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A Critique of Software Defect Prediction Models (1999)
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| Venue: | IEEE TRANSACTIONS ON SOFTWARE ENGINEERING |
| Citations: | 292 - 21 self |
BibTeX
@ARTICLE{Fenton99acritique,
author = {Norman E. Fenton and Martin Neil},
title = {A Critique of Software Defect Prediction Models},
journal = {IEEE TRANSACTIONS ON SOFTWARE ENGINEERING},
year = {1999},
volume = {25},
number = {5},
pages = {675--689}
}
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Abstract
Many organizations want to predict the number of defects (faults) in software systems, before they are deployed, to gauge the likely delivered quality and maintenance effort. To help in this numerous software metrics and statistical models have been developed, with a correspondingly large literature. We provide a critical review of this literature and the state-of-the-art. Most of the wide range of prediction models use size and complexity metrics to predict defects. Others are based on testing data, the “quality ” of the development process, or take a multivariate approach. The authors of the models have often made heroic contributions to a subject otherwise bereft of empirical studies. However, there are a number of serious theoretical and practical problems in many studies. The models are weak because of their inability to cope with the, as yet, unknown relationship between defects and failures. There are fundamental statistical and data quality problems that undermine model validity. More significantly many prediction models tend to model only part of the underlying problem and seriously misspecify it. To illustrate these points the “Goldilock’s Conjecture,” that there is an optimum module size, is used to show the considerable problems inherent in current defect prediction approaches. Careful and considered analysis of past and new results shows that the conjecture lacks support and that some models are misleading. We recommend holistic models for software defect prediction, using Bayesian Belief Networks, as alternative approaches to the single-issue models used at present. We also argue for research into a theory of “software decomposition” in order to test hypotheses about defect introduction and help construct a better science of software engineering.
Keyphrases
software defect prediction model subject otherwise bereft software decomposition model validity wide range single-issue model optimum module size unknown relationship development process many prediction model critical review maintenance effort empirical study bayesian belief network large literature data quality problem new result numerous software metric practical problem goldilock conjecture many organization holistic model statistical model software defect prediction prediction model complexity metric software engineering defect introduction current defect prediction approach many study considerable problem heroic contribution alternative approach multivariate approach software system
