1. IV&V Facility 1 FY2002 Initiative: Software Architecture Metrics Hany Ammar, Mark Shereshevsky, Nicholay Gradetsky, Diaa Eldin Nassar, Walid AbdelMoez, J. Shriver, A. Jalan LANE Department of Computer Science and Electrical Engineering West Virginia University Ali Mili, Bo Yu, Yan Wang, Krupa Doshi College of Computing Science New Jersey Institute of Technology The OSMA Software Assurance Symposium Berkley Springs, WV, September, 2002 The more information I give the more errors I may pass– Couched by Tim WVU UI: Software Architecture Metrics

2. IV&V Facility 2 Outline Core idea The amount of information Transferred What we can do Benefits Project Overview Research Objectives Quantitative Factors Error Propagation Change Propagation Future Work

3. IV&V Facility 3 Core idea The amount of information Transferred If component A talks to components B –It is important how much information can be transferred –In Information theoretic terms this is measured by the entropy of data and control information Standard view –Call graphs – control-flow and data-flow information between components –“This talks to that” What is the amount of information transferred? –example1: Binary information- entropy is 1-bit –example2: 1000 possible commands – entropy is 10-bits Intuition: –Error propagation in example2 > Error propagation in example1

4. IV&V Facility 4 What We Can Do Develop error propagation matrix (details follow) Component B (listens) alarm!!! Component A (talks)

5. IV&V Facility 5 Benefits Use metrics to identify and focus on problem areas in the architecture There is a need to analyze the quality of reference architectures in product-line architectural development (NASA Reference Architecture – the SEEDS project at GFSC, Workshop was held in June 17 - 19, 2002 in San Diego, CA) http://lennier.gsfc.nasa.gov/seeds/ The developments of techniques for measuring Error/Change Propagation help analysts identify trouble spots in the architecture (advances the state of the art) The development of automated tools help the analyst apply these techniques on large architectures (advances the state of the practice)

6. IV&V Facility 6 Project Overview FY01 Defined quantitative factors that are relevant to qualitative attributes of the architecture –Error Propagation –Change Propagation –Requirements Propagation Defined information theoretic (Entropy-based) architecture metrics

7. IV&V Facility 7 Project Overview (cont.) FY02 Establish analytical/and empirical relationships between the Entropy-based metrics and Error/Change propagation factors Automate computation of the metrics, and apply to a NASA case study, –Architecture Metrics Tool to be presented in the tools session, www.csee.wvu.edu/swarch/ www.csee.wvu.edu/swarch/

8. IV&V Facility 8 Project Overview (cont.) FY03 Apply the process and metrics to several NASA case studies - Use sanitized Architecture information to estimate the quantitative factors, (links to SIAT tool project with Mark Suder) - Compare Entropy-based metrics to metrics produced from existing metrics tools such as McCabe, (links to Tim Menzies machine learning tool) Conduct empirical Studies to validate the analytical and empirical relationships

9. IV&V Facility 9 Research Objectives Define information metrics for software architectures based on information theoretic measures Automate the process of computing these metrics. Apply the process and metrics to NASA projects Validate the correctness and usefulness of these metrics.

10. IV&V Facility 10 Quantitative Factors Selected and defined the following factors Error Propagation, Significance for Reliability Change Propagation, Significance for Maintainability Requirements Propagation, Significance for Evolvability

11. IV&V Facility 11 Definition of Quantitative Factors: Error Propagation  The error propagation (EP) is a measure of the probability that an error in the message sent by A will cause an error in B. S X AB x x` x OR Error

12. IV&V Facility 12 Error Propagation AB VABVAB v1v1 v3v3 v2v2 S1S1 S2S2 S3S3 EP(A  B) = Observed Message Redundancy (OMR) TR(V A  B, S B ) measures the average amount of redundancy in messages that B receives from A as seen by component B Intrinsic Message Redundancy (IMR) R(V A  B ) measures the intrinsic amount of redundancy in the messages that B receives from A

13. IV&V Facility 13 Static Error Propagation Matrix Error Propagation Coefficient (static) = 0.0503

14. IV&V Facility 14 Dynamic Error Propagation Matrix Error Propagation Coefficient (dynamic) = 0.0534

15. IV&V Facility 15 Controlled Experiments Empirical Validation of Error Propagation Analysis Extract Model Info..rtmdl Design Experiments Corrupt Model Simulate and Log Normal Log Error Logs Analyze Logs Empirical Error Propagation Components, Connectors Connector, Message MATLAB Summit BASIC Fault Model

16. IV&V Facility 16 Error Propagation Controlled Experiments Results The following correlation results are based on 3562 experiments The correlation goes down as the number of injected faults in the experimental side goes down The over all correlation coefficient between the two matrices = 0.8335.

17. IV&V Facility 17 Change Propagation We define Change Propagation from component A to component B as the probability that a change in A due to corrective/ perfective maintenance requires a change in B to maintain the overall function of the system. We propose an analytical formula that approximate change propagation, and present conducted empirical study to measure change propagation.

18. IV&V Facility 18 Change Propagation Analytical study Where |I X | and |O X | stand for the number of inputs and outputs, respectively of component X. DIR(Deterministic injectivity rate) of X is defined as An upper bound on Change Propagation Factor CP between two components A and B

19. IV&V Facility 19 Change Propagation Empirical Study In our experiment, we randomly select a large amount of changes within each component, then go into the source code level to see, if the change will propagate to other components. By computing for each pair of components (A,B), the number for which change in A cause a change in B, we are able to derive the change propagation matrix.

20. IV&V Facility 20 Change Propagation Matrix (Empirical study)

21. IV&V Facility 21 Future Work Investigate correlation between Error Propagation estimates and data measured in several NASA software (SIAT using X34, ITOS case studies) Investigate correlation between Change Propagation estimates and data measured from several applications, for more examples of changes. Investigate the correlation between static and dynamic metrics Develop a methodology for estimating Requirements Propagation