Predicting Fault-Prone Modules with Case-Based Reasoning

  • Authors:

  • Taghi M. Khoshgoftaar;K. Ganesan;Edward B. Allen;Fletcher D. Ross;Rama Munikoti;Nishith Goel;Amit Nandi

  • Affiliations:

  • -;-;-;-;-;-;-

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

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Abstract

Software quality classification models seek to predict quality factors such as whether a module will be fault-prone, or not. Case-based reasoning (cbr) is a modeling technique that seeks to answer new questions by identifying similar ``cases'' from the past. When applied to software reliability, the working hypothesis of our approach is this: a module currently under development is probably fault-prone if a module with similar product and process attributes in an earlier release was fault-prone. The contribution of this paper is application of case-based reasoning to software quality modeling. To the best of our knowledge, this is the first time that case-based reasoning has been used to identify fault-prone modules.A case study illustrates our approach and provides evidence that case-based reasoning can be the basis for useful software quality classification models that are competitive with discriminant models. The case study revisits data from a previously published nonparametric discriminant analysis study. The Type II misclassification rate of the cbr model was substantially better than that of the discriminant model. Although the Type I misclassification rate was slightly greater and the overall misclassification rate was only slightly less, the cbr model was preferred when costs of misclassification were considered.