This paper applies the learning-to-rank approach to software defect prediction. Ranking software modules in order of defect-proneness is important to ensure that testing resources are allocated efficiently. However, prediction models that are optimized for predicting explicitly the number of defects often fail to correctly predict rankings based on those defect numbers. We show in this paper that the model construction methods, which include the ranking performance measure in the objective function, perform better in predicting defect-proneness rankings of multiple modules. We present the experimental results, in which our method is compared against three other methods from the literature, using five publicly available data sets. © 2012 Springer-Verlag.
CITATION STYLE
Yang, X., Tang, K., & Yao, X. (2012). A learning-to-rank algorithm for constructing defect prediction models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7435 LNCS, pp. 167–175). https://doi.org/10.1007/978-3-642-32639-4_21
Mendeley helps you to discover research relevant for your work.