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Predicting defect content and quality assurance effectiveness by combining expert judgment and defect data - a case study

 
: Kläs, Michael; Nakao, Haruka; Elberzhager, Frank; Münch, Jürgen

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IEEE Computer Society:
19th International Symposium on Software Reliability Engineering, ISSRE 2008 : 11th - 14th Nov 2008, Seattle/Redmond, WA
Los Alamitos, Calif.: IEEE Computer Society Press, 2008
ISBN: 978-0-7695-3405-3
pp.17-26
International Symposium on Software Reliability Engineering (ISSRE) <19, 2008, Seattle/Wash.>
English
Conference Paper
Fraunhofer IESE ()
defect content estimation; defect prediction; effectiveness; quality management; TestBalance; inspection; case study; JAMSS; HyDEEP

Abstract
Planning quality assurance (QA) activities in a systematic way and controlling their execution are challenging tasks for companies that develop software or software-intensive systems. Both require estimation capabilities regarding the effectiveness of the applied QA techniques and the defect content of the checked artifacts. Existing approaches for these purposes need extensive measurement data from historical projects. Due to the fact that many companies do not collect enough data for applying these approaches (especially for the early project lifecycle), they typically base their QA planning and controlling solely on expert opinion. This article presents a hybrid method that combines commonly available measurement data and context-specific expert knowledge. To evaluate the method's applicability and usefulness, we conducted a case study in the context of independent verification and validation activities for critical software in the space domain. A hybrid defect content and effectiveness model was developed for the software requirements analysis phase and evaluated with available legacy data. One major result is that the hybrid model provides improved estimation accuracy when compared to applicable models based solely on data. The mean magnitude of relative error (MMRE) determined by cross-validation is 29.6% compared to 76.5% obtained by the most accurate data-based model.

: http://publica.fraunhofer.de/documents/N-84750.html