Metrics for Structural Assessment of Product Line Architecture

1. Metrics for the Structural Assessment of Product Line Architecture Asim Rahman asim.rahman@gmail.com

2. Agenda • Introduction • Research Area • Reseach Methodology • Component Based Design • Service Utilization Concept • Structural Soundness • Measuring Strcutural Soundness • Measuring Quality Attributes • Case Study: Library System • Conclusions • Future Work

3. Introduction • Software Reuse • Software Product Lines (SPL) as a reuse approach • SA vs SPL Architecture • Variability is Software Systems • Architectural Assessments

4. Research Area & Questions • Questions? • How do we judge the qulaity of the structure of the architecture? • How to we quanitfy the Property? • How can this measure be used to build a better Product Line Architecture? • Motivation • Almost no published reseach in the area • Large amount of published papers in component based software engineering • Many PL approaches (KobrA, Jan Bosch, Clements) follow a component based approach

5. Research Methodology

6. Component Based Design • Component is a unit of composition with contractually specified interfaces and explicit context dependencies...[Szyperski ’97]

7. Service Ultization Concept

8. Structural Soundness • components are divided, planned and then connected together in most effective way possible to provide a solution to a well defined problem. • SS cannot be gerneralized for all architectures – property should only be compared when two or more architecture provide solution to a similar problem

9. Measuring Structural Soundness • Four step model:Research Methodology • Identifying design/architecture properties (L1) • Identifying metrics to measure properties (L2) • Identifying measurable elements (L3) • Validating the metrics (L4)

10. Measuring Quality Attributes (i) • Observability • Ease with which a developer can peak into the component and learn about its fuctions • Behavior of the component is observed by read methods corresponding to the the read properties • Customizability • Ability or the extent to which a component can be configured, without changing its code • Writable properties are accessed by writable methods

11. Measuring Quality Attributes (ii) • Interface Complexity • Interface Signature • Properties,operations and events • Interface Constraints • Pre-conditions • Post-conditions • Range constraints on properties • Sequence of interface invocations • Interface Packaging and Configurations • Configurations a component can operate in

12. Measuring Quality Attributes (iii) • Self Completeness • SC measured by return value • SC measured by parameters

13. Measuring Quality Attributes (iv) • Provided Service Utilization • We use only the PSU value in order to determine the resuability of the components

14. Measuring Quality Attributes (v) • Maturity • Mature component tend to be more reusable • Less testing effort • Low probability of defects • More familiarity of developers with the component usage • Known behavior in multiple situations • Some characteristics also tend to decrease the probability of reuse • Increase in size • Non-conformance from component model

15. Measuring Quality Attributes (vi) • ...Maturity • Measures to quantify maturity • # of faults in requirement and design • # of open faults • # of closed faults • Avg. # of days a fault remains • Avg. # of days to close a fault • Avg. age of a fault

16. Measuring Quality Attributes (vii) • Modularity • Measured by TREE IMPURITY [Fenton ’97] • Considering Optionality

17. Case Study: Library System (i) • Based on Case Study performed by Fraunhofer Institute of Experimental Studies • Product Line of Library Systems (German Libraries) • Limitations of the Case Study • Few details on operation/service (return val & param) • Scarce information on range constraints on properties • Sequence of method invocation was missing • No details of maturity measures • Variabilty was low

18. Case Study: Library System (ii)

19. Case Study: Library System (iii) • Correlation Analysis of Quality Attributes • Check for mullticollinearity • If strong correlation is found between two quality attributes, then one of the two metrics must be selected • Multicollinearity • Conservative: R ≤ 0.5 • Liberal: If relation between variable remain constant, then multicollinearity is not a problem • Jenson: R ≤ 0.9 • Reasons: • Dont want to double count • Dont expect to have large data points • Carefully categorized quality attributes; hence slight indication of correlation is alarming

20. Case Study: Library System (iv) • Analysis Test Selection • Analysis of Component Level Metrics

21. Case Study: Library System (v) • Analysis of the Architecture Level Metrics

22. Case Study: Library System (vi) • Wilcoxon Signed Rank Test • SCp and SCr (W = 36, N = 8) • Level of Significance = 0.005 (Directional Test) • PSU and SCr (W = 0, N = 3) • For sample sizes smaller than N=5 there are no possible values of W that would be significant at or beyond the baseline 0.05 level • CICM and SCr (W = 10, N = 9) • Level of Significance > 0.05 (Directional Test)

23. Conclusion • Structural Assessment of Product Lines can make use of Component Based Metrics • Adaptation requires consideration of PLA specific features, variability and optionality • Aggregated value as well as individual metrics are usefull for the architects • The metrics need to be validated further by studying industrial Product Lines

24. Future Work • Infuence towards overall quality • Degree of Relation • Unavailability of Product Line Architecture

25. Questions?