research-article

Recommending refactorings based on team co-maintenance patterns

Authors:

Gabriele Bavota,

Sebastiano Panichella,

Nikolaos Tsantalis,

Massimiliano Di Penta,

Gerardo CanforaAuthors Info & Claims

ASE '14: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

Pages 337 - 342

https://doi.org/10.1145/2642937.2642948

Published: 15 September 2014 Publication History

Abstract

Refactoring aims at restructuring existing source code when undisciplined development activities have deteriorated its comprehensibility and maintainability. There exist various approaches for suggesting refactoring opportunities, based on different sources of information, e.g., structural, semantic, and historical. In this paper we claim that an additional source of information for identifying refactoring opportunities, sometimes orthogonal to the ones mentioned above, is team development activity. When the activity of a team working on common modules is not aligned with the current design structure of a system, it would be possible to recommend appropriate refactoring operations---e.g., extract class/method/package---to adjust the design according to the teams' activity patterns. Results of a preliminary study---conducted in the context of extract class refactoring---show the feasibility of the approach, and also suggest that this new refactoring dimension can be complemented with others to build better refactoring recommendation tools.

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Cited By

Javan Jafari ACosta DShihab EAbdalkareem R(2023)Dependency Update Strategies and Package CharacteristicsACM Transactions on Software Engineering and Methodology10.1145/360311032:6(1-29)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3603110
Zhang JLuo JLiang JGong LHuang Z(2023)An Accurate Identifier Renaming Prediction and Suggestion ApproachACM Transactions on Software Engineering and Methodology10.1145/360310932:6(1-51)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3603109
Liu XGu DChen ZWen JZhang ZMa YWang HJin X(2023)Rise of Distributed Deep Learning Training in the Big Model Era: From a Software Engineering PerspectiveACM Transactions on Software Engineering and Methodology10.1145/359720432:6(1-26)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3597204
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Index Terms

Recommending refactorings based on team co-maintenance patterns
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues

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Information & Contributors

Information

Published In

cover image ACM Conferences

ASE '14: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

September 2014

934 pages

ISBN:9781450330138

DOI:10.1145/2642937

General Chair:
Ivica Crnkovic
Mälardalen University, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Paul Grünbacher
Johannes Kepler Universität Linz, 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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SIGAI: ACM Special Interest Group on Artificial Intelligence
SIGSOFT: ACM Special Interest Group on Software Engineering
Mälardalen University: Mälardalen University
IEEE CS

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 September 2014

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Seventh Framework Programme

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ASE '14

Sponsor:

SIGAI
SIGSOFT
Mälardalen University

ASE '14: ACM/IEEE International Conference on Automated Software Engineering

September 15 - 19, 2014

Vasteras, Sweden

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ASE '14 Paper Acceptance Rate 82 of 337 submissions, 24%;

Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Javan Jafari ACosta DShihab EAbdalkareem R(2023)Dependency Update Strategies and Package CharacteristicsACM Transactions on Software Engineering and Methodology10.1145/360311032:6(1-29)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3603110
Zhang JLuo JLiang JGong LHuang Z(2023)An Accurate Identifier Renaming Prediction and Suggestion ApproachACM Transactions on Software Engineering and Methodology10.1145/360310932:6(1-51)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3603109
Liu XGu DChen ZWen JZhang ZMa YWang HJin X(2023)Rise of Distributed Deep Learning Training in the Big Model Era: From a Software Engineering PerspectiveACM Transactions on Software Engineering and Methodology10.1145/359720432:6(1-26)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3597204
Wang SWen MLin BLiu YBissyandé TMao X(2023)Pre-implementation Method Name Prediction for Object-oriented ProgrammingACM Transactions on Software Engineering and Methodology10.1145/359720332:6(1-35)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3597203
Wang CPeng XXing ZZhang YLiu MLuo RMeng X(2023)XCoS: Explainable Code Search Based on Query Scoping and Knowledge GraphACM Transactions on Software Engineering and Methodology10.1145/359380032:6(1-28)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3593800
Chen JLiang YShen QJiang JLi S(2023)Toward Understanding Deep Learning Framework BugsACM Transactions on Software Engineering and Methodology10.1145/358715532:6(1-31)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3587155
Majumder SChakraborty JBai GStolee KMenzies T(2023)Fair Enough: Searching for Sufficient Measures of FairnessACM Transactions on Software Engineering and Methodology10.1145/358500632:6(1-22)Online publication date: 29-Sep-2023
https://dl.acm.org/doi/10.1145/3585006
Abdullah AlOmar E(2023)State of Refactoring Adoption: Better Understanding Developer Perception of Refactoring2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)10.1109/MSR59073.2023.00090(635-639)Online publication date: May-2023
https://doi.org/10.1109/MSR59073.2023.00090
Akour MAlenezi MAlsghaier H(2022)Software Refactoring Prediction Using SVM and Optimization AlgorithmsProcesses10.3390/pr1008161110:8(1611)Online publication date: 15-Aug-2022
https://doi.org/10.3390/pr10081611
Marmolejos LAlOmar EMkaouer MNewman COuni A(2022)On the use of textual feature extraction techniques to support the automated detection of refactoring documentationInnovations in Systems and Software Engineering10.1007/s11334-021-00388-518:2(233-249)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11334-021-00388-5
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