.4 0.6 0.8 1.0 Type of Meaningful Invariant Enforceable with Standards−Compliant Database Enforceable with Current Database Already Enforced 0 0 0 menagerie did not already enforce any of the meaningful invariants Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  142. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Schema Modification

    Percentage of Meaningful Invariants JWhoisServer menagerie JTrac employees iTrust world sakila 0.0 0.2 0.4 0.6 0.8 1.0 Type of Meaningful Invariant Enforceable with Standards−Compliant Database Enforceable with Current Database Already Enforced 0 0 0 JWhoisServer’s MySQL doesn’t support constraint enforcement Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  143. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Empirical Conclusions

    and Future Research Conclusions • Meaningful invariants may be mined from both relational databases and database applications • Invariant quality depends on existence of diverse data • Data integrity may be enhanced by using invariants for modification of the database’s schema Future Research • Invariants between multiple tables • Invariants for individual queries • Explore additional client applications Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  144. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Empirical Conclusions

    and Future Research Conclusions • Meaningful invariants may be mined from both relational databases and database applications • Invariant quality depends on existence of diverse data • Data integrity may be enhanced by using invariants for modification of the database’s schema Future Research • Invariants between multiple tables • Invariants for individual queries • Explore additional client applications Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  145. Introduction Database Applications Dynamic Invariants Conclusion Empirical Results Empirical Conclusions

    and Future Research Conclusions • Meaningful invariants may be mined from both relational databases and database applications • Invariant quality depends on existence of diverse data • Data integrity may be enhanced by using invariants for modification of the database’s schema Future Research • Invariants between multiple tables • Invariants for individual queries • Explore additional client applications Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  146. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  147. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Input Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  148. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Input Output Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  149. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  150. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  151. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  152. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Test Clean Up Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  153. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Test Clean Up Test Oracle Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  154. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Test Clean Up Test Oracle Expected Output Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  155. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Test Clean Up Test Oracle Expected Output Test Verdict Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  156. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Test Clean Up Test Oracle Expected Output Test Verdict Expected Output Output Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  157. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Test Clean Up Test Oracle Expected Output Test Verdict Expected Output Output Test Verdict The test case passes and the code is correct! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  158. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Test Clean Up Test Oracle Expected Output Test Verdict Expected Output Output Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  159. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Case? Method Under Test Test Set Up Input Output Test Clean Up Test Oracle Expected Output Test Verdict Expected Output Output Test Verdict The test case fails and a defect is found! Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  160. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  161. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  162. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  163. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 T4 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  164. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 T4 . . . Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  165. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 T4 . . . Tn Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  166. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 T4 . . . Tn Organize the Test Cases into a Test Suite Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  167. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 T4 . . . Tn Organize the Test Cases into a Test Suite Tool Support for Software Testing? Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  168. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 T4 . . . Tn Organize the Test Cases into a Test Suite Tool Support for Software Testing? JUnit Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  169. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 T4 . . . Tn Organize the Test Cases into a Test Suite Tool Support for Software Testing? JUnit Apache Ant Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  170. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing What is

    a Test Suite? T1 T2 T3 T4 . . . Tn Organize the Test Cases into a Test Suite Tool Support for Software Testing? JUnit Apache Ant Eclipse Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  171. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Organize the Test Cases into a Test Suite Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  172. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  173. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique What if Some Test Cases are More Effective? T3 Tn Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  174. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique What if Some Test Cases are More Effective? T3 Tn Prioritization T3 Tn T1 T4 . . . T2 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  175. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique What if Some Test Cases are More Effective? T3 Tn Prioritization T3 Tn T1 T4 . . . T2 T1 T2 T3 T4 . . . Tn Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  176. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique T3 Tn Prioritization T3 Tn T1 T4 . . . T2 T1 T2 T3 T4 . . . Tn What if Some Test Cases are Redundant? T1 T2 T3 T4 . . . Tn Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  177. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique T3 Tn Prioritization T3 Tn T1 T4 . . . T2 T1 T2 T3 T4 . . . Tn What if Some Test Cases are Redundant? T1 T2 T3 T4 . . . Tn Reduction T4 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  178. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique T3 Tn Prioritization T3 Tn T1 T4 . . . T2 T1 T2 T3 T4 . . . Tn What if Some Test Cases are Redundant? T1 T2 T3 T4 . . . Tn Reduction T4 T1 T2 T3 T4 . . . Tn Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  179. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique T3 Tn Prioritization T3 Tn T1 T4 . . . T2 T1 T2 T3 T4 . . . Tn T1 T2 T3 T4 . . . Tn Reduction T4 T1 T2 T3 T4 . . . Tn What if Only Certain Tests are Needed? T2 Tn Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  180. Introduction Database Applications Dynamic Invariants Conclusion Regression Testing Test Suite

    Management T1 T2 T3 T4 . . . Tn Regression Testing Technique T3 Tn Prioritization T3 Tn T1 T4 . . . T2 T1 T2 T3 T4 . . . Tn T1 T2 T3 T4 . . . Tn Reduction T4 T1 T2 T3 T4 . . . Tn What if Only Certain Tests are Needed? T2 Tn Selection T1 T2 T3 T4 . . . Tn Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  181. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  182. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  183. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  184. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Test Suite Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  185. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Test Suite Database Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  186. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Test Suite Database Invariants Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  187. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Test Suite Database Invariants T1, . . . , T10 = Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  188. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Test Suite Database Invariants T1, . . . , T10 = T41, . . . , T50 = Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  189. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Test Suite Database Invariants T1, . . . , T10 = T41, . . . , T50 = T21, . . . , T30 = Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  190. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Test Suite Database Invariants T1, . . . , T10 = T41, . . . , T50 = T21, . . . , T30 = Divide and Conquer Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  191. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Database-Aware Test

    Suite Reduction T1, . . . , T10 T11, . . . , T20 T21, . . . , T30 T31, . . . , T40 T41, . . . , T50 Dynamic Invariant Detection Program Test Suite Database Invariants T1, . . . , T10 = T41, . . . , T50 = T21, . . . , T30 Divide and Conquer = T21, . . . , T30 Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  192. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Conclusion Conclusion

    • Databases are widely used in real-world applications • Database applications have complex state and structure • Programmers often encode constraints in program source • Dynamic invariant detection reverse engineers constraints • Detected invariants are meaningful and enforceable Future Work • Further empirical studies of dynamic invariants • Implement and evaluate several client applications Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  193. Introduction Database Applications Dynamic Invariants Conclusion Database-Aware Method Conclusion Conclusion

    • Databases are widely used in real-world applications • Database applications have complex state and structure • Programmers often encode constraints in program source • Dynamic invariant detection reverse engineers constraints • Detected invariants are meaningful and enforceable Future Work • Further empirical studies of dynamic invariants • Implement and evaluate several client applications Kapfhammer Allegheny College Using Dynamic Invariant Detection to Support the Testing and Analysis of Database Applications
  194. Using Dynamic Invariant Detection to Support the Testing and Analysis

    of Database Applications Gregory M. Kapfhammer Department of Computer Science Allegheny College http://www.cs.allegheny.edu/∼gkapfham/ Thank you for your attention! I welcome your questions and comments.