research-article

Towards a more efficient static software change impact analysis method

Authors:

Mohammad-Amin Jashki,

Ebrahim BagheriAuthors Info & Claims

PASTE '08: Proceedings of the 8th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering

Pages 84 - 90

https://doi.org/10.1145/1512475.1512493

Published: 09 November 2008 Publication History

Abstract

Impact analysis methods are commonly employed to reduce the likelihood of encountering faulty or unexpected behavior from a software program as a result of developers' oblivious modifications. In this paper, we propose a static impact analysis technique that creates clusters of closely associated software program files based on their co-modification history in the software repository. The proposed method benefits from dimensionality reduction techniques to reduce the complexity of the collected information and perform the impact analysis process faster. The method has been tested on four different open source project repositories, namely Firefox, Firebird, Thunderbird, and FileZilla. The results of the impact analysis method performance in terms of precision (impact set identification accuracy) and execution time cost have been reported in this paper. The proposed method shows promising behavior when used with several specific clustering techniques such as DBscan and X-Means.

References

[1]

Taweesup Apiwattanapong, Alessandro Orso, and Mary Jean Harrold. Efficient and precise dynamic impact analysis using execute-after sequences. In ICSE '05: Proceedings of the 27th international conference on Software engineering, pages 432--441, New York, NY, USA, 2005. ACM.

Digital Library

[2]

Robert S. Arnold. Software Change Impact Analysis. IEEE Computer Society Press, Los Alamitos, CA, USA, 1996.

Digital Library

[3]

P. S. Bradley, U. Fayyad, and C. Reina. Scaling EM (Expectation-Maximization) Clustering to Large Databases. Microsoft Research Report, MSR-TR-98-35, Aug, 1998.

[4]

Ben Breech, Mike Tegtmeyer, and Lori Pollock. A comparison of online and dynamic impact analysis algorithms. In CSMR '05: Proceedings of the Ninth European Conference on Software Maintenance and Reengineering, pages 143--152, Washington, DC, USA, 2005. IEEE Computer Society.

Digital Library

[5]

Songcan Chen and Yulian Zhu. Subpattern-based principle component analysis. Pattern Recognition, 37(5):1081--1083, 2004.

[6]

M. Ester, H. P. Kriegel, J. Sander, and X. Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pages 226--231, 1996.

[7]

Beat Fluri, Harald C. Gall, and Martin Pinzger. Fine-grained analysis of change couplings. In SCAM '05: Proceedings of the Fifth IEEE International Workshop on Source Code Analysis and Manipulation, pages 66--74, Washington, DC, USA, 2005. IEEE Computer Society.

Digital Library

[8]

Harald Gall, Karin Hajek, and Mehdi Jazayeri. Detection of logical coupling based on product release history. In ICSM '98: Proceedings of the International Conference on Software Maintenance, page 190, Washington, DC, USA, 1998. IEEE Computer Society.

Digital Library

[9]

Harald Gall, Mehdi Jazayeri, and Jacek Krajewski. Cvs release history data for detecting logical couplings. In IWPSE '03: Proceedings of the 6th International Workshop on Principles of Software Evolution, page 13, Washington, DC, USA, 2003. IEEE Computer Society.

Digital Library

[10]

JA Hartigan and MA Wong. A K-means clustering algorithm. JR Stat. Soc., Ser. C, 28:100--108, 1979.

[11]

G. Holmes, A. Donkin, and I. H. Witten. Weka: a machine learning workbench. Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on, pages 357--361, Nov-2 Dec 1994.

[12]

Lulu Huang and Yeong-Tae Song. Dynamic impact analysis using execution profile tracing. In SERA '06: Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications, pages 237--244, Washington, DC, USA, 2006. IEEE Computer Society.

Digital Library

[13]

Huzefa Kagdi, Jonathan I. Maletic, and Bonita Sharif. Mining software repositories for traceability links. In ICPC '07: Proceedings of the 15th IEEE International Conference on Program Comprehension, pages 145--154, Washington, DC, USA, 2007. IEEE Computer Society.

Digital Library

[14]

Huzefa Kagdi, Shehnaaz Yusuf, and Jonathan I. Maletic. Mining sequences of changed-files from version histories. In MSR '06: Proceedings of the 2006 international workshop on Mining software repositories, pages 47--53, New York, NY, USA, 2006. ACM.

Digital Library

[15]

James Law and Gregg Rothermel. Incremental dynamic impact analysis for evolving software systems. In ISSRE '03: Proceedings of the 14th International Symposium on Software Reliability Engineering, page 430, Washington, DC, USA, 2003. IEEE Computer Society.

Digital Library

[16]

Alessandro Orso, Taweesup Apiwattanapong, and Mary Jean Harrold. Leveraging field data for impact analysis and regression testing. In ESEC/FSE-11: Proceedings of the 9th European software engineering conference held jointly with 11th ACM SIGSOFT international symposium on Foundations of software engineering, pages 128--137, New York, NY, USA, 2003. ACM.

Digital Library

[17]

Alessandro Orso, Taweesup Apiwattanapong, James Law, Gregg Rothermel, and Mary Jean Harrold. An empirical comparison of dynamic impact analysis algorithms. In ICSE '04: Proceedings of the 26th International Conference on Software Engineering, pages 491--500, Washington, DC, USA, 2004. IEEE Computer Society.

Digital Library

[18]

D. Pelleg and A. Moore. X-means: Extending K-means with efficient estimation of the number of clusters. Proceedings of the Seventeenth International Conference on Machine Learning, pages 727--734, 1929.

Digital Library

[19]

Xiaoxia Ren, Barbara G. Ryder, Maximilian Stoerzer, and Frank Tip. Chianti: a change impact analysis tool for java programs. In ICSE '05: Proceedings of the 27th international conference on Software engineering, pages 664--665, New York, NY, USA, 2005. ACM.

Digital Library

[20]

Mark Sherriff and Laurie Williams. Empirical software change impact analysis using singular value decomposition. First International Conference on Software Testing, Verification, and Validation, 0:268--277, 2008.

Digital Library

[21]

Jianbo Shi and J. Malik. Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8):888--905, Aug 2000.

Digital Library

[22]

L. J. P. van der Maaten, E. O. Postma, and H. J. van den Herik. Dimensionality reduction: A comparative review. 2007.

[23]

Annie T. T. Ying, Raymond Ng, and Mark C. Chu-Carroll. Predicting source code changes by mining change history. IEEE Trans. Softw. Eng., 30(9):574--586, 2004. Member-Gail C. Murphy.

Digital Library

[24]

Thomas Zimmermann, Member-Andreas Zeller, Peter Weissgerber, and Stephan Diehl. Mining version histories to guide software changes. IEEE Trans. Softw. Eng., 31(6):429--445, 2005.

Digital Library

Cited By

Yan YCooper NMoran KBavota GPoshyvanyk DRich S(2024)Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence GraphsProceedings of the ACM on Software Engineering10.1145/36437701:FSE(972-995)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643770
Bairi RSonwane AKanade AC. VIyer AParthasarathy SRajamani SAshok BShet S(2024)CodePlan: Repository-Level Coding using LLMs and PlanningProceedings of the ACM on Software Engineering10.1145/36437571:FSE(675-698)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643757
Yimer SMolla YAlemneh E(2022)Predicting Software Maintenance Type, Change Impact, and Maintenance Time Using Machine Learning Algorithms2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)10.1109/ICT4DA56482.2022.9971350(37-41)Online publication date: 28-Nov-2022
https://doi.org/10.1109/ICT4DA56482.2022.9971350
Show More Cited By

Towards a more efficient static software change impact analysis method
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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cover image ACM Conferences

PASTE '08: Proceedings of the 8th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering

November 2008

92 pages

ISBN:9781605583822

DOI:10.1145/1512475

Program Chairs:
Shriram Krishnamurthi
Brown University
,
Michal Young
University of Oregon

Copyright © 2008 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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Published: 09 November 2008

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PASTE08: PASTE '08 - ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering

November 9 - 10, 2008

Georgia, Atlanta

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Overall Acceptance Rate 57 of 159 submissions, 36%

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Cited By

Yan YCooper NMoran KBavota GPoshyvanyk DRich S(2024)Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence GraphsProceedings of the ACM on Software Engineering10.1145/36437701:FSE(972-995)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643770
Bairi RSonwane AKanade AC. VIyer AParthasarathy SRajamani SAshok BShet S(2024)CodePlan: Repository-Level Coding using LLMs and PlanningProceedings of the ACM on Software Engineering10.1145/36437571:FSE(675-698)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643757
Yimer SMolla YAlemneh E(2022)Predicting Software Maintenance Type, Change Impact, and Maintenance Time Using Machine Learning Algorithms2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA)10.1109/ICT4DA56482.2022.9971350(37-41)Online publication date: 28-Nov-2022
https://doi.org/10.1109/ICT4DA56482.2022.9971350
Malhotra RCherukuri M(2020)Software Defect Categorization based on Maintenance Effort and Change Impact using Multinomial Naïve Bayes Algorithm2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO48877.2020.9198037(1068-1073)Online publication date: Jun-2020
https://doi.org/10.1109/ICRITO48877.2020.9198037
Sahin CPollock LClause J(2019)Supporting software evolution through feedback on executing/skipping energy tests for proposed source code changesJournal of Software: Evolution and Process10.1002/smr.2155(e2155)Online publication date: 20-Feb-2019
https://doi.org/10.1002/smr.2155
Wang WHe YLi TZhu JLiu J(2018)An Integrated Model for Information Retrieval Based Change Impact AnalysisScientific Programming10.1155/2018/59136342018(5)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1155/2018/5913634
Pugh SBinkley DMoonen L(2018)[Research Paper] The Case for Adaptive Change Recommendation2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM.2018.00022(129-138)Online publication date: Sep-2018
https://doi.org/10.1109/SCAM.2018.00022
Wen RGilbert DRoche MMcIntosh S(2018)BLIMP Tracer: Integrating Build Impact Analysis with Code Review2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2018.00078(685-694)Online publication date: Sep-2018
https://doi.org/10.1109/ICSME.2018.00078
Rubasinghe IMeedeniya DPerera I(2018)Traceability Management with Impact Analysis in DevOps based Software Development2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2018.8554399(1956-1962)Online publication date: Sep-2018
https://doi.org/10.1109/ICACCI.2018.8554399
Moonen LRolfsnes TBinkley DAlesio S(2018)What are the effects of history length and age on mining software change impact?Empirical Software Engineering10.1007/s10664-017-9588-z23:4(2362-2397)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10664-017-9588-z
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