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A preliminary study on using code smells to improve bug localization

Authors:

Natthawute Sae-Lim,

Shinpei Hayashi,

Motoshi SaekiAuthors Info & Claims

ICPC '18: Proceedings of the 26th Conference on Program Comprehension

Pages 324 - 327

https://doi.org/10.1145/3196321.3196361

Published: 28 May 2018 Publication History

Abstract

Bug localization is a technique that has been proposed to support the process of identifying the locations of bugs specified in a bug report. A traditional approach such as information retrieval (IR)-based bug localization calculates the similarity between the bug description and the source code and suggests locations that are likely to contain the bug. However, while many approaches have been proposed to improve the accuracy, the likelihood of each module having a bug is often overlooked or they are treated equally, whereas this may not be the case. For example, modules having code smells have been found to be more prone to changes and faults. Therefore, in this paper, we explore a first step toward leveraging code smells to improve bug localization. By combining the code smell severity with the textual similarity from IR-based bug localization, we can identify the modules that are not only similar to the bug description but also have a higher likelihood of containing bugs. Our preliminary evaluation on four open source projects shows that our technique can improve the baseline approach by 142.25% and 30.50% on average for method and class levels, respectively.

References

[1]

Bruce Croft, Donald Metzler, and Trevor Strohman. 2009. Search Engines: Information Retrieval in Practice (1 ed.). Addison-Wesley Publishing Company.

Digital Library

[2]

Bogdan Dit, Evan Moritz, and Denys Poshyvanyk. 2012. A tracelab-based solution for creating, conducting, and sharing feature location experiments. In Proceedings of the 20th IEEE International Conference on Program Comprehension (ICPC'12). 203--208.

[3]

Bogdan Dit, Meghan Revelle, Malcom Gethers, and Denys Poshyvanyk. 2013. Feature location in source code: a taxonomy and survey. Journal of Software: Evolution and Process 25, 1 (2013), 53--95.

[4]

Latifa Guerrouj, Zeinab Kermansaravi, Venera Arnaoudova, Benjamin CM Fung, Foutse Khomh, Giuliano Antoniol, and Yann-Gaël Guéhéneuc. 2017. Investigating the relation between lexical smells and change-and fault-proneness: an empirical study. Software Quality Journal 25, 3 (2017), 641--670.

Digital Library

[5]

Foutse Khomh, Massimiliano Di Penta, Yann-Gaël Guéhéneuc, and Giuliano Antoniol. 2012. An exploratory study of the impact of antipatterns on class change-and fault-proneness. Empirical Software Engineering 17, 3 (2012), 243--275.

Digital Library

[6]

Radu Marinescu. 2012. Assessing technical debt by identifying design flaws in software systems. IBM Journal of Research and Development 56, 5 (2012), 9--1.

Digital Library

[7]

Anh Tuan Nguyen, Tung Thanh Nguyen, Jafar Al-Kofahi, Hung Viet Nguyen, and Tien N Nguyen. 2011. A topic-based approach for narrowing the search space of buggy files from a bug report. In Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering (ASE'11). 263--272.

Digital Library

[8]

Shivani Rao and Avinash Kak. 2011. Retrieval from software libraries for bug localization: a comparative study of generic and composite text models. In Proceedings of the 8th Working Conference on Mining Software Repositories (MSR'09). 43--52.

Digital Library

[9]

Ripon K Saha, Matthew Lease, Sarfraz Khurshid, and Dewayne E Perry. 2013. Improving bug localization using structured information retrieval. In Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering (ASE'13). 345--355.

Digital Library

[10]

Zhendong Shi, Jacky Keung, Kwabena Ebo Bennin, and Xingjun Zhang. 2018. Comparing learning to rank techniques in hybrid bug localization. Applied Soft Computing 62 (2018), 636--648.

[11]

Chakkrit Tantithamthavorn, Akinori Ihara, and Ken-ichi Matsumoto. 2013. Using co-change histories to improve bug localization performance. In Proceedings of the 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD'13). 543--548.

Digital Library

[12]

Shaowei Wang and David Lo. 2014. Version history, similar report, and structure: Putting them together for improved bug localization. In Proceedings of the 22nd International Conference on Program Comprehension (ICPC'14). 53--63.

Digital Library

[13]

Chu-Pan Wong, Yingfei Xiong, Hongyu Zhang, Dan Hao, Lu Zhang, and Hong Mei. 2014. Boosting bug-report-oriented fault localization with segmentation and stack-trace analysis. In Proceedings of the 30th IEEE International Conference on Software Maintenance and Evolution (ICSME'14). 181--190.

Digital Library

[14]

Klaus Changsun Youm, June Ahn, and Eunseok Lee. 2017. Improved bug localization based on code change histories and bug reports. Information and Software Technology 82 (2017), 177--192.

[15]

Jian Zhou, Hongyu Zhang, and David Lo. 2012. Where should the bugs be fixed?-more accurate information retrieval-based bug localization based on bug reports. In Proceedings of the 34th International Conference on Software Engineering (ICSE'12). 14--24.

Digital Library

Cited By

Mahmud JDe Silva NKhan SMostafavi SMansur SChaparro OMarcus AMoran KRoychoudhury APaiva AAbreu RStorey M(2024)On Using GUI Interaction Data to Improve Text Retrieval-based Bug LocalizationProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3608139(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3608139
Kim JLee E(2023)An Empirical Study on Code Smell Introduction and Removal in Deep Learning Software ProjectsInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402350014633:05(765-786)Online publication date: 14-Apr-2023
https://doi.org/10.1142/S0218194023500146
SAE-LIM NHAYASHI SSAEKI M(2021)Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their PredictionIEICE Transactions on Information and Systems10.1587/transinf.2020EDP7255E104.D:10(1601-1615)Online publication date: 1-Oct-2021
https://doi.org/10.1587/transinf.2020EDP7255
Show More Cited By

Index Terms

A preliminary study on using code smells to improve bug localization
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Maintaining software

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Published In

cover image ACM Conferences

ICPC '18: Proceedings of the 26th Conference on Program Comprehension

May 2018

423 pages

ISBN:9781450357142

DOI:10.1145/3196321

General Chair:
Foutse Khomh
École Polytechnique de Montréal, Canada
,
Program Chairs:
Chanchal K. Roy
University of Saskatchewan, Canada
,
Janet Siegmund
University of Passau, Germany

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].

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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 May 2018

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Japan Society for the Promotion of Science

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ICSE '18

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SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 28 - 29, 2018

Gothenburg, Sweden

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ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

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Cited By

Mahmud JDe Silva NKhan SMostafavi SMansur SChaparro OMarcus AMoran KRoychoudhury APaiva AAbreu RStorey M(2024)On Using GUI Interaction Data to Improve Text Retrieval-based Bug LocalizationProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3608139(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3608139
Kim JLee E(2023)An Empirical Study on Code Smell Introduction and Removal in Deep Learning Software ProjectsInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402350014633:05(765-786)Online publication date: 14-Apr-2023
https://doi.org/10.1142/S0218194023500146
SAE-LIM NHAYASHI SSAEKI M(2021)Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their PredictionIEICE Transactions on Information and Systems10.1587/transinf.2020EDP7255E104.D:10(1601-1615)Online publication date: 1-Oct-2021
https://doi.org/10.1587/transinf.2020EDP7255
Razzaq ABuckley JPatten JChochlov MSai A(2021)BoostNSift: A Query Boosting and Code Sifting Technique for Method Level Bug Localization2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM52516.2021.00019(81-91)Online publication date: Sep-2021
https://doi.org/10.1109/SCAM52516.2021.00019
Takahashi ASae-Lim NHayashi SSaeki M(2021)An extensive study on smell-aware bug localizationJournal of Systems and Software10.1016/j.jss.2021.110986178(110986)Online publication date: Aug-2021
https://doi.org/10.1016/j.jss.2021.110986
Chaparro OFlorez JMarcus A(2019)Using bug descriptions to reformulate queries during text-retrieval-based bug localizationEmpirical Software Engineering10.1007/s10664-018-9672-z24:5(2947-3007)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10664-018-9672-z

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