research-article

Accurate and efficient refactoring detection in commit history

Authors:

Nikolaos Tsantalis,

Matin Mansouri,

Laleh M. Eshkevari,

Davood Mazinanian,

Danny DigAuthors Info & Claims

ICSE '18: Proceedings of the 40th International Conference on Software Engineering

Pages 483 - 494

https://doi.org/10.1145/3180155.3180206

Published: 27 May 2018 Publication History

Abstract

Refactoring detection algorithms have been crucial to a variety of applications: (i) empirical studies about the evolution of code, tests, and faults, (ii) tools for library API migration, (iii) improving the comprehension of changes and code reviews, etc. However, recent research has questioned the accuracy of the state-of-the-art refactoring detection tools, which poses threats to the reliability of their application. Moreover, previous refactoring detection tools are very sensitive to user-provided similarity thresholds, which further reduces their practical accuracy. In addition, their requirement to build the project versions/revisions under analysis makes them inapplicable in many real-world scenarios.

To reinvigorate a previously fruitful line of research that has stifled, we designed, implemented, and evaluated RMiner, a technique that overcomes the above limitations. At the heart of RMiner is an AST-based statement matching algorithm that determines refactoring candidates without requiring user-defined thresholds. To empirically evaluate RMiner, we created the most comprehensive oracle to date that uses triangulation to create a dataset with considerably reduced bias, representing 3,188 refactorings from 185 open-source projects. Using this oracle, we found that RMiner has a precision of 98% and recall of 87%, which is a significant improvement over the previous state-of-the-art.

References

[1]

Everton L. G. Alves, Myoungkyu Song, and Miryung Kim. 2014. RefDistiller: A Refactoring Aware Code Review Tool for Inspecting Manual Refactoring Edits. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE '14). ACM, New York, NY, USA, 751--754.

Digital Library

[2]

Tiago L. Alves, Christiaan Ypma, and Joost Visser. 2010. Deriving Metric Thresholds from Benchmark Data. In Proceedings of the 2010 IEEE International Conference on Software Maintenance (ICSM '10). IEEE Computer Society, Washington, DC, USA, 1--10.

Digital Library

[3]

Maurício Aniche, Christoph Treude, Andy Zaidman, Arie van Deursen, and Marco Aurélio Gerosa. 2016. SATT: Tailoring Code Metric Thresholds for Different Software Architectures. In Proceedings of the IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM '16). 41--50.

[4]

Giuliano Antoniol, Massimiliano Di Penta, and Ettore Merlo. 2004. An Automatic Approach to identify Class Evolution Discontinuities. In 7th International Workshop on Principles of Software Evolution. 31--40.

Digital Library

[5]

Ittai Balaban, Frank Tip, and Robert M. Fuhrer. 2005. Refactoring support for class library migration. In Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications. 265--279.

Digital Library

[6]

Gabriele Bavota, Bernardino De Carluccio, Andrea De Lucia, Massimiliano Di Penta, Rocco Oliveto, and Orazio Strollo. 2012. When Does a Refactoring Induce Bugs? An Empirical Study. In Proceedings of the IEEE 12th International Working Conference on Source Code Analysis and Manipulation (SCAM '12). 104--113.

Digital Library

[7]

Gabriele Bavota, Andrea De Lucia, Massimiliano Di Penta, Rocco Oliveto, and Fabio Palomba. 2015. An Experimental Investigation on the Innate Relationship Between Quality and Refactoring. Journal of Systems and Software 107 (Sept 2015), 1--14.

Digital Library

[8]

Benjamin Biegel, Quinten David Soetens, Willi Hornig, Stephan Diehl, and Serge Demeyer. 2011. Comparison of Similarity Metrics for Refactoring Detection. In Proceedings of the 8th Working Conference on Mining Software Repositories (MSR '11). ACM, New York, NY, USA, 53--62.

Digital Library

[9]

Jim Buckley, Tom Mens, Matthias Zenger, Awais Rashid, and Güinter Kniesel. 2005. Towards a Taxonomy of Software Change. Journal of Software Maintenance and Evolution: Research and Practice 17, 5 (Sept. 2005), 309--332.

Digital Library

[10]

Apache Cassandra. 2018. Mirror of Apache Cassandra. (2018). https://github.com/apache/cassandra/commit/446e2537895c15b404a74107069a12f3fc404b15#diff-8d5005607847694afae01a22fa8fdbce

[11]

Oscar Chaparro, Gabriele Bavota, Andrian Marcus, and Massimiliano Di Penta. 2014. On the Impact of Refactoring Operations on Code Quality Metrics. In Proceedings of the 2014 IEEE International Conference on Software Maintenance and Evolution (ICSME '14). IEEE Computer Society, Washington, DC, USA, 456--460.

Digital Library

[12]

Daniel Alencar da Costa, Shane McIntosh, Weiyi Shang, Uirá Kulesza, Roberta Coelho, and Ahmed Hassan. 2017. A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes. IEEE Transactions on Software Engineering 43, 7 (July 2017), 641--657.

Digital Library

[13]

Steven Davies, Marc Roper, and Murray Wood. 2014. Comparing text-based and dependence-based approaches for determining the origins of bugs. Journal of Software: Evolution and Process 26, 1 (2014), 107--139.

[14]

Eclipse Deeplearning4J. 2018. Deep Learning for Java, Scala & Clojure on Hadoop & Spark. (2018). https://github.com/deeplearning4j/deeplearning4j/commit/91cdfa1ffd937a4cb01cdc0052874ef7831955e2#diff-367fe3c8ca7846530b2d0562b3b83324R61

[15]

Serge Demeyer, Stéphane Ducasse, and Oscar Nierstrasz. 2000. Finding Refactorings via Change Metrics. In Proceedings of the 15th ACM SIGPLAN Conference on Object-oriented Programming, Systems, Languages, and Applications (OOPSLA 00). ACM, New York, NY, USA, 166--177.

Digital Library

[16]

Martín Dias, Alberto Bacchelli, Georgios Gousios, Damien Cassou, and Stéphane Ducasse. 2015. Untangling fine-grained code changes. In Proceedings of the IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER '15). 341--350.

[17]

Daniel Dig. 2007. Automated Upgrading of Component-based Applications. Ph.D. Dissertation. University of Illinois at Urbana-Champaign, Champaign, IL, USA.

Digital Library

[18]

Danny Dig, Can Comertoglu, Darko Marinov, and Ralph Johnson. 2006. Automated Detection of Refactorings in Evolving Components. In Proceedings of the 20th European Conference on Object-Oriented Programming(ECOOP '06). Springer-Verlag, Berlin, Heidelberg, 404--428.

Digital Library

[19]

Danny Dig, William G. Griswold, Emerson Murphy-Hill, and Max Schäfer. 2014. The Future of Refactoring (Dagstuhl Seminar 14211). Dagstuhl Reports 4, 5 (2014), 40--67.

[20]

Danny Dig and Ralph Johnson. 2006. How Do APIs Evolve? A Story of Refactoring. Journal of Software Maintenance and Evolution: Research and Practice 18, 2 (March 2006), 83--107.

Digital Library

[21]

Danny Dig, Kashif Manzoor, Ralph E. Johnson, and Tien N. Nguyen. 2008. Effective Software Merging in the Presence of Object-Oriented Refactorings. IEEE Transactions on Software Engineering 34, 3 (May 2008), 321--335.

Digital Library

[22]

Kecia A.M. Ferreira, Mariza A.S. Bigonha, Roberto S. Bigonha, Luiz F.O. Mendes, and Heitor C. Almeida. 2012. Identifying thresholds for object-oriented software metrics. Journal of Systems and Software 85, 2 (2012), 244--257. Special issue with selected papers from the 23rd Brazilian Symposium on Software Engineering.

Digital Library

[23]

Beat Fluri, Michael Würsch, Martin Pinzger, and Harald Gall. 2007. Change Distilling: Tree Differencing for Fine-Grained Source Code Change Extraction. IEEE Transactions on Software Engineering 33, 11 (Nov. 2007), 725--743.

Digital Library

[24]

Francesca Arcelli Fontana, Andrea Caracciolo, and Marco Zanoni. 2012. DPB: A Benchmark for Design Pattern Detection Tools. In Proceedings of the 16th European Conference on Software Maintenance and Reengineering (CSMR '12).235--244.

Digital Library

[25]

Francesca Arcelli Fontana, Vincenzo Ferme, Marco Zanoni, and Aiko Yamashita. 2015. Automatic Metric Thresholds Derivation for Code Smell Detection. In Proceedings of the Sixth International Workshop on Emerging Trends in Software Metrics (WETSoM '15). IEEE Press, Piscataway, NJ, USA, 44--53. http://dl.acm.org/citation.cfm?id=2821491.2821501

Digital Library

[26]

Francesca Arcelli Fontana, Mika V. Mäntylä, Marco Zanoni, and Alessandro Marino. 2016. Comparing and Experimenting Machine Learning Techniques for Code Smell Detection. Empirical Software Engineering 21, 3 (June 2016), 1143--1191.

Digital Library

[27]

Stephen R. Foster, William G. Griswold, and Sorin Lerner. 2012. WitchDoctor: IDE support for real-time auto-completion of refactorings. In Proceedings of the 34th International Conference on Software Engineering (ICSE '12). 222--232.

Digital Library

[28]

Martin Fowler. 1999. Refactoring: Improving the Design of Existing Code. Addison-Wesley, Boston, MA, USA.

Digital Library

[29]

Martin Fowler. 2005. Fluent Interface. (2005). https://martinfowler.com/bliki/FluentInterface.html

[30]

Lajos Jenő Fülöp, Rudolf Ferenc, and Tibor Gyimóthy. 2008. Towards a Benchmark for Evaluating Design Pattern Miner Tools. In Proceedings of the 12th European Conference on Software Maintenance and Reengineering (CSMR 08). 143--152.

Digital Library

[31]

Xi Ge, Quinton L. DuBose, and Emerson Murphy-Hill. 2012. Reconciling Manual and Automatic Refactoring. In Proceedings of the 34th International Conference on Software Engineering (ICSE '12). IEEE Press, Piscataway, NJ, USA, 211--221. http://dl.acm.org/citation.cfm?id=2337223.2337249

Digital Library

[32]

Xi Ge and Emerson Murphy-Hill. 2014. Manual Refactoring Changes with Automated Refactoring Validation. In Proceedings of the 36th International Conference on Software Engineering (ICSE '14). ACM, New York, NY, USA, 1095--1105.

Digital Library

[33]

Xi Ge, Saurabh Sarkar, and Emerson Murphy-Hill. 2014. Towards Refactoring-aware Code Review. In Proceedings of the 7th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE '14). ACM, New York, NY, USA, 99--102.

Digital Library

[34]

Xi Ge, Saurabh Sarkar, Jim Witschey, and Emerson Murphy-Hill. 2017. Refactoring-Aware Code Review. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC '17). 71--79.

[35]

Michael W. Godfrey and Lijie Zou. 2005. Using Origin Analysis to Detect Merging and Splitting of Source Code Entities. IEEE Transactions on Software Engineering 31,2 (2005), 166--181.

Digital Library

[36]

Carsten Görg and Peter Weissgerber. 2005. Detecting and visualizing refactorings from software archives. In Proceedings of the 13th International Workshop on Program Comprehension (IWPC '05). 205--214.

Digital Library

[37]

William G. Griswold. 1992. Program Restructuring As an Aid to Software Maintenance. Ph.D. Dissertation. Seattle, WA, USA.

Digital Library

[38]

Hazelcast. 2018. Open Source In-Memory Data Grid. (2018). https://github.com/hazelcast/hazelcast/commit/76d7f5e3fe4eb41b383c1d884bc1217b9fa7192e#diff-17f53e9abe4ccd40013a293698fa234dL143

[39]

Péter Hegedűs, István Kádár, Rudolf Ferenc, and Tibor Gyimóthy. 2017. Empirical Evaluation of Software Maintainability Based on a Manually Validated Refactoring Dataset. Information and Software Technology (Nov. 2017). Accepted,to appear.

[40]

Johannes Henkel and Amer Diwan. 2005. CatchUp!: capturing and replaying refactorings to support API evolution. In 27th International Conference on Software Engineering. 274--283.

Digital Library

[41]

Eclipse Jetty. 2018. Web Container & Clients. (2018). https://github.com/eclipse/jettty.project/commit/1f3be625e62f44d929c01f6574678eea05754474#diff-ff02a462f6cc50644669e515c691229dR580

[42]

István Kádár, Péter Hegedűs, Rudolf Ferenc, and Tibor Gyimóthy. 2016. A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability. In Proceedings of the 12th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '16). ACM, New York, NY, USA, Article 10, 4 pages.

Digital Library

[43]

Toshihiro Kamiya, Shinji Kusumoto, and Katsuro Inoue. 2002. CCFinder: A Multilinguistic Token-based Code Clone Detection System for Large Scale Source Code. IEEE Transactions on Software Engineering 28, 7 (July 2002), 654--670.

Digital Library

[44]

David Kawrykow and Martin P. Robillard. 2011. Non-essential Changes in Version Histories. In Proceedings of the 33rd International Conference on Software Engineering (ICSE '11). ACM, New York, NY, USA, 351--360.

Digital Library

[45]

Miryung Kim, Dongxiang Cai, and Sunghun Kim. 2011. An Empirical Investigation into the Role of API-level Refactorings During Software Evolution. In Proceedings of the 33rd International Conference on Software Engineering (ICSE '11). ACM, New York, NY, USA, 151--160.

Digital Library

[46]

Miryung Kim, Matthew Gee, Alex Loh, and Napol Rachatasumrit. 2010. Ref-Finder: A Refactoring Reconstruction Tool Based on Logic Query Templates. In Proceedings of the 18th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE '10). ACM, New York, NY, USA, 371--372.

Digital Library

[47]

Sunghun Kim, Thomas Zimmermann, Kai Pan, and E. James Jr. Whitehead. 2006. Automatic Identification of Bug-Introducing Changes. In Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering (ASE '06). IEEE Computer Society, Washington, DC, USA, 81--90.

Digital Library

[48]

Günter Kniesel and Alexander Binun. 2009. Standing on the shoulders of giants - A data fusion approach to design pattern detection. In Proceedings of the IEEE 17th International Conference on Program Comprehension (ICPC '09). 208--217.

[49]

Grzegorz Kondrak. 2005. N-gram Similarity and Distance. In Proceedings of the 12th International Conference on String Processing and Information Retrieval (SPIRE'05). Springer-Verlag, Berlin, Heidelberg, 115--126.

Digital Library

[50]

Vladimir I. Levenshtein. 1966. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10, 8 (1966), 707--710.

[51]

Anas Mahmoud and Nan Niu. 2013. Supporting requirements traceability through refactoring. In Proceedings of the 21st IEEE International Requirements Engineering Conference (RE '13). 32--41.

[52]

Anas Mahmoud and Nan Niu. 2014. Supporting Requirements to Code Traceability Through Refactoring. Requirements Engineering19, 3 (Sept2014), 309--329.

Digital Library

[53]

Matin Mansouri. 2018. Refactoring Benchmark. (2018). https://github.com/MatinMan/RefactoringBenchmark

[54]

Laura Moreno, Gabriele Bavota, Massimiliano Di Penta, Rocco Oliveto, Andrian Marcus, and Gerardo Canfora. 2017. ARENA: An Approach for the Automated Generation of Release Notes. IEEE Transactions on Software Engineering 43, 2 (Feb 2017), 106--127.

Digital Library

[55]

Emerson Murphy-Hill, Chris Parnin, and Andrew P. Black. 2012. How We Refactor, and How We Know It. IEEE Transactions on Software Engineering 38, 1 (Jan 2012), 5--18.

Digital Library

[56]

Stas Negara, Nicholas Chen, Mohsen Vakilian, Ralph E. Johnson, and Danny Dig. 2013. A Comparative Study of Manual and Automated Refactorings. In Proceedings of the 27th European Conference on Object-Oriented Programming (ECOOP '13). Springer-Verlag, Berlin, Heidelberg, 552--576.

Digital Library

[57]

Stas Negara, Mohsen Vakilian, Nicholas Chen, Ralph E. Johnson, and Danny Dig. 2012. Is It Dangerous to Use Version Control Histories to Study Source Code Evolution?. In Proceedings of the 26th European Conference on Object-Oriented Programming (ECOOP'12). Springer-Verlag, Berlin, Heidelberg, 79--103.

Digital Library

[58]

Neo4j. 2018. Graphs for Everyone. (2018). https://github.com/neo4j/neo4j/commit/f6f87f7d5c5d3987db45db7845d221d7abc33146#diff-0694c9de7c6c3b2738144757b771b751L441

[59]

Paloma Oliveira, Marco Tulio Valente, and Fernando Paim Lima. 2014. Extracting relative thresholds for source code metrics. In Proceedings of the IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE '14). 254--263.

[60]

William F. Opdyke. 1992. Refactoring Object-oriented Frameworks. Ph.D. Dissertation. Champaign, IL, USA.

Digital Library

[61]

Fabio Palomba, Andy Zaidman, Rocco Oliveto, and Andrea De Lucia. 2017. An Exploratory Study on the Relationship Between Changes and Refactoring. In Proceedings of the 25th International Conference on Program Comprehension (ICPC'17). IEEE Press, Piscataway, NJ, USA, 176--185.

Digital Library

[62]

Kyle Prete, Napol Rachatasumrit, Nikita Sudan, and Miryung Kim. 2010. Templatebased reconstruction of complex refactorings. In Proceedings of the 26th IEEE International Conference on Software Maintenance (ICSM '10). 1--10.

Digital Library

[63]

Napol Rachatasumrit and Miryung Kim. 2012. An empirical investigation into the impact of refactoring on regression testing. In Proceedings of the 28th IEEE International Conference on Software Maintenance (ICSM '12). 357--366.

Digital Library

[64]

Danilo Silva, Nikolaos Tsantalis, and Marco Tulio Valente. 2016. Why We Refactor? Confessions of GitHub Contributors. In Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE '16). ACM, New York, NY, USA, 858--870.

Digital Library

[65]

Danilo Silva and Marco Tulio Valente. 2017. RefDiff: Detecting Refactorings in Version Histories. In Proceedings of the 14th International Conference on Mining Software Repositories (MSR '17). IEEE Press, Piscataway, NJ, USA, 269--279.

Digital Library

[66]

Jacek Śliwerski, Thomas Zimmermann, and Andreas Zeller. 2005. When Do Changes Induce Fixes?. In Proceedings of the 2005 International Workshop on Mining Software Repositories (MSR '05). ACM, New York, NY, USA, 1--5.

Digital Library

[67]

Gustavo Soares, Rohit Gheyi, Emerson Murphy-Hill, and Brittany Johnson. 2013. Comparing Approaches to Analyze Refactoring Activity on Software Repositories. Journal of Systems and Software 86, 4 (Apr 2013), 1006--1022.

Digital Library

[68]

Quinten David Soetens, Javier Pérez, Serge Demeyer, and Andy Zaidman. 2015. Circumventing Refactoring Masking Using Fine-grained Change Recording. In Proceedings of the 14th International Workshop on Principles of Software Evolution (IWPSE 2015). ACM, New York, NY, USA, 9--18.

Digital Library

[69]

Nikolaos Tsantalis. 2018. RefactoringMiner. (2018). https://github.com/tsantalis/RefactoringMiner

[70]

Nikolaos Tsantalis, Matin Mansouri, Laleh Eshkevari, and Davood Mazinanian. 2018. Refactoring Oracle. (2018). http://refactoring.encs.concordia.ca/oracle/

[71]

Michele Tufano, Fabio Palomba, Gabriele Bavota, Massimiliano Di Penta, Rocco Oliveto, Andrea De Lucia, and Denys Poshyvanyk. 2017. There and back again: Can you compile that snapshot? Journal of Software: Evolution and Process 29, 4 (2017).

[72]

Arie van Deursen, Leon Moonen, Alex Bergh, and Gerard Kok. 2001. Refactoring Test Code. In Proceedings of the 2nd International Conference on Extreme Programming and Flexible Processes in Software Engineering (XP 2001). 92--95.

[73]

Peter Weissgerber and Stephan Diehl. 2006. Are Refactorings Less Error-prone Than Other Changes?. In Proceedings of the 2006 International Workshop on Mining Software Repositories (MSR '06). ACM, New York, NY, USA, 112--118.

Digital Library

[74]

Peter Weissgerber and Stephan Diehl. 2006. Identifying Refactorings from Source-Code Changes. In Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering (ASE '06). 231--240.

Digital Library

[75]

Chris K. Wensel. 2018. Cascading. (2018). https://github.com/cwensel/cascading/commit/f9d3171f5020da5c359cdda28ef05172e858c464

[76]

Chadd Williams and Jaime Spacco. 2008. SZZ Revisited:VerifyingwhenChanges Induce Fixes. In Proceedings of the 2008 Workshop on Defects in Large Software Systems (DEFECTS '08). ACM, New York, NY, USA, 32--36.

Digital Library

[77]

Zhenchang Xing and Eleni Stroulia. 2005. UMLDiff: An Algorithm for Objectoriented Design Differencing. In Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering (ASE '05). ACM, New York, NY, USA, 54--65.

Digital Library

[78]

Zhenchang Xing and Eleni Stroulia. 2006. Refactoring Detection Based on UMLD-iff Change-Facts Queries. In Proceedings of the 13th Working Conference on Reverse Engineering (WCRE '06). IEEE Computer Society, Washington, DC, USA, 263--274.

Digital Library

[79]

Zhenchang Xing and Eleni Stroulia. 2007. API-Evolution Support with Diff-CatchUp. IEEE Transactions on Software Engineering 33, 12 (Dec 2007), 818--836.

Digital Library

[80]

Zhenchang Xing and Eleni Stroulia. 2008. The JDEvAn Tool Suite in Support of Object-oriented Evolutionary Development. In Companion of the 30th International Conference on Software Engineering (ICSE Companion 08). ACM, New York, NY, USA, 951--952.

Digital Library

Cited By

Nguyen MCarlisle M(2025)C/C++ Vulnerability Data Set Generation Framework using a Parser and LLM2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC)10.1109/ICAIC63015.2025.10848559(1-11)Online publication date: 5-Feb-2025
https://doi.org/10.1109/ICAIC63015.2025.10848559
Liu BJiang YZhang YNiu NLi GLiu H(2025)Exploring the potential of general purpose LLMs in automated software refactoring: an empirical studyAutomated Software Engineering10.1007/s10515-025-00500-032:1Online publication date: 1-Mar-2025
https://doi.org/10.1007/s10515-025-00500-0
Noei SLi HZou Y(2024)Detecting Refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based ApproachACM Transactions on Software Engineering and Methodology10.1145/370530934:3(1-25)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3705309
Show More Cited By

Index Terms

Accurate and efficient refactoring detection in commit history
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software evolution

Recommendations

Aspect-Oriented Refactoring of Legacy Applications: An Evaluation

The primary claimed benefits of aspect-oriented programming (AOP) are that it improves the understandability and maintainability of software applications by modularizing crosscutting concerns. Before there is widespread adoption of AOP, developers need ...
Tool-Supported Refactoring of Existing Object-Oriented Code into Aspects

Aspect-Oriented Programming (AOP) provides mechanisms for the separation of crosscutting concerns—functionalities scattered through the system and tangled with the base code. Existing systems are a natural testbed for the AOP approach since they often ...
Refactoring of Crosscutting Concerns with Metaphor-Based Heuristics

It has been advocated that Aspect-Oriented Programming (AOP) is an effective technique to improve software maintainability through explicit support for modularising crosscutting concerns. However, in order to take the advantages of AOP, there is a need ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '18: Proceedings of the 40th International Conference on Software Engineering

May 2018

1307 pages

ISBN:9781450356381

DOI:10.1145/3180155

Conference Chair:
Michel Chaudron
Chalmers University of Technology, University of Gothenburg, Sweden
,
General Chair:
Ivica Crnkovic
Chalmers University of Technology, University of Gothenburg, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

Copyright © 2018 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 the author(s) 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].

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSERC

Conference

ICSE '18

Sponsor:

SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 27 - June 3, 2018

Gothenburg, Sweden

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

203
Total Citations
View Citations
1,024
Total Downloads

Downloads (Last 12 months)125
Downloads (Last 6 weeks)18

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nguyen MCarlisle M(2025)C/C++ Vulnerability Data Set Generation Framework using a Parser and LLM2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC)10.1109/ICAIC63015.2025.10848559(1-11)Online publication date: 5-Feb-2025
https://doi.org/10.1109/ICAIC63015.2025.10848559
Liu BJiang YZhang YNiu NLi GLiu H(2025)Exploring the potential of general purpose LLMs in automated software refactoring: an empirical studyAutomated Software Engineering10.1007/s10515-025-00500-032:1Online publication date: 1-Mar-2025
https://doi.org/10.1007/s10515-025-00500-0
Noei SLi HZou Y(2024)Detecting Refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based ApproachACM Transactions on Software Engineering and Methodology10.1145/370530934:3(1-25)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3705309
Alikhanifard PTsantalis N(2024)A Novel Refactoring and Semantic Aware Abstract Syntax Tree Differencing Tool and a Benchmark for Evaluating the Accuracy of Diff ToolsACM Transactions on Software Engineering and Methodology10.1145/369600234:2(1-63)Online publication date: 12-Sep-2024
https://dl.acm.org/doi/10.1145/3696002
Xie LLi CPei YZhang TPan MChristakis MPradel M(2024)BRAFAR: Bidirectional Refactoring, Alignment, Fault Localization, and Repair for Programming AssignmentsProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680326(856-868)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680326
Ksontini EAbid AKhalsi RKessentini MSpinellis DConstantinou EBacchelli A(2024)DRMiner: A Tool For Identifying And Analyzing Refactorings In DockerfileProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644921(584-594)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644921
Zhang ZXing ZRen XLu QXu X(2024)Refactoring to Pythonic Idioms: A Hybrid Knowledge-Driven Approach Leveraging Large Language ModelsProceedings of the ACM on Software Engineering10.1145/36437761:FSE(1107-1128)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643776
Niu FShao JXu CMayr-Dorn CAssuncao WHuang LLi CGe JLuo BEgyed ARoychoudhury APaiva AAbreu RStorey M(2024)RAT: A Refactoring-Aware Tool for Tracking Code HistoryProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3640047(104-108)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3640047
AlOmar EKnobloch BKain TKalish CMkaouer MOuni ARoychoudhury APaiva AAbreu RStorey M(2024)AntiCopyPaster 2.0: Whitebox just-in-time code duplicates extractionProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3640035(84-88)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3640035
OBrien DDyer RNguyen TRajan HRoychoudhury APaiva AAbreu RStorey M(2024)Data-Driven Evidence-Based Syntactic Sugar DesignProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639580(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639580
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten