Bug forecast: A method for automatic bug prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper we present an approach and a toolset for automatic bug prediction during software development and maintenance. The toolset extends the Columbus source code quality framework, which is able to integrate into the regular builds, analyze the source code, calculate different quality attributes like product metrics and bad code smells; and monitor the changes of these attributes. The new bug forecast toolset connects to the bug tracking and version control systems and assigns the reported and fixed bugs to the source code classes from the past. It then applies machine learning methods to learn which values of which quality attributes typically characterized buggy classes. Based on this information it is able to predict bugs in current and future versions of the classes. The toolset was evaluated on an industrial software system developed by a large software company called evosoft. We studied the behavior of the toolset through a 1,5 year development period during which 128 snapshots of the software were analyzed. The toolset reached an average bug prediction precision of 72%, reaching many times 100%. We concentrated on high precision, as the primary purpose of the toolset is to aid software developers and testers in pointing out the classes which contain bugs with a high probability and keep the number of false positives relatively low.

Original languageEnglish
Title of host publicationAdvances in Software Engineering - International Conference, ASEA 2010, Held as Part of the Future Generation Information Technology Conference, FGIT 2010, Proceedings
Pages283-295
Number of pages13
DOIs
Publication statusPublished - Dec 1 2010
Event2010 International Conference on Advanced Software Engineering and Its Applications, ASEA 2010 - Jeju Island, Korea, Republic of
Duration: Dec 13 2010Dec 15 2010

Publication series

NameCommunications in Computer and Information Science
Volume117 CCIS
ISSN (Print)1865-0929

Other

Other2010 International Conference on Advanced Software Engineering and Its Applications, ASEA 2010
CountryKorea, Republic of
CityJeju Island
Period12/13/1012/15/10

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Keywords

  • bad code smells
  • bug prediction
  • machine learning
  • software product metrics

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Cite this

Ferenc, R. (2010). Bug forecast: A method for automatic bug prediction. In Advances in Software Engineering - International Conference, ASEA 2010, Held as Part of the Future Generation Information Technology Conference, FGIT 2010, Proceedings (pp. 283-295). (Communications in Computer and Information Science; Vol. 117 CCIS). https://doi.org/10.1007/978-3-642-17578-7_28