Virtual mutation analysis of relational database schemas

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1. Virtual Mutation Analysis of Relational Database Schemas Phil McMinn
 Gregory M. Kapfhammer
 Chris J. Wright University of Sheffield
 Allegheny College
 University of Sheffield
2. Relational Databases – 
 Why Should We (Still) Care? A vital component of many software systems Despite the wave of interest in “NoSQL” technologies, Relational Databases are still popular (and faster) For developers: schemas provide self-documentation
3. Relational Databases – 
 Why Should We (Still) Care? Relational Databases are still 
 important, popular and relevant A vital component of many software systems Despite the wave of interest in “NoSQL” technologies, Relational Databases are still popular (and faster) For developers: schemas provide self-documentation
4. A Relational Database Schema
5. A Relational Database Schema Table
6. A Relational Database Schema Table Column and
 data type
7. Integrity Constraints Prevent invalid data being entered into the database Encode domain logic
8. Integrity Constraints Prevent invalid data being entered into the database Encode domain logic
9. Testing the Schema Correctly accepted by the schema Correctly rejected by the schema Correctly rejected by the schema
10. Testing the Schema Correctly accepted by the schema Correctly rejected by the schema Correctly rejected by the schema
11. Testing the Schema Correctly accepted by the schema Correctly rejected by the schema Correctly rejected by the schema
12. Testing the Schema Correctly accepted by the schema Correctly rejected by the schema Correctly rejected by the schema
13. Why Do We Need to Do This?
14. Why Do We Need to Do This? To trap common errors when designing a schema
 For example: lack of uniqueness property on usernames, out of range values
15. Why Do We Need to Do This? To trap common errors when designing a schema
 For example: lack of uniqueness property on usernames, out of range values To test development behaviour vs deployment
 DBMSs have subtly different behaviors
16. Why Do We Need to Do This? To trap common errors when designing a schema
 For example: lack of uniqueness property on usernames, out of range values To test development behaviour vs deployment
 DBMSs have subtly different behaviors Nobody throws away a database of data
 To test the success of database migrations
17. Why Do We Need to Do This? To trap common errors when designing a schema
 For example: lack of uniqueness property on usernames, out of range values To test development behaviour vs deployment
 DBMSs have subtly different behaviors Nobody throws away a database of data
 To test the success of database migrations Industry advice
 Destroying database consistency can have huge cost implications
18. Mutation Analysis Once a test suite has been created, its fault finding capability can be estimated with mutation analysis. For relational database schema testing, mutants are created by making small changes to the schema
19. Mutation Analysis Once a test suite has been created, its fault finding capability can be estimated with mutation analysis. For relational database schema testing, mutants are created by making small changes to the schema
20. Mutation Analysis Once a test suite has been created, its fault finding capability can be estimated with mutation analysis. For relational database schema testing, mutants are created by making small changes to the schema
21. Mutation Analysis Once a test suite has been created, its fault finding capability can be estimated with mutation analysis. For relational database schema testing, mutants are created by making small changes to the schema
22. Mutation Analysis is Costly schema mutants
23. Mutation Analysis is Costly schema mutants mutant test suite database Mutant killed / alive +
24. Mutation Analysis is Costly schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst
25. Mutation Analysis is Costly
26. Mutation Analysis is Costly DO FEWER
27. Mutation Analysis is Costly DO FEWER DO SMARTER
28. Mutation Analysis is Costly DO FEWER DO SMARTER DO FASTER
29. Mutation Analysis is Costly schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst
30. Mutation Analysis is Costly schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst
31. Mutation Analysis is Costly schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst High cost of communicating with the DBMS and executing SQL queries on it
32. Reducing the Cost schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst
33. Reducing the Cost schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst Local, no communication overhead
34. Reducing the Cost schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst model of
35. Reducing the Cost schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst model of Virtual Mutation
 Analysis
36. Reducing the Cost schema mutants mutant test suite database Mutant killed / alive + SchemaAnalyst model of Virtual Mutation
 Analysis Lower execution overhead
37. The Model
38. The Model Integrity constraint predicate icp1
39. The Model icp2 icp3 Integrity constraint predicate icp1
40. The Model icp2 icp3 icp4 icp5 Integrity constraint predicate icp1
41. The Model Form an acceptance predicate for the table: icp2 icp3 icp4 icp5 ap = icp1 /\ icp2 /\ icp3 /\ icp4 /\ icp5 Integrity constraint predicate icp1
42. The Model Form an acceptance predicate for the table: icp2 icp3 icp4 icp5 ap = icp1 /\ icp2 /\ icp3 /\ icp4 /\ icp5 Integrity constraint predicate icp1 True when DBMS would accept the data False otherwise
43. Virtual DBMS Models
44. Empirical Study RQ1. What is the relative efficiency of the virtual approach? RQ2. What are the time savings? RQ3. How do mutation scores compare when the standard approach is run for as long as the virtual one?
45. Subject Schemas
46. RQ1: Efficiency Standard Virtual 1000 100000 1000 100000 1000 100000 HyperSQL PostgreSQL SQLite C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products Database Schema Mutation Analysis Time (Log Transformed)
47. RQ1: Efficiency Standard Virtual 1000 100000 1000 100000 1000 100000 HyperSQL PostgreSQL SQLite C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products Database Schema Mutation Analysis Time (Log Transformed) Virtual Mutation Analysis is significantly more efficient for Postgres and HyperSQL, but not SQLite
48. RQ2: Time Savings −100 −50 0 50 100 50 100 150 Number of Mutants Percentage of Mean Time Saved HyperSQL PostgreSQL SQLite
49. RQ2: Time Savings −100 −50 0 50 100 50 100 150 Number of Mutants Percentage of Mean Time Saved HyperSQL PostgreSQL SQLite Virtual Mutation Analysis yields large time savings for Postgres and HyperSQL but not always with SQLite, leading to an average time saving of 51% overall
50. HyperSQL PostgreSQL SQLite 0 50 100 150 C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products Database Schema Total Number of Mutants Selective Virtual RQ3: Comparison
51. HyperSQL PostgreSQL SQLite 0 50 100 150 C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products Database Schema Total Number of Mutants Selective Virtual RQ3: Comparison Virtual Mutation Analysis evaluates more mutants
52. RQ3: Comparison Selective Virtual 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 HyperSQL PostgreSQL SQLite C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products Database Schema Mutation Score
53. RQ3: Comparison Selective Virtual 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 HyperSQL PostgreSQL SQLite C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products C offeeO rders Em ployee Inventory Iso3166 JW hoisServer M ozillaPerm issions N istW eather Person Products Database Schema Mutation Score Virtual Mutation Analysis is the best option when highly accurate scores are needed under a time constraint
54. Conclusions Virtual Mutation Analysis Technique: Removes the need to use a real DBMS for relational database schema mutation testing More cost-effective while still being accurate: • More efficient for 22 of 27 configurations studied • Yields time savings of 13 to 99%