Heuristics for Discovering Architectural Violations (WCRE 2013)

Software architecture conformance is a key software quality control activity that aims to reveal the progressive gap normally observed between concrete and planned software architectures. In this paper, we present ArchLint, a lightweight approach for architecture conformance based on a combination of static and historical source code analysis. For this purpose, ArchLint relies on four heuristics for detecting both absences and divergences in source code based architectures. We applied ArchLint in an industrial-strength system and as a result we detected 119 architectural violations, with an overall precision of 46.7% and a recall of 96.2%, for divergences. We also evaluated ArchLint with four open-source systems, used in an independent study on reflexion models. In this second study, ArchLint achieved precision results ranging from 57.1% to 89.4%.

Transcript

1. Heuristics for Discovering
   Architectural Violations
   Cristiano Maffort, Marco Tulio Valente,
   Mariza Bigonha, Nicolas Anquetil, Andre Hora
   1
   WCRE, Koblenz, Germany, October 2013
   

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2. Software Architectures
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   Idealized
   

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3. Software Architectures
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   Idealized Concrete
   

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4. Architecture Conformance
    Goal: expose architectural violations
    Implementation decisions not following the planned
   architecture
   4
   

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5. Architecture Conformance
    Goal: expose architectural violations
    Implementation decisions not following the planned
   architecture
    Two techniques:
    Domain-specific languages
    Model-based techniques
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6. Domain-Specific Languages
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   .QL DCL
   

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7. Domain-Specific Languages
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   .QL DCL
   Problem: detailed definitions of constraints
   

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8. Model-based Techniques
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   Reflexion Model
   

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9. Model-based Techniques
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   Reflexion Model
   Problem: successive model refinements
   

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10. Our Goal
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     Lightweight approach for conformance:
     Without constraints
     Without model refinements
     Minimal input information:
     Just a high-level component model (HLM)
    

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11. ArchLint
     Our inspiration:
    11
    Bug Finding Tools
    (Lint, FindBugs, PMD,
    FxCop, etc)
    Warnings
    

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12. ArchLint
     Our inspiration:
     Our approach:
    12
    Bug Finding Tools
    (Lint, FindBugs, PMD,
    FxCop, etc)
    Warnings
    

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13. ArchLint
     Heuristics to detect absences and divergences
     Absences: dependencies prescribed by the HLM
     But that are not present in the concrete model
     Divergences: dependencies not prescribed by the HLM
     But that are present in the concrete model
     Heuristics:
     False positives are expected
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14. Heuristics
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15. Heuristics for Detecting Absences
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    Evidence #1: 4 classes dependend on TargetClass
    

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16. Heuristics for Detecting Absences
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    Evidence #1: 4 classes dependend on TargetClass
    Evidence #2: 3 classes initially created without this
    dependency, but inserted it later
    

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17. Heuristics for Detecting Absences
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    Evidence #1: 4 classes dependend on TargetClass
    Evidence #2: 3 classes initially created without this
    dependency, but inserted it later
    Evidence #1 && Evidence #2  architectural warning
    C2 missing a dependency with TargetClass
    

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18. Heuristics #1 for Divergences
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    Evidence #1: cp2 is a heavy-user of TargetModule
    

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19. Heuristics #1 for Divergences
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    Evidence #1: cp2 is a heavy-user of TargetModule
    Evidence #2: c2 is the only class in cp1 that depends on TargetModule
    

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20. Heuristics #1 for Divergences
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    Evidence #1: cp2 is a heavy-user of TargetModule
    Evidence #2: c2 is the only class in cp1 that depends on TargetModule
    Evidence #3: dependency to TargetModule were removed from Cp1
    

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21. Heuristics #1 for Divergences
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    Evidence #1: cp2 is a heavy-user of TargetModule
    Evidence #2: c2 is the only class in cp1 that depends on TargetModule
    Evidence #3: dependency to TargetModule were removed from Cp1
    Evidence #1 && Evidence #2 && Evidence #3  architectural warning
    C2 should not depend on TargetModule
    

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22. Evaluation #1
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23. Evaluation
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     SGA System:
     EJB-based information system
     4,923 revisions
     March, 2009 to September, 2011
     1,852 classes and interfaces
     127 KLOC
    

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24. Thresholds
     Absences:
     Asca
    = 0.9 -- 90% of the classes have dependency
     Ains
    = 0.35 -- 35% inserted the dependency
     Divergences
     Dsca
    = 0.05
     Ddel
    = 0.65
     Ddir
    = 0.05
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25. Precision and Recall
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     Classification made by the system’s architect
     For absences, architect classified each warning
     For divergences, architect classified existing
    dependencies
    

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26. Precision and Recall
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     Classification made by the system’s architect
     For absences, architect classified each warning
     For divergences, architect classified existing
    dependencies
    

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27. Precision and Recall
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     Classification made by the system’s architect
     For absences, architect classified each warning
     For divergences, architect classified existing
    dependencies
    

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28. Evaluation #2
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29. RM Dataset
     RMs independently proposed by Bittencourt et al.
     Ant
     ArgoUML
     Lucene
     SweetHome3D
     False positives/negatives regarding such models
     Same thresholds from the 1st evaluation
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30.  Precision
    Results for Divergences
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31.  Precision
     Recall
    Results for Divergences
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32. Conclusion
     ArchLint:
     Lighweighted approach for architetcure conformance
     Minimal input
     Static and historical analysis
     Architetcural warnings
     Future work: heuristics based on data mining techniques
    (association rules)
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33. Thanks!
    Heuristics for Discovering Architectural Violations
    Cristiano Maffort, Marco Tulio Valente,
    Mariza Bigonha, Nicolas Anquetil, Andre Hora
    33
    WCRE, Koblenz, Germany, October 2013
    

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