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Multi-Objective Cross-Project Defect Prediction by Gerardo Canfora, Andrea De Lucia, Massimiliano Di Penta, Rocco Oliveto, Annibale Panichella, Sebastiano Panichella

pubblicato 16 apr 2013, 06:39 da Gerardo Canfora

Cross-project defect prediction is very appealing because (i) it allows predicting defects in projects for which the availability of data is limited, and (ii) it allows producing generalizable prediction models. However, existing research suggests that cross-project prediction is particularly challenging and, due to heterogeneity of projects, prediction accuracy is not always very good.
This paper proposes a novel, multi-objective approach for cross-project defect prediction, based on a multi-objective lo- gistic regression model built using a genetic algorithm. Instead of providing the software engineer with a single predictive model, the multi-objective approach allows software engineers to choose predictors achieving a compromise between number of likely defect-prone artifacts (effectiveness) and LOC to be analyzed/tested (which can be considered as a proxy of the cost of code inspection). Results of an empirical evaluation on 10 datasets from the Promise repository indicate the superiority and the usefulness of the multi-objective approach with respect to single-objective predictors. Also, the proposed approach outperforms an al- ternative approach for cross-project prediction, based on local prediction upon clusters of similar classes.
Sixth IEEE International Conference on Software Testing, Verification and Validation (ICST 2013)

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