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Quantitative Relations Between Static and Dynamic Software Metrics

This research explores the correlation between static and dynamic software metrics, specifically focusing on error propagation and coupling metrics. The study involves conducting experiments, measuring static and dynamic metrics, and analyzing the data statistically. The results support Hypothesis II, but not Hypothesis I and Hypothesis III.

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Quantitative Relations Between Static and Dynamic Software Metrics

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  1. FY2003 Initiative: WVU UI: Quantitative Relations Between Static and Dynamic Software Metrics Hany Ammar, Mark Shereshevsky,Walid AbdelMoez, Rajesh Gunnalan, and Ahmad Hassan LANE Department of Computer Science and Electrical EngineeringWest Virginia University The OSMA Software Assurance Symposium July 30, 2003Lakeview, WV

  2. Two main views of software systems:  The static view of the structure and composition, The dynamic view during execution • Static and dynamic properties of software have been widely studied in the literature. NASA OSMA SAS’03

  3. So how good is static analysis? • IV&V testing is mostly focused of static analysis • Software rarely executed at Fairmont. • Why? • NASA software supports NASA hardware • So, some software can’t run without its associated hardware • Traditional view: • Dynamic execution more informative than static analysis • This research: • To what extent are static measure surrogate for dynamic measures? • Compare insights gained from static and dynamic measures NASA OSMA SAS’03

  4. Propose three hypotheses of possible correlations between selected static and dynamic metrics Hypothesis I: Static coupling metrics correlate with error propagation in software architectures. Hypothesis II: Static error propagation correlates with dynamic error propagation Hypothesis III: Change proneness” correlates with dynamic coupling of components Experiments to measure static metrics dynamic metrics for selected case studies Statistical analysis of the data: computing correlations developing linear or non-linear regression models. In summary: Hypothesis I: not quite supported Hypothesis II: supported Hypothesis III: rejected This talk NASA OSMA SAS’03

  5. Project Overview FY03 • Conduct empirical study of the relationship between the static and dynamic metrics. • Measure Static Metrics • Measure related Dynamic Metrics • Conduct statistical Analysis of the Data FY04 • Establish Relationships between Static and Dynamic Metrics NASA OSMA SAS’03

  6. Tools

  7. Tools for Static Metrics • We can automatically collect several static metrics (OO metrics,Complexity metrics and Size/Volume metrics). • We are currently using Understand for Java and C++ by Scientific Toolworks for collecting static metrics. NASA OSMA SAS’03

  8. Tools for Dynamic Metrics • Rational Rose-Realtime simulation facility is used to simulate UML models • At the code level, Rational Purifyplus produces dynamic sequence diagrams at run-time • JProbe profiler by Quest Software serves a similar purpose for Java code but does not produce sequence diagrams. NASA OSMA SAS’03

  9. Hypothesis I Static coupling metrics correlate with error propagation in software architectures.

  10. Study of Static Coupling Metrics and Error Propagation • Correlate the experimental data on • error propagation • static information coupling (connector based). • Correlate: • experimental error propagation • and static NAS (number of associations) coupling measure (connector Based). • Correlate: • experimental error propagation • and CBO (connector based). NASA OSMA SAS’03

  11. NASA OSMA SAS’03

  12. Table Highlighting Non-zero Values Information Flow Vs Ep NASA OSMA SAS’03

  13. Table Highlighting Non-zero Values CBO Vs Ep NASA OSMA SAS’03

  14. Correlation Values • Correlation between Information Flow Coupling and Error Propagation is 0.545657 • Correlation between CBO and Error Propagation is 0.130478 • Correlation between NAS and Error Propagation is 0.0 • The correlations are only for the values where the # fault injections >25. NASA OSMA SAS’03

  15. Discussion of Results • Information flow coupling metric shows a higher correlation with dynamic error propagation than CBO. • On further analysis the R2 correlation between Information Flow and EP was 0.297742. • This low correlation value could be partly due to the small size of our sample (9 data points) • This result is not sufficient to validate the Hypothesis I NASA OSMA SAS’03

  16. Hypothesis II: Static error propagation correlates with dynamic error propagation

  17. Static and Dynamic Error Propagation • We will use the experimental data on error propagation and the static error propagation measure(connector Based). • The static error propagation measure is based on the relation developed in the software architecture metrics USIP using the information flow between components. NASA OSMA SAS’03

  18. Experimental (Dynamic) Error Propagation Matrix of HCS NASA OSMA SAS’03

  19. Static Error Propagation Matrix of HCS NASA OSMA SAS’03

  20. Correlation Results • There is a strong correlation between static error propagation and dynamic error propagation. • Correlation = 0.875098 • R2 = 0.765797 • This supports Hypothesis II and our conjecture that some static metrics correlate with related dynamic metrics. NASA OSMA SAS’03

  21. Hypothesis III: “Change proneness” correlates with dynamic coupling of components

  22. Change Proneness and Change Propagation • Hypothesis III “change proneness correlates with dynamic coupling of components” • from the study of Erik Arisholm June 2002, • Dynamic Coupling Measures for Object-Oriented Software, • Eighth IEEE Symposium on Software Metrics • Correlate • dynamic import coupling, • dynamic export coupling metrics • with change proneness measured • based on the software architecture metrics USIP using interface change propagation probabilities. NASA OSMA SAS’03

  23. Dynamic Coupling Metrics • The dynamic coupling metrics we focus on are as follows • Dynamic Export Coupling • Dynamic Import Coupling • Total Dynamic Coupling sum of both import and export coupling. NASA OSMA SAS’03

  24. Experiment • The experiment was conducted on the SharpTool case study.with 32 components. • Dynamic coupling was obtained by counting the object interactions dynamically at run time. During the run all functionalities of the tool were executed. NASA OSMA SAS’03

  25. Experiment Results NASA OSMA SAS’03

  26. Discussion of Results • Zero correlation between • change proneness • and dynamic coupling. • We also computed CBO and found zero correlation between change proneness and CBO • This negates Hypothesis III. • The study in the paper by Erik Arisholm was done using actual project changes data . • In our experiment we used static change proneness data using our interface change propagation probabilities defined in the architecture metrics USIP NASA OSMA SAS’03

  27. So how good is static analysis? • Hypothesis I: Static coupling metrics correlate with error propagation in software architectures. • Not Sure • Hypothesis II: Static error propagation correlates with dynamic error propagation • Yes • Hypothesis III: Change proneness” correlates with dynamic coupling of components • No • And so, what does all this mean? • IV&V can not run code • And still offer value added to NASA systems NASA OSMA SAS’03

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