Predicting Fault-Prone Software Modules in Telephone Switches
IEEE Transactions on Software Engineering
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
An empirical evaluation of fault-proneness models
Proceedings of the 24th International Conference on Software Engineering
Journal of Global Optimization
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Looking for bugs in all the right places
Proceedings of the 2006 international symposium on Software testing and analysis
Predicting fault-prone components in a java legacy system
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Automating algorithms for the identification of fault-prone files
Proceedings of the 2007 international symposium on Software testing and analysis
Comparing negative binomial and recursive partitioning models for fault prediction
Proceedings of the 4th international workshop on Predictor models in software engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Comparing the effectiveness of several modeling methods for fault prediction
Empirical Software Engineering
A stochastic learning-to-rank algorithm and its application to contextual advertising
Proceedings of the 20th international conference on World wide web
Scalability of generalized adaptive differential evolution for large-scale continuous optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
IEEE Transactions on Evolutionary Computation
Evaluating defect prediction approaches: a benchmark and an extensive comparison
Empirical Software Engineering
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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.