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Managing Qualitative Knowledge in Software Architecture Assessment: Insights from SAABNet

This study explores the management of qualitative knowledge in software development, specifically through SAABNet. It discusses the necessity for early assessments in the design process and highlights the limitations of relying solely on quantitative information. The research illustrates how qualitative knowledge, such as expert insights and design patterns, can enhance software architecture evaluations through Bayesian Belief Networks (BBNs). Furthermore, the findings emphasize the potential impact of qualitative assessments on identifying defects, improving quality attributes, and promoting better design practices.

megan-johnston
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Managing Qualitative Knowledge in Software Architecture Assessment: Insights from SAABNet

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  1. SAABNet Managing Qualitative Knowledge in Software Architecture Assesment Jilles van Gurp & Jan Bosch Högskolan Karlskrona/Ronneby in Sweden Department of Software Engineering & Computer Science

  2. Contents • Qualitative Knowledge in SD • SAABNet • Validation results SAABNet

  3. no quantitative information early in the design process greater role of metrics in assessment Software Development requirements spec. design implementation test deployment SAABNet

  4. But • Defect fixing becomes more expensive later in the development process • So there is a need to do assessments early on • There is not enough quantitative information available to use existing techniques SAABNet

  5. Qualitative Knowledge • Examples • expert knowledge • general statistical knowledge • design/architecture patterns • Informal • Badly documented SAABNet

  6. How to use Qualitative Knowledge • Assign expert designers to team • Do peer reviews of requirement specs. and designs • Try to document the knowledge • Use AI SAABNet

  7. Bayesian Belief Networks • Model probabilistic distributions using information about dependencies between the variables • Are an excellent way to model uncertain causal relations between variables • SAABNet (Software Architecture Assessment Belief Network) SAABNet

  8. BBNs in a nutshell Quantitative specification Qualitative specification SAABNet

  9. BBNs in other domains • Medical domain • MS Office assistants • Windows problem analyzer • So why not SE? SAABNet

  10. SAABNet

  11. More abstract Three types of variables • Architecture Attributes • programming language, inheritance • Quality Criteria • complexity, coupling • Quality Factors • maintenance, performance SAABNet

  12. Usage • Insert what you know • Let the BBN calculate probabilities for what you don’t know SAABNet

  13. Usage (2) The screenshots were taken from a tool called Hugin professional which is a modeling tool used for creating and testing BBNs. See www.hugin.com. SAABNet

  14. Usage Strategy • Diagnostic Use • Impact Analysis • Quality Attribute Prediction • Quality Attribute Fulfillment SAABNet

  15. Diagnostic Use • Input variables related to your problem • Examine other variables for anomalities to trace the causes of the problem SAABNet

  16. Impact Analysis • Enter the known variables of the future architecture • Examine the other variables for potential problems SAABNet

  17. Quality Attribute Prediction • Enter architecture attributes & criteria • Examine the Quality attributes for problems SAABNet

  18. Quality Attribute Fulfillment • Enter the desired Quality attributes • Observe the quality attributes and criteria SAABNet

  19. Validation • An embedded system • Evaluation of existing architecture • Impact of suggested changes in the architecture • Epoc 32 • Evaluation of Design • Impact of QRs on Architecture SAABNet

  20. Our findings • We can explain SAABNets output (i.e. it doesn’t produce nonsense) • Given the limited input, the output is remarkably realistic SAABNet

  21. Value of SAABNet • It’s a prototype, not a solution • However, it shows that this way of treating qualitative knowledge • is feasible • and useful SAABNet

  22. Future work • Extend SAABNet to include more variables. • Build a more friendly GUI around SAABNet. • Do an experiment to verify whether a SAABNet based tool can help designers. SAABNet

  23. Conclusions • BBNs provide a way to reason with qualitative knowledge in SD. • Our validation shows that even with a small amount of variables the output can be useful. • More validation is needed. SAABNet

  24. Contact information Jilles van Gurp http://www.ipd.hk-r.se/jvg jvg@ipd.hk-r.se Jan Bosch http://www.ipd.hk-r.se/jbo jbo@ipd.hk-r.se Högskolan Karlskrona/Ronneby in Sweden Department of Software Engineering & Computer Science http://www.ipd.hk-r.se/ SAABNet

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