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What do large commits tell us?: a taxonomical study of large commits

Published: 10 May 2008 Publication History

Abstract

Research in the mining of software repositories has frequently ignored commits that include a large number of files (we call these large commits). The main goal of this paper is to understand the rationale behind large commits, and if there is anything we can learn from them. To address this goal we performed a case study that included the manual classification of large commits of nine open source projects. The contributions include a taxonomy of large commits, which are grouped according to their intention. We contrast large commits against small commits and show that large commits are more perfective while small commits are more corrective. These large commits provide us with a window on the development practices of maintenance teams.

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cover image ACM Conferences
MSR '08: Proceedings of the 2008 international working conference on Mining software repositories
May 2008
162 pages
ISBN:9781605580241
DOI:10.1145/1370750
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 May 2008

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Author Tags

  1. large commits
  2. software evolution
  3. source control system

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  • (2024)A Meta-Study of Software-Change IntentionsACM Computing Surveys10.1145/366148456:12(1-41)Online publication date: 25-Apr-2024
  • (2024)RavenBuild: Context, Relevance, and Dependency Aware Build Outcome PredictionProceedings of the ACM on Software Engineering10.1145/36437711:FSE(996-1018)Online publication date: 12-Jul-2024
  • (2024)An empirical study of token-based micro commitsEmpirical Software Engineering10.1007/s10664-024-10527-829:6Online publication date: 4-Sep-2024
  • (2024)When less is more: on the value of “co-training” for semi-supervised software defect predictorsEmpirical Software Engineering10.1007/s10664-023-10418-429:2Online publication date: 24-Feb-2024
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  • (2023)Exploring the Impact of Code Clones on Deep Learning SoftwareACM Transactions on Software Engineering and Methodology10.1145/360718132:6(1-34)Online publication date: 3-Jul-2023
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  • (2023)Do API Migrations revert commits - A Preliminary Investigation2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON59035.2023.10316025(50-56)Online publication date: 12-Oct-2023
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