Boosting automatic commit classification into maintenance activities by utilizing source code changes

Stanislav Levin, Amiram Yehudai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

62 Scopus citations

Abstract

Background: Understanding maintenance activities performed in a source code repository could help practitioners reduce uncertainty and improve cost-effectiveness by planning ahead and pre-allocating resources towards source code maintenance. Theresearch community uses 3 main classification categories for maintenance activities: Corrective: fault fixing; Perfective: system improvements; Adaptive: new feature introduction. Previous work in this area has mostly concentrated on evaluating commit classification (into maintenance activities) models in the scope of a single software project. Aims: In this work we seek to design a commit classification model capable of providing high accuracy and Kappa across different projects. In addition, we wish to compare the accuracy and kappa characteristics of classification models that utilize word frequency analysis, source code changes, and combination thereof. Method: We suggest a novel method for automatically classifying commits into maintenance activities by utilizing source code changes (e.g, statement added, method removed, etc.). The results we report are based on studying 11 popular open source projects from various professional domains from which we had manually classified 1151 commits, over 100 from each of the studied projects. Our models were trained using 85% of the dataset, while the remaining 15% were used as a test set. Results: Our method shows a promising accuracy of 76% and Cohen's kappa of 63% (considered "Good" in this context) for the test dataset, an improvement of over 20 percentage points, and a relative boost of ~40% in the context of cross-project classification. Conclusions: We show that by using source code changes in combination with commit message word frequency analysis we are able to considerably boost classification quality in a project agnostic manner.

Original languageEnglish
Title of host publicationPROMISE 2017 - 13th International Conference on Predictive Models and Data Analytics in Software Engineering
PublisherAssociation for Computing Machinery
Pages97-106
Number of pages10
ISBN (Print)9781450353052
DOIs
StatePublished - 8 Nov 2017
Event13th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2017 - Toronto, Canada
Duration: 8 Nov 2017 → …

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2017
Country/TerritoryCanada
CityToronto
Period8/11/17 → …

Keywords

  • Human factors
  • Mining software repositories
  • Predictive models
  • Software maintenance

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