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Article

Incremental origin analysis of source code files

Authors:

Daniela Steidl,

Benjamin Hummel,

Elmar JuergensAuthors Info & Claims

MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories

Pages 42 - 51

https://doi.org/10.1145/2597073.2597111

Published: 31 May 2014 Publication History

Abstract

The history of software systems tracked by version control systems is often incomplete because many file movements are not recorded. However, static code analyses that mine the file history, such as change frequency or code churn, produce precise results only if the complete history of a source code file is available. In this paper, we show that up to 38.9% of the files in open source systems have an incomplete history, and we propose an incremental, commit-based approach to reconstruct the history based on clone information and name similarity. With this approach, the history of a file can be reconstructed across repository boundaries and thus provides accurate information for any source code analysis. We evaluate the approach in terms of correctness, completeness, performance, and relevance with a case study among seven open source systems and a developer survey.

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Cited By

Ahmad SChowdhury SHolmes R(2025)Impact of methodological choices on the analysis of code metrics and maintenanceJournal of Systems and Software10.1016/j.jss.2024.112263220(112263)Online publication date: Feb-2025
https://doi.org/10.1016/j.jss.2024.112263
Hasan MTsantalis NAlikhanifard P(2024)Refactoring-aware Block Tracking in Commit HistoryIEEE Transactions on Software Engineering10.1109/TSE.2024.3484586(1-20)Online publication date: 2024
https://doi.org/10.1109/TSE.2024.3484586
Mashhadi EChowdhury SModaberi SHemmati HUddin G(2024)An empirical study on bug severity estimation using source code metrics and static analysisJournal of Systems and Software10.1016/j.jss.2024.112179(112179)Online publication date: Aug-2024
https://doi.org/10.1016/j.jss.2024.112179
Show More Cited By

Index Terms

Incremental origin analysis of source code files
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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Information & Contributors

Information

Published In

cover image ACM Conferences

MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories

May 2014

427 pages

ISBN:9781450328630

DOI:10.1145/2597073

General Chair:
Premkumar Devanbu
University of California at Davis, USA
,
Program Chairs:
Sung Kim
Hong Kong University of Science and Technology, China
,
Martin Pinzger
University of Klagenfurt, Austria

Copyright © 2014 ACM.

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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SIGSOFT: ACM Special Interest Group on Software Engineering

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TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2014

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ICSE '14

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SIGSOFT

ICSE '14: 36th International Conference on Software Engineering

May 31 - June 1, 2014

Hyderabad, India

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ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

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Citations

Cited By

Ahmad SChowdhury SHolmes R(2025)Impact of methodological choices on the analysis of code metrics and maintenanceJournal of Systems and Software10.1016/j.jss.2024.112263220(112263)Online publication date: Feb-2025
https://doi.org/10.1016/j.jss.2024.112263
Hasan MTsantalis NAlikhanifard P(2024)Refactoring-aware Block Tracking in Commit HistoryIEEE Transactions on Software Engineering10.1109/TSE.2024.3484586(1-20)Online publication date: 2024
https://doi.org/10.1109/TSE.2024.3484586
Mashhadi EChowdhury SModaberi SHemmati HUddin G(2024)An empirical study on bug severity estimation using source code metrics and static analysisJournal of Systems and Software10.1016/j.jss.2024.112179(112179)Online publication date: Aug-2024
https://doi.org/10.1016/j.jss.2024.112179
Mashhadi EAhmadvand HHemmati H(2023)Method-Level Bug Severity Prediction using Source Code Metrics and LLMs2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE59848.2023.00055(635-646)Online publication date: 9-Oct-2023
https://doi.org/10.1109/ISSRE59848.2023.00055
Effendi SÇirisci BMukherjee RNguyen HTripp O(2023)A Language-agnostic Framework for Mining Static Analysis Rules from Code Changes2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)10.1109/ICSE-SEIP58684.2023.00035(327-339)Online publication date: May-2023
https://doi.org/10.1109/ICSE-SEIP58684.2023.00035
Jodavi MTsantalis NRoychoudhury ACadar CKim M(2022)Accurate method and variable tracking in commit historyProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3549079(183-195)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3540250.3549079
Grund FChowdhury SBradley NHall BHolmes R(2021)CodeShovelProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00135(1510-1522)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00135
Hata HKula RIshio TTreude C(2021)Same File, Different ChangesProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00076(773-784)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00076
Quach SLamothe MKamei YShang W(2021)An empirical study on the use of SZZ for identifying inducing changes of non-functional bugsEmpirical Software Engineering10.1007/s10664-021-09970-826:4Online publication date: 19-May-2021
https://doi.org/10.1007/s10664-021-09970-8
Li GWu YRoy CSun JPeng XZhan NHu BMa J(2020)SAGA: Efficient and Large-Scale Detection of Near-Miss Clones with GPU Acceleration2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER48275.2020.9054832(272-283)Online publication date: Feb-2020
https://doi.org/10.1109/SANER48275.2020.9054832
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