Against the Mainstream in Bug Prediction

Osman, Haidar (July 2016). Against the Mainstream in Bug Prediction. In: Proceedings of the Seminar Series on Advanced Techniques and Tools for Software Evolution SATToSE2016. 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 AI models perform differently on different projects, in the context of bug prediction. We confirm our hypothesis empirically by running different bug pre- dictors on different systems. We show that no single bug prediction configuration works globally on all projects and, thus, previous bug prediction findings cannot generalize.

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
Date Deposited: 08 Mar 2017 10:03
Last Modified: 08 Mar 2017 10:03
Related URLs:
Uncontrolled Keywords: scg-pub snf-asa2 scg16 jb16 skip-doi
BORIS DOI: 10.7892/boris.94648
URI: http://boris.unibe.ch/id/eprint/94648

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