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Quantitative Evaluation of Capture-Recapture Models to Control Software Inspections

 

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IEEE Computer Society:
8th International Symposium on Software Reliability Engineering 1997. Proceedings
Los Alamitos: IEEE Computer Society Press, 1997
ISBN: 0-8186-8120-9
pp.234-244 : Ill., Lit.
International Symposium on Software Reliability Engineering (ISSRE) <8, 1997, Albuquerque>
Research Report
English
Conference Paper
Fraunhofer IESE ()
capture-recapture model; defect prediction; model comparison; software inspection; software quality

Abstract
An important requirement to control the inspection of software artifacts is to be able to decide, based on objective information, whether inspection can stop or whether it should continue to achieve a suitable level of artifact quality. Several studies in software engineering have considered the use of capture-recapture models to predict the number of remaining defects in an inspected document as a decision criterion about reinspection. However, no study on software engineering artifacts compares the actual number of remaining defects to the one predicted by a capture-recapture model. Simulations have been performed but no definite conclusions can be drawn regarding the degree of accuracy of such models under realistic inspection conditions, and the factors affecting this accuracy. Furthermore, none of these studies performed an exhaustive comparison of existing models. In this study, we focus on traditional inspections and estimate, based on actual inspections' data, the degree of acc uracy of all relevant, state-of-the-art, capture-recapture models for which statistical estimators exist. We compare the various models' accuracies and look at the impact of the number of inspectors on these accuracies. Results show that models' accuracies are strongly affected by the number of inspectors and, therefore, one must consider this factor before using capture-recapture models. When the number of inspectors is below 4, no model is sufficiently accurate and underestimation may be substantial. In addition, some models perform better than others in a large number of conditions and plausible reasons are discussed. Based on our analyses, we recommend using a model taking into account different probabilities of detecting defects and a Jacknife estimator.

: http://publica.fraunhofer.de/documents/PX-30641.html