An Extensive Analysis of Efficient Bug Prediction Configurations

Osman, Haidar; Ghafari, Mohammad; Nierstrasz, Oscar Marius; Lungu, Mircea (2017). An Extensive Analysis of Efficient Bug Prediction Configurations. In: 13th International Conference on Predictive Models and Data Analytics in Software Engineering. PROMISE (pp. 107-116). New York, NY, USA: ACM 10.1145/3127005.3127017

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Background: Bug prediction helps developers steer maintenance activities towards the buggy parts of a software. There are many design aspects to a bug predictor, each of which has several options, i.e. software metrics, machine learning model, and response variable. Aims: These design decisions should be judiciously made because an improper choice in any of them might lead to wrong, misleading, or even useless results. We argue that bug prediction configurations are intertwined and thus need to be evaluated in their entirety, in contrast to the common practice in the field where each aspect is investigated in isolation. Method: We use a cost-aware evaluation scheme to evaluate 60 different bug prediction configuration combinations on five open source Java projects. Results: We find out that the best choices for building a cost-effective bug predictor are change metrics mixed with source code metrics as independent variables, Random Forest as the machine learning model, and the number of bugs as the response variable. Combining these configuration options results in the most efficient bug predictor across all subject systems. Conclusions: We demonstrate a strong evidence for the interplay among bug prediction configurations and provide concrete guidelines for researchers and practitioners on how to build and evaluate efficient bug predictors.

Item Type:

Conference or Workshop Item (Paper)


08 Faculty of Science > Institute of Computer Science (INF)
08 Faculty of Science > Institute of Computer Science (INF) > Software Composition Group (SCG)

UniBE Contributor:

Osman, Haidar; Ghafari, Mohammad and Nierstrasz, Oscar Marius


000 Computer science, knowledge & systems
500 Science > 510 Mathematics










Oscar Marius Nierstrasz-Margiotta

Date Deposited:

16 Apr 2018 09:14

Last Modified:

16 Apr 2018 09:14

Publisher DOI:





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