A learning algorithm for change impact prediction

Vincenzo Musco, Antonin Carette, Martin Monperrus, Philippe Preux

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

2 Scopus citations

Abstract

Change impact analysis (CIA) consists in predicting the impact of a code change in a software application. In this paper, the artifacts that are considered for CIA are methods of object-oriented software; the change under study is a change in the code of the method, the impact is the test methods that fail because of the change that has been performed. We propose LCIP, a learning algorithm that learns from past impacts to predict future impacts. To evaluate LCIP, we consider Java software applications that are strongly tested. We simulate 6000 changes and their actual impact through code mutations, as done in mutation testing. We find that LCIP can predict the impact with a precision of 74%, a recall of 85%, corresponding to a F-score of 64%. This shows that taking a learning perspective on change impact analysis let us achieve good precision and recall in change impact analysis.

Original languageEnglish (US)
Title of host publicationProceedings - 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2016
PublisherAssociation for Computing Machinery, Inc
Pages8-14
Number of pages7
ISBN (Electronic)9781450341653
DOIs
StatePublished - May 14 2016
Event5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2016 - Austin, United States
Duration: May 17 2016 → …

Publication series

NameProceedings - 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2016

Other

Other5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2016
Country/TerritoryUnited States
CityAustin
Period5/17/16 → …

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software

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