On the Non-Generalizability in Bug Prediction

Osman, Haidar (2016). On the Non-Generalizability in Bug Prediction. In: Ninth Seminar on Advanced Techniques and Tools for Software Evolution (SATToSE 2016). Bergen, Norway. 11.-13.07.2016.

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Bug prediction is a technique used to estimate the most bug-prone entities in software systems. Bug prediction approaches vary in many design options, such as dependent variables, independent variables, and machine learning models. Choosing the right combination of design options to build an effective bug predictor is hard. Previous studies do not consider this complexity and draw conclusions based on fewer-than-necessary experiments. We argue that each software project is unique from the perspective of its development process. Consequently, metrics and machine learning models perform differently on different projects, in the context of bug prediction. We confirm our hypothesis empirically by running different bug predictors on different systems. We show there are no universal bug prediction configurations that work on all projects.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

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

Subjects:

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

Language:

English

Submitter:

Oscar Marius Nierstrasz-Margiotta

Date Deposited:

08 May 2017 14:14

Last Modified:

08 May 2017 14:14

Uncontrolled Keywords:

scg-pub snf-asa2 scg16 jb17 skip-doi

BORIS DOI:

10.7892/boris.96865

URI:

https://boris.unibe.ch/id/eprint/96865

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