Quantitative Evaluation of Capture-Recapture Models to Control Software Inspections

  • Authors:

  • Lionel C. Briand;Khaled El Emam;Bernd Freimut; Oliver

  • Affiliations:

  • -;-;-;-

  • Venue:
  • ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
  • Year:
  • 1997

Quantified Score

Hi-index 0.00

Visualization

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 accuracy 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 that defects have different probabilities of being detected and a corresponding Jackknife estimator.