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PerfRanker: prioritization of performance regression tests for collection-intensive software

Authors:

Shaikh Mostafa,

Tao XieAuthors Info & Claims

ISSTA 2017: Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 23 - 34

https://doi.org/10.1145/3092703.3092725

Published: 10 July 2017 Publication History

Abstract

Regression performance testing is an important but time/resource-consuming phase during software development. Developers need to detect performance regressions as early as possible to reduce their negative impact and fixing cost. However, conducting regression performance testing frequently (e.g., after each commit) is prohibitively expensive. To address this issue, in this paper, we propose PerfRanker, the first approach to prioritizing test cases in performance regression testing for collection-intensive software, a common type of modern software heavily using collections. Our test prioritization is based on performance impact analysis that estimates the performance impact of a given code revision on a given test execution. Evaluation shows that our approach can cover top 3 test cases whose performance is most affected within top 30% to 37% prioritized test cases, in contrast to top 65% to 79% by 3 baseline techniques.

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Auxiliary Archive (issta17-mainid87-s.zip)

The data set and prototype to run the test prioritization process.

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References

[1]

Apache Commons Maths. http://commons.apache.org/proper/commons-math/

[2]

Xalan for Java. https://xml.apache.org/xalan-j/ 2017. PerfRanker Project Web. (2017).

[3]

https://sites.google.com/site/ perfranker2017/

[4]

Marcos K. Aguilera, Jeffrey C. Mogul, Janet L. Wiener, Patrick Reynolds, and Athicha Muthitacharoen. 2003. Performance Debugging for Distributed Systems of Black Boxes. In Proceedings of the 19th ACM Symposium on Operating Systems Principles. 74–89.

Digital Library

[5]

Azzah Al-Maskari, Mark Sanderson, and Paul Clough. 2007. The Relationship Between IR Effectiveness Measures and User Satisfaction. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 773–774.

Digital Library

[6]

Taweesup Apiwattanapong, Alessandro Orso, and Mary J. Harrold. 2005. Efficient and Precise Dynamic Impact Analysis Using Execute-after Sequences. In Proceedings of the 27th International Conference on Software Engineering. 432–441.

Digital Library

[7]

Robert S. Arnold. 1996. Software Change Impact Analysis. IEEE Computer Society Press.

Digital Library

[8]

Cornel Barna, Marin Litoiu, and Hamoun Ghanbari. 2011. Model-based Performance Testing (NIER Track). In Proceedings of the 33rd International Conference on Software Engineering. 872–875.

Digital Library

[9]

Jennifer Black, Emanuel Melachrinoudis, and David Kaeli. 2004. Bi-Criteria Models for All-Uses Test Suite Reduction. In Proceedings of the 26th International Conference on Software Engineering. 106–115.

Digital Library

[10]

Stephen M. Blackburn, Robin Garner, Chris Hoffmann, Asjad M. Khang, Kathryn S. McKinley, Rotem Bentzur, Amer Diwan, Daniel Feinberg, Daniel Frampton, Samuel Z. Guyer, Martin Hirzel, Antony Hosking, Maria Jump, Han Lee, J. Eliot B. Moss, Aashish Phansalkar, Darko Stefanović, Thomas VanDrunen, Daniel von Dincklage, and Ben Wiedermann. 2006. The DaCapo Benchmarks: Java Benchmarking Development and Analysis. In Proceedings of the 21st Annual ACM SIGPLAN Conference on Object-oriented Programming Systems, Languages, and Applications. 169–190.

Digital Library

[11]

Eric Bodden. 2012. Inter-procedural Data-flow Analysis with IFDS/IDE and Soot. In Proceedings of the ACM SIGPLAN International Workshop on State of the Art in Java Program Analysis. 3–8.

Digital Library

[12]

Bihuan Chen, Yang Liu, and Wei Le. 2016. Generating Performance Distributions via Probabilistic Symbolic Execution. In Proceedings of the 38th International Conference on Software Engineering. 49–60.

Digital Library

[13]

Yih-Farn Chen, David S. Rosenblum, and Kiem-Phong Vo. 1994. TESTTUBE: A System for Selective Regression Testing. In Proceedings of 16th International Conference on Software Engineering. 211–220.

Digital Library

[14]

Giovanni Denaro, Andrea Polini, and Wolfgang Emmerich. 2004. Early Performance Testing of Distributed Software Applications. In Proceedings of the 4th International Workshop on Software and Performance. 94–103.

Digital Library

[15]

Sebastian Elbaum, Alexey G. Malishevsky, and Gregg Rothermel. 2000. Prioritizing Test Cases for Regression Testing. In Proceedings of the 2000 ACM SIGSOFT International Symposium on Software Testing and Analysis. 102–112.

Digital Library

[16]

King Chun Foo. 2011. Automated Discovery of Performance Regressions in Enterprise Applications. In Master’s Thesis.

[17]

King Chun Foo, Zhen Ming Jiang, Bram Adams, Ahmed E. Hassan, Ying Zou, and Parminder Flora. 2010. Mining Performance Regression Testing Repositories for Automated Performance Analysis. In 2010 10th International Conference on Quality Software. 32–41.

Digital Library

[18]

Gregory Fox. 1989. Performance Engineering As a Part of the Development Life Cycle for Large-scale Software Systems. In Proceedings of the 11th International Conference on Software Engineering (ICSE ’89). 85–94.

Digital Library

[19]

Mark Grechanik, Chen Fu, and Qing Xie. 2012. Automatically Finding Performance Problems with Feedback-directed Learning Software Testing. In Proceedings of the 34th International Conference on Software Engineering. 156–166.

Digital Library

[20]

Sumit Gulwani, Krishna K. Mehra, and Trishul Chilimbi. 2009. SPEED: Precise and Efficient Static Estimation of Program Computational Complexity. In Proceedings of the 36th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. 127–139.

Digital Library

[21]

Shi Han, Yingnong Dang, Song Ge, Dongmei Zhang, and Tao Xie. 2012. Performance Debugging in the Large via Mining Millions of Stack Traces. In Proceedings of the 34th International Conference on Software Engineering (ICSE ’12). 145–155.

Digital Library

[22]

Dan Hao, Tao Xie, Lu Zhang, Xiaoyin Wang, Jiasu Sun, and Hong Mei. 2009. Test Input Reduction for Result Inspection to Facilitate Fault Localization. Automated Software Engineering 17, 1 (2009), 5.

Digital Library

[23]

M. Jean Harrold, Rajiv Gupta, and Mary Lou Soffa. 1993. A Methodology for Controlling the Size of a Test Suite. ACM Trans. Softw. Eng. Methodol. 2, 3 (July 1993), 270–285.

Digital Library

[24]

Peng Huang, Xiao Ma, Dongcai Shen, and Yuanyuan Zhou. 2014. Performance Regression Testing Target Prioritization via Performance Risk Analysis. In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). 60–71.

Digital Library

[25]

Guoliang Jin, Linhai Song, Xiaoming Shi, Joel Scherpelz, and Shan Lu. 2012. Understanding and Detecting Real-world Performance Bugs. In Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation. 77–88.

Digital Library

[26]

Milan Jovic, Andrea Adamoli, and Matthias Hauswirth. 2011. Catch Me if You Can: Performance Bug Detection in the Wild. In Proceedings of the 2011 ACM International Conference on Object Oriented Programming Systems Languages and Applications. 155–170.

Digital Library

[27]

Tomas Kalibera, Lubomir Bulej, and Petr Tuma. 2005. Automated Detection of Performance Regressions: The Mono Experience. In 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems. 183–190.

Digital Library

[28]

Charles Killian, Karthik Nagaraj, Salman Pervez, Ryan Braud, James W. Anderson, and Ranjit Jhala. 2010. Finding Latent Performance Bugs in Systems Implementations. In Proceedings of the Eighteenth ACM SIGSOFT International Symposium on Foundations of Software Engineering. 17–26.

Digital Library

[29]

Jung-Min Kim and Adam Porter. 2002. A History-based Test Prioritization Technique for Regression Testing in Resource Constrained Environments. In Proceedings of the 24th International Conference on Software Engineering. 119–129.

Digital Library

[30]

Yongin Kwon, Sangmin Lee, Hayoon Yi, Donghyun Kwon, Seungjun Yang, Byung-Gon Chun, Ling Huang, Petros Maniatis, Mayur Naik, and Yunheung Paek. 2013.

[31]

Mantis: Automatic Performance Prediction for Smartphone Applications. In Proceedings of the 2013 USENIX Conference on Annual Technical Conference. 297– 308.

Digital Library

[32]

James Law and Gregg Rothermel. 2003. Whole Program Path-Based Dynamic Impact Analysis. In Proceedings of the 25th International Conference on Software Engineering. 308–318.

Digital Library

[33]

Andrew W. Leung, Eric Lalonde, Jacob Telleen, James Davis, and Carlos Maltzahn. 2007. Using Comprehensive Analysis for Performance Debugging in Distributed Storage Systems. In 24th IEEE Conference on Mass Storage Systems and Technologies (MSST 2007). 281–286.

Digital Library

[34]

Ondřej Lhoták and Laurie Hendren. 2003. Scaling Java Points-to Analysis Using Spark. In Compiler Construction: 12th International Conference, CC 2003. 153–169.

Digital Library

[35]

Zheng Li, Mark Harman, and Robert M. Hierons. 2007. Search Algorithms for Regression Test Case Prioritization. IEEE Trans. Softw. Eng. 33, 4 (April 2007), 225–237.

Digital Library

[36]

Tongping Liu, Charlie Curtsinger, and Emery D. Berger. 2016. DoubleTake: Fast and Precise Error Detection via Evidence-based Dynamic Analysis. In Proceedings of the 38th International Conference on Software Engineering. 911–922.

Digital Library

[37]

Tongping Liu and Xu Liu. 2016. Cheetah: Detecting False Sharing Efficiently and Effectively. In Proceedings of the 2016 International Symposium on Code Generation and Optimization. 1–11.

Digital Library

[38]

Qi Luo. 2016. Automatic Performance Testing Using Input-sensitive Profiling. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering. 1139–1141.

Digital Library

[39]

Qi Luo, Denys Poshyvanyk, and Mark Grechanik. 2016. Mining Performance Regression Inducing Code Changes in Evolving Software. In Proceedings of the 13th International Conference on Mining Software Repositories. 25–36.

Digital Library

[40]

Alexey G. Malishevsky, Joseph R. Ruthruff, Gregg Rothermel, and Sebastian Elbaum. 2006. Cost-Cognizant Test Case Prioritization. Technical Report.

[41]

Marissa Mayer. In Search of a Better, Faster, Stronger Web. http://goo.gl/m4fXx

[42]

Sichen Meng, Xiaoyin Wang, Lu Zhang, and Hong Mei. 2012. A History-Based Matching Approach to Identification of Framework Evolution. In 2012 34th International Conference on Software Engineering (ICSE). 353–363.

[43]

M. MITCHELL. GCC Performance Regression Testing Discussion. http://gcc.gnu. org/ml/gcc/2005-11/msg01306

[44]

Shaikh Mostafa and Xiaoyin Wang. 2014. An Empirical Study on the Usage of Mocking Frameworks in Software Testing. In 14th International Conference on Quality Software. 127–132.

Digital Library

[45]

Adrian Nistor, Po-Chun Chang, Cosmin Ra

[46]

Adrian Nistor, Linhai Song, Darko Marinov, and Shan Lu. 2013. Toddler: Detecting Performance Problems via Similar Memory-Access Patterns. In 2013 35th International Conference on Software Engineering (ICSE). 562–571.

Digital Library

[47]

Alessandro Orso, Taweesup Apiwattanapong, and Mary J. Harrold. 2003. Leveraging Field Data for Impact Analysis and Regression Testing. In Proceedings of the 9th European Software Engineering Conference Held Jointly with 11th ACM SIGSOFT International Symposium on Foundations of Software Engineering. 128– 137.

Digital Library

[48]

Rohan Padhye and Koushik Sen. 2017. TRAVIOLI: A Dynamic Analysis for Detecting Data-Structure Traversals. In Proceedings of the International Conference on Software Engineering. 473–483.

Digital Library

[49]

Heekwon Park, Seungjae Baek, Jongmoo Choi, Donghee Lee, and Sam H. Noh. 2013. Regularities Considered Harmful: Forcing Randomness to Memory Accesses to Reduce Row Buffer Conflicts for Multi-core, Multi-bank Systems. In Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems. 181–192. ISSTA’17, July 2017, Santa Barbara, CA, USA Shaikh Mostafa, Xiaoyin Wang, and Tao Xie

Digital Library

[50]

Michael Pradel, Markus Huggler, and Thomas R. Gross. 2014. Performance Regression Testing of Concurrent Classes. In Proceedings of the International Symposium on Software Testing and Analysis. 13–25.

Digital Library

[51]

C. Pyo, S. Pae, and G. Lee. 2009. DRAM as Source of Randomness. Electronics Letters 45, 1 (2009), 26–27.

[52]

Thomas Reps, Susan Horwitz, and Mooly Sagiv. 1995. Precise Interprocedural Dataflow Analysis via Graph Reachability. In Proceedings of the 22Nd ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. 49–61.

Digital Library

[53]

Gregg Rothermel and Mary J. Harrold. 1997. A Safe, Efficient Regression Test Selection Technique. ACM Trans. Softw. Eng. Methodol. 6, 2 (April 1997), 173–210.

Digital Library

[54]

Gregg Rothermel, Roland H. Untch, Chengyun Chu, and Mary J. Harrold. 1999. Test Case Prioritization: An Empirical Study. In Software Maintenance, 1999. (ICSM ’99) Proceedings. IEEE International Conference on. 179–188.

Digital Library

[55]

Kai Shen, Ming Zhong, and Chuanpeng Li. 2005. I/O System Performance Debugging Using Model-driven Anomaly Characterization. In Proceedings of the 4th Conference on USENIX Conference on File and Storage Technologies - Volume 4. 23–23.

Digital Library

[56]

Mark Sherriff and Laurie Williams. 2008. Empirical Software Change Impact Analysis Using Singular Value Decomposition. In Proceedings of the 2008 International Conference on Software Testing, Verification, and Validation. 268–277.

Digital Library

[57]

Stoyan Stefanov. 2008. Yslow 2.0. In CSDN Software Development 2.0 Conference.

[58]

Richard J. Turver and Malcolm Munro. 1994. An early impact analysis technique for software maintenance. Journal of Software Maintenance: Research and Practice 6, 1 (1994), 35–52.

[59]

Raja Vallée-Rai, Phong Co, Etienne Gagnon, Laurie Hendren, Patrick Lam, and Vijay Sundaresan. 1999. Soot - a Java Bytecode Optimization Framework. In Proceedings of the 1999 Conference of the Centre for Advanced Studies on Collaborative Research (CASCON ’99). 13.

Digital Library

[60]

Xiaoyin Wang, David Lo, Jiefeng Cheng, Lu Zhang, Hong Mei, and Jeffrey Xu Yu. 2010. Matching Dependence-related Queries in the System Dependence Graph. In Proceedings of the IEEE/ACM International Conference on Automated Software Engineering. 457–466.

Digital Library

[61]

Elaine J. Weyuker and Filippos I. Vokolos. 2000. Experience with Performance Testing of Software Systems: Issues, an Approach, and Case Study. IEEE Trans. Softw. Eng. 26, 12 (Dec. 2000), 1147–1156.

Digital Library

[62]

Elain J. Weyuker and Filippos I. Vokolos. 2000. Experience with Performance Testing of Software Systems: Issues, An Approach, and Case Study. IEEE Transactions on Software Engineering 26, 12 (2000), 1147–1156.

Digital Library

[63]

Paul R. Wilson. 1992. Uniprocessor Garbage Collection Techniques. 1–42.

[64]

Dacong Yan, Guoqing Xu, and Atanas Rountev. 2012. Uncovering Performance Problems in Java Applications with Reference Propagation Profiling. In Proceedings of the 34th International Conference on Software Engineering. 134–144.

Digital Library

[65]

Shahed Zaman, Bram Adams, and Ahmed E. Hassan. 2012. A Qualitative Study on Performance Bugs. In Proceedings of the 9th IEEE Working Conference on Mining Software Repositories. 199–208.

Digital Library

[66]

Lingming Zhang, Dan Hao, Lu Zhang, Gregg Rothermel, and Hong Mei. 2013. Bridging the Gap Between the Total and Additional Test-case Prioritization Strategies. In Proceedings of the 2013 International Conference on Software Engineering (ICSE ’13). 192–201.

Digital Library

[67]

Pingyu Zhang, Sebastian Elbaum, and Matthew B. Dwyer. 2011. Automatic Generation of Load Tests. In Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering. 43–52.

Digital Library

[68]

Hao Zhong, Lu Zhang, and Hong Mei. 2006. An Experimental Comparison of Four Test Suite Reduction Techniques. In Proceedings of the 28th International Conference on Software Engineering. 636–640.

Digital Library

Cited By

Laaber CYue TAli S(2024)Evaluating Search-Based Software Microbenchmark PrioritizationIEEE Transactions on Software Engineering10.1109/TSE.2024.338083650:7(1687-1703)Online publication date: 1-Jul-2024
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Jin XFeng YWang CLiu YHu YGao YXia KGuo L(2024)PIPELINEASCODE: A CI/CD Workflow Management System through Configuration Files at ByteDance2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00109(1011-1022)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00109
Japke NWitzko CGrambow MBermbach DFu SCarnevale LRana OVillari M(2023)The Early Microbenchmark Catches the Bug -- Studying Performance Issues Using Micro- and Application BenchmarksProceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing10.1145/3603166.3632128(1-10)Online publication date: 4-Dec-2023
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Index Terms

PerfRanker: prioritization of performance regression tests for collection-intensive software
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

ISSTA 2017: Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis

July 2017

447 pages

ISBN:9781450350761

DOI:10.1145/3092703

General Chair:
Tevfik Bultan
University of California at Santa Barbara, USA
,
Program Chair:
Koushik Sen
University of California at Berkeley, USA

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Published: 10 July 2017

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July 10 - 14, 2017

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Cited By

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https://dl.acm.org/doi/10.1109/TSE.2024.3380836
Jin XFeng YWang CLiu YHu YGao YXia KGuo L(2024)PIPELINEASCODE: A CI/CD Workflow Management System through Configuration Files at ByteDance2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00109(1011-1022)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00109
Japke NWitzko CGrambow MBermbach DFu SCarnevale LRana OVillari M(2023)The Early Microbenchmark Catches the Bug -- Studying Performance Issues Using Micro- and Application BenchmarksProceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing10.1145/3603166.3632128(1-10)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3603166.3632128
Hassan FMeng NWang X(2023)UniLoc: Unified Fault Localization of Continuous Integration FailuresACM Transactions on Software Engineering and Methodology10.1145/359379932:6(1-31)Online publication date: 8-May-2023
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Trubiani CPinciroli RBiaggi AFontana F(2023)Automated Detection of Software Performance Antipatterns in Java-Based ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2023.323432149:4(2873-2891)Online publication date: 1-Apr-2023
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Al Shoaibi DMkaouer M(2023)Understanding Software Performance Challenges an Empirical Study on Stack Overflow2023 International Conference on Code Quality (ICCQ)10.1109/ICCQ57276.2023.10114662(1-15)Online publication date: 22-Apr-2023
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