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Compiler bug isolation via effective witness test program generation

Authors:

Lingming Zhang,

Lu ZhangAuthors Info & Claims

ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 223 - 234

https://doi.org/10.1145/3338906.3338957

Published: 12 August 2019 Publication History

Abstract

Compiler bugs are extremely harmful, but are notoriously difficult to debug because compiler bugs usually produce few debugging information. Given a bug-triggering test program for a compiler, hundreds of compiler files are usually involved during compilation, and thus are suspect buggy files. Although there are lots of automated bug isolation techniques, they are not applicable to compilers due to the scalability or effectiveness problem. To solve this problem, in this paper, we transform the compiler bug isolation problem into a search problem, i.e., searching for a set of effective witness test programs that are able to eliminate innocent compiler files from suspects. Based on this intuition, we propose an automated compiler bug isolation technique, DiWi, which (1) proposes a heuristic-based search strategy to generate such a set of effective witness test programs via applying our designed witnessing mutation rules to the given failing test program, and (2) compares their coverage to isolate bugs following the practice of spectrum-based bug isolation. The experimental results on 90 real bugs from popular GCC and LLVM compilers show that DiWi effectively isolates 66.67%/78.89% bugs within Top-10/Top-20 compiler files, significantly outperforming state-of-the-art bug isolation techniques.

References

[1]

Accessed: 2019. Clang Libtooling library. http://clang.llvm.org/docs/LibTooling. html.

[2]

Accessed: 2019. GCC. https://gcc.gnu.org.

[3]

Accessed: 2019. GCC bug repository. https://gcc.gnu.org/bugzilla/.

[4]

Accessed: 2019. Gcov. https://gcc.gnu.org/onlinedocs/gcc/Gcov.html.

[5]

Accessed: 2019. LLVM. https://llvm.org.

[6]

Accessed: 2019. LLVM bug repository. https://bugs.llvm.org.

[7]

Rui Abreu, Peter Zoeteweij, and Arjan JC Van Gemund. 2007. On the accuracy of spectrum-based fault localization. In TAICPART-MUTATION 2007. 89–98.

Digital Library

[8]

Shay Artzi, Julian Dolby, Frank Tip, and Marco Pistoia. 2010. Directed test generation for e�ective fault localization. In ISSTA. 49–60.

Digital Library

[9]

Tien-Duy B Le, David Lo, Claire Le Goues, and Lars Grunske. 2016. A learning-torank based fault localization approach using likely invariants. In ISSTA. 177–188.

Digital Library

[10]

Benoit Baudry, Franck Fleurey, and Yves Le Traon. 2006. Improving test suites for e�cient fault localization. In ICSE. 82–91.

Digital Library

[11]

José Campos, Rui Abreu, Gordon Fraser, and Marcelo d’Amorim. 2013. Entropybased test generation for improved fault localization. In ASE. 257–267.

Digital Library

[12]

Jacqueline M. Caron and Peter A. Darnell. 1990. Bug�nd: A Tool for Debugging Optimizing Compilers. SIGPLAN Notices 25, 1 (1990), 17–22.

Digital Library

[13]

Bor-Yuh Evan Chang, Adam Chlipala, George C. Necula, and Robert R. Schneck. 2005. Type-based veri�cation of assembly language for compiler debugging. In TLDI. 91–102.

Digital Library

[14]

Junjie Chen. 2018. Learning to accelerate compiler testing. In ICSE: Companion Proceeedings. 472–475.

Digital Library

[15]

Junjie Chen, Yanwei Bai, Dan Hao, Yingfei Xiong, Hongyu Zhang, and Bing Xie. 2017. Learning to prioritize test programs for compiler testing. In ICSE. 700–711.

Digital Library

[16]

Junjie Chen, Yanwei Bai, Dan Hao, Yingfei Xiong, Hongyu Zhang, Lu Zhang, and Bing Xie. 2016. Test case prioritization for compilers: A text-vector based approach. In ICST. 266–277.

[17]

Junjie Chen, Wenxiang Hu, Dan Hao, Yingfei Xiong, Hongyu Zhang, Lu Zhang, and Bing Xie. 2016. An empirical comparison of compiler testing techniques. In ICSE. 180–190.

Digital Library

[18]

Junjie Chen, Guancheng Wang, Dan Hao, Yingfei Xiong, Hongyu Zhang, Lu Zhang, and XIE Bing. 2018. Coverage Prediction for Accelerating Compiler Testing. TSE (2018). to appear.

[19]

Yang Chen, Alex Groce, Chaoqiang Zhang, Weng-Keen Wong, Xiaoli Fern, Eric Eide, and John Regehr. 2013. Taming compiler fuzzers. In PLDI, Vol. 48. 197–208.

Digital Library

[20]

Yuting Chen, Ting Su, Chengnian Sun, Zhendong Su, and Jianjun Zhao. 2016. Coverage-directed Di�erential Testing of JVM Implementations. In PLDI. 85–99.

Digital Library

[21]

Vidroha Debroy and W Eric Wong. 2010. Using mutation to automatically suggest �xes for faulty programs. In ICST. 65–74.

Digital Library

[22]

Nicholas DiGiuseppe and James A Jones. 2011. On the in�uence of multiple faults on coverage-based fault localization. In ISSTA. 210–220.

Digital Library

[23]

Yadolah Dodge. 2006. The Oxford dictionary of statistical terms. Oxford University Press.

[24]

Alastair F. Donaldson, Hugues Evrard, Andrei Lascu, and Paul Thomson. 2017. Automated Testing of Graphics Shader Compilers. Proc. ACM Program. Lang. 1, OOPSLA (2017), 93:1–93:29.

Digital Library

[25]

Robert Feldt, Simon Poulding, David Clark, and Shin Yoo. 2016. Test set diameter: Quantifying the diversity of sets of test cases. In ICST. 223–233.

[26]

Robert Feldt, Richard Torkar, Tony Gorschek, and Wasif Afzal. 2008. Searching for cognitively diverse tests: Towards universal test diversity metrics. In ICSTW. 178–186.

Digital Library

[27]

Zachary P Fry and Westley Weimer. 2010. A human study of fault localization accuracy. In ICSM. 1–10.

Digital Library

[28]

Ali Ghanbari, Samuel Benton, and Lingming Zhang. 2019. Practical Program Repair via Bytecode Mutation. In ISSTA. to appear.

Digital Library

[29]

Alex Groce, Chaoqiang Zhang, Eric Eide, Yang Chen, and John Regehr. 2012. Swarm testing. In ISSTA. 78–88.

Digital Library

[30]

Chris Hathhorn, Chucky Ellison, and Grigore Roşu. 2015. De�ning the Unde- �nedness of C. In PLDI. 336–345.

[31]

K. Scott Hemmert, Justin L. Tripp, Brad L. Hutchings, and Preston A. Jackson. 2003. Source Level Debugger for the Sea Cucumber Synthesizing Compiler. In FCCM. 228.

Digital Library

[32]

Satia Herfert, Jibesh Patra, and Michael Pradel. 2017. Automatically Reducing Tree-structured Test Inputs. In ASE. 861–871.

Digital Library

[33]

Christian Holler, Kim Herzig, and Andreas Zeller. 2012. Fuzzing with code fragments. In USENIX Security. 445–458.

Digital Library

[34]

Josie Holmes and Alex Groce. 2018. Causal distance-metric-based assistance for debugging after compiler fuzzing. In ISSRE. 166–177.

[35]

Shin Hong, Byeongcheol Lee, Taehoon Kwak, Yiru Jeon, Bongsuk Ko, Yunho Kim, and Moonzoo Kim. 2015. Mutation-based fault localization for real-world multilingual programs. In ASE. 464–475.

Digital Library

[36]

Reyhaneh Jabbarvand and Sam Malek. 2017. µDroid: an energy-aware mutation testing framework for Android. In FSE. 208–219.

Digital Library

[37]

Dennis Je�rey, Neelam Gupta, and Rajiv Gupta. 2008. Fault Localization Using Value Replacement. In ISSTA. 167–178.

Digital Library

[38]

James A Jones and Mary Jean Harrold. 2005. Empirical evaluation of the tarantula automatic fault-localization technique. In ASE. 273–282.

Digital Library

[39]

René Just. 2014. The Major mutation framework: E�cient and scalable mutation analysis for Java. In ISSTA. 433–436.

Digital Library

[40]

Robert E. Kass, Bradley P. Carlin, Andrew Gelman, and Radford M. Neal. 1998. Markov Chain Monte Carlo in Practice: A Roundtable Discussion. American Statistician 52, 2 (1998), 93–100.

[41]

Nico Krebs and Lothar Schmitz. 2014. Jaccie: A Java-based compiler–compiler for generating, visualizing and debugging compiler components. Science of Computer Programming 79 (2014), 101–115.

Digital Library

[42]

Stephen Kyle, Hugh Leather, Björn Franke, Dave Butcher, and Stuart Monteith. 2015. Application of Domain-aware Binary Fuzzing to Aid Android Virtual Machine Testing. In VEE. 121–132.

Digital Library

[43]

Vu Le, Mehrdad Afshari, and Zhendong Su. 2014. Compiler validation via equivalence modulo inputs. In PLDI. 216–226.

Digital Library

[44]

Vu Le, Chengnian Sun, and Zhendong Su. 2015. Finding Deep Compiler Bugs via Guided Stochastic Program Mutation. In OOPSLA. 386–399.

Digital Library

[45]

Wei Le and Mary Lou So�a. 2010. Path-based fault correlations. In FSE. 307–316.

Digital Library

[46]

Wei Le and Mary Lou So�a. 2011. Generating analyses for detecting faults in path segments. In ISSTA. 320–330.

Digital Library

[47]

Wei Le and Mary Lou So�a. 2013. Marple: Detecting faults in path segments using automatically generated analyses. TOSEM 22, 3 (2013), 18.

Digital Library

[48]

Claire Le Goues, Michael Dewey-Vogt, Stephanie Forrest, and Westley Weimer. 2012. A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each. In ICSE. 3–13.

Digital Library

[49]

Juneyoung Lee, Yoonseung Kim, Youngju Song, Chung-Kil Hur, Sanjoy Das, David Majnemer, John Regehr, and Nuno P Lopes. 2017. Taming unde�ned behavior in LLVM. In PLDI. 633–647.

Digital Library

[50]

Xia Li, Wei Li, Yuqun Zhang, and Lingming Zhang. 2019. DeepFL: Integrating Multiple Fault Diagnosis Dimensions for Deep Fault Localization. In ISSTA. to appear.

Digital Library

[51]

Xia Li and Lingming Zhang. 2017. Transforming Programs and Tests in Tandem for Fault Localization. In OOPSLA. 92:1–92:30.

Digital Library

[52]

Ben Liblit, Mayur Naik, Alice X. Zheng, Alex Aiken, and Michael I. Jordan. 2005. Scalable Statistical Bug Isolation. In PLDI. 15–26.

Digital Library

[53]

Christopher Lidbury, Andrei Lascu, Nathan Chong, and Alastair F. Donaldson. 2015. Many-core Compiler Fuzzing. In PLDI. 65–76.

Digital Library

[54]

Bing Liu, Shiva Nejati, Lionel C Briand, et al. 2017. Improving fault localization for Simulink models using search-based testing and prediction models. In SANER. 359–370.

[55]

Wes Masri. 2015. Automated Fault Localization: Advances and Challenges. In Advances in Computers. Vol. 99. 103–156.

[56]

Wes Masri and Rawad Abou Assi. 2010. Cleansing test suites from coincidental correctness to enhance fault-localization. In ICST. 165–174.

Digital Library

[57]

William M McKeeman. 1998. Di�erential testing for software. Digital Technical Journal 10, 1 (1998), 100–107.

[58]

Hong Mei and Lu Zhang. 2018. Can big data bring a breakthrough for software automation? SCIENCE CHINA Information Sciences 61, 5 (2018), 056101:1–056101:3.

[59]

Martin Monperrus. 2018. Automatic software repair: a bibliography. CSUR 51, 1 (2018), 17:1–17:24.

Digital Library

[60]

Seokhyeon Moon, Yunho Kim, Moonzoo Kim, and Shin Yoo. 2014. Ask the mutants: Mutating faulty programs for fault localization. In ICST. 153–162.

Digital Library

[61]

Francisco Gomes de Oliveira Neto, Robert Feldt, Linda Erlenhov, and José Benardi de Souza Nunes. 2018. Visualizing test diversity to support test optimisation. arXiv preprint arXiv:1807.05593 (2018).

[62]

Hoang Duong Thien Nguyen, Dawei Qi, Abhik Roychoudhury, and Satish Chandra. 2013. Sem�x: Program repair via semantic analysis. In ICSE. 772–781.

[63]

Flemming Nielson, Hanne R. Nielson, and Chris Hankin. 1999. Principles of program analysis. Springer Verlag Berlin (1999).

Digital Library

[64]

Kazunori Ogata, Tamiya Onodera, Kiyokuni Kawachiya, Hideaki Komatsu, and Toshio Nakatani. 2006. Replay compilation: improving debuggability of a just-intime compiler. In OOPSLA. 241–252.

Digital Library

[65]

Kai Pan, Sunghun Kim, and E James Whitehead. 2009. Toward an understanding of bug �x patterns. EMSE 14, 3 (2009), 286–315.

Digital Library

[66]

Mike Papadakis and Yves Le Traon. 2012. Using mutants to locate" unknown" faults. In ICST. 691–700.

Digital Library

[67]

Mike Papadakis and Yves Le Traon. 2015. Metallaxis-FL: Mutation-based Fault Localization. STVR 25, 5-7 (2015), 605–628.

Digital Library

[68]

Spencer Pearson, José Campos, René Just, Gordon Fraser, Rui Abreu, Michael D. Ernst, Deric Pang, and Benjamin Keller. 2017. Evaluating and Improving Fault Localization. In ICSE. 609–620.

Digital Library

[69]

John Regehr, Yang Chen, Pascal Cuoq, Eric Eide, Chucky Ellison, and Xuejun Yang. 2012. Test-case reduction for C compiler bugs. In PLDI, Vol. 47. 335–346.

Digital Library

[70]

Manos Renieres and Steven P Reiss. 2003. Fault localization with nearest neighbor queries. In ASE. 30–39.

Digital Library

[71]

Jeremias Rö ler, Gordon Fraser, Andreas Zeller, and Alessandro Orso. 2012. Isolating failure causes through test case generation. In ISSTA. 309–319. ESEC/FSE ’19, August 26–30, 2019, Tallinn, Estonia Junjie Chen, Jiaqi Han, Peiyi Sun, Lingming Zhang, Dan Hao, and Lu Zhang

[72]

Raul Santelices, James A Jones, Yanbing Yu, and Mary Jean Harrold. 2009. Lightweight fault-localization using multiple coverage types. In ICSE. 56–66.

Digital Library

[73]

Anthony M. Sloane. 1999. Debugging Eli-Generated Compilers With Noosa. In CC. 17–31.

Digital Library

[74]

Jeongju Sohn and Shin Yoo. 2017. FLUCCS: using code and change metrics to improve fault localization. In ISSTA. 273–283.

Digital Library

[75]

Chengnian Sun, Vu Le, Qirun Zhang, and Zhendong Su. 2016. Toward Understanding Compiler Bugs in GCC and LLVM. In ISSTA. 294–305.

Digital Library

[76]

Chengnian Sun, Yuanbo Li, Qirun Zhang, Tianxiao Gu, and Zhendong Su. 2018. Perses: Syntax-guided program reduction. In ICSE. 361–371.

Digital Library

[77]

Shaowei Wang, David Lo, Lingxiao Jiang, Hoong Chuin Lau, et al. 2011. Searchbased fault localization. In ASE. 556–559.

Digital Library

[78]

Xi Wang, Nickolai Zeldovich, M Frans Kaashoek, and Armando Solar-Lezama. 2013. Towards optimization-safe systems: Analyzing the impact of unde�ned behavior. In SOSP. 260–275.

Digital Library

[79]

Frank Wilcoxon, SK Katti, and Roberta A Wilcox. 1970. Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Selected tables in mathematical statistics 1 (1970), 171–259.

[80]

W. Eric Wong, Ruizhi Gao, Yihao Li, Rui Abreu, and Franz Wotawa. 2016. A Survey on Software Fault Localization. TSE 42, 8 (2016), 707–740.

Digital Library

[81]

Jifeng Xuan and Martin Monperrus. 2014. Test case puri�cation for improving fault localization. In FSE. 52–63.

Digital Library

[82]

Xuejun Yang, Yang Chen, Eric Eide, and John Regehr. 2011. Finding and understanding bugs in C compilers. In PLDI. 283–294.

Digital Library

[83]

Andreas Zeller. 2002. Isolating cause-e�ect chains from computer programs. In FSE. 1–10.

Digital Library

[84]

Andreas Zeller and Ralf Hildebrandt. 2002. Simplifying and isolating failureinducing input. TSE 28, 2 (2002), 183–200.

Digital Library

[85]

Lingming Zhang, Miryung Kim, and Sarfraz Khurshid. 2011. Localizing failureinducing program edits based on spectrum information. In ICSM. 23–32.

Digital Library

[86]

Lingming Zhang, Tao Xie, Lu Zhang, Nikolai Tillmann, Jonathan De Halleux, and Hong Mei. 2010. Test generation via dynamic symbolic execution for mutation testing. In ICSM. 1–10.

Digital Library

[87]

Lingming Zhang, Lu Zhang, and Sarfraz Khurshid. 2013. Injecting mechanical faults to localize developer faults for evolving software. In OOPSLA. 765–784.

Digital Library

[88]

Mengshi Zhang, Xia Li, Lingming Zhang, and Sarfraz Khurshid. 2017. Boosting Spectrum-based Fault Localization Using PageRank. In ISSTA. 261–272.

Digital Library

[89]

Qirun Zhang, Chengnian Sun, and Zhendong Su. 2017. Skeletal Program Enumeration for Rigorous Compiler Testing. In PLDI. 347–361.

Digital Library

Cited By

Liu YZhu MDong JYu JHao DFilkov VRay BZhou M(2024)Compiler Bug Isolation via Enhanced Test Program MutationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695074(819-830)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695074
Kim HKim SLee JCha SChristakis MPradel M(2024)AsFuzzer: Differential Testing of Assemblers with Error-Driven Grammar InferenceProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680345(1099-1111)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680345
Chen JWang JSun YCheng PChen JChristakis MPradel M(2024)Isolation-Based Debugging for Neural NetworksProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3652132(338-349)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3652132
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Index Terms

Compiler bug isolation via effective witness test program generation
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

ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 2019

1264 pages

ISBN:9781450355728

DOI:10.1145/3338906

General Chairs:
Marlon Dumas
University of Tartu, Estonia
,
Dietmar Pfahl
University of Tartu, Estonia
,
Program Chairs:
Sven Apel
Saarland University, Germany
,
Alessandra Russo
Imperial College, UK

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Published: 12 August 2019

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ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 26 - 30, 2019

Tallinn, Estonia

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Liu YZhu MDong JYu JHao DFilkov VRay BZhou M(2024)Compiler Bug Isolation via Enhanced Test Program MutationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695074(819-830)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695074
Kim HKim SLee JCha SChristakis MPradel M(2024)AsFuzzer: Differential Testing of Assemblers with Error-Driven Grammar InferenceProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680345(1099-1111)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680345
Chen JWang JSun YCheng PChen JChristakis MPradel M(2024)Isolation-Based Debugging for Neural NetworksProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3652132(338-349)Online publication date: 11-Sep-2024
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Tu HZhou ZJiang HYusuf ILi YJiang L(2024)Isolating Compiler Bugs by Generating Effective Witness Programs with Large Language ModelsIEEE Transactions on Software Engineering10.1109/TSE.2024.3397822(1-20)Online publication date: 2024
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