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Multi-objective cross-version defect prediction
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DOI：10.1007/s00500-016-2456-8.png)
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Abstract
En 中文
Defect prediction models help software project teams to spot defect-prone source files of software systems. Software project teams can prioritize and put up rigorous quality assurance (QA) activities on these predicted defect-prone files to minimize post-release defects so that quality software can be delivered. Cross-version defect prediction is building a prediction model from the previous version of a software project to predict defects in the current version. This is more practical than the other two ways of building models, i.e., cross-project prediction model and cross- validation prediction models, as previous version of same software project will have similar parameter distribution among files. In this paper, we formulate cross-version defect prediction problem as a multi-objective optimization problem with two objective functions: (a) maximizing recall by minimizing misclassification cost and (b) maximizing recall by minimizing cost of QA activities on defect prone files. The two multi-objective defect prediction models are compared with four traditional machine learning algorithms, namely logistic regression, na < ve Bayes, decision tree and random forest. We have used 11 projects from the PROMISE repository consisting of a total of 41 different versions of these projects. Our findings show that multi-objective logistic regression is more cost-effective than single-objective algorithms.
Keywords:
Cross-version defect prediction
Multi-objective optimization
Search-based software engineering
Misclassification cost
Cost-effectiveness
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