research-article

Enhanced compiler bug isolation via memoized search

Authors:

Lingming ZhangAuthors Info & Claims

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

Pages 78 - 89

https://doi.org/10.1145/3324884.3416570

Published: 27 January 2021 Publication History

Abstract

Compiler bugs can be disastrous since they could affect all the software systems built on the buggy compilers. Meanwhile, diagnosing compiler bugs is extremely challenging since usually limited debugging information is available and a large number of compiler files can be suspicious. More specifically, when compiling a given bug-triggering test program, hundreds of compiler files are usually involved, and can all be treated as suspicious buggy files. To facilitate compiler debugging, in this paper we propose the first reinforcement compiler bug isolation approach via structural mutation, called RecBi. For a given bug-triggering test program, RecBi first augments traditional local mutation operators with structural ones to transform it into a set of passing test programs. Since not all the passing test programs can help isolate compiler bugs effectively, RecBi further leverages reinforcement learning to intelligently guide the process of passing test program generation. Then, RecBi ranks all the suspicious files by analyzing the compiler execution traces of the generated passing test programs and the given failing test program following the practice of compiler bug isolation. The experimental results on 120 real bugs from two most popular C open-source compilers, i.e., GCC and LLVM, show that RecBi is able to isolate about 23%/58%/78% bugs within Top-1/Top-5/Top-10 compiler files, and significantly outperforms the state-of-the-art compiler bug isolation approach by improving 92.86%/55.56%/25.68% isolation effectiveness in terms of Top-1/Top-5/Top-10 results.

References

[1]

Accessed: 2020. Clang Libtooling library. http://clang.llvm.org/docs/LibTooling.html.

[2]

Accessed: 2020. GCC. https://gcc.gnu.org.

[3]

Accessed: 2020. Gcov. https://gcc.gnu.org/onlinedocs/gcc/Gcov.html.

[4]

Accessed: 2020. LLVM. https://llvm.org.

[5]

Accessed: 2020. PyTorch. https://pytorch.org/.

[6]

R. Abreu, P. Zoeteweij, and A. J. c. Van Gemund. 2006. An Evaluation of Similarity Coefficients for Software Fault Localization. In 2006 12th Pacific Rim International Symposium on Dependable Computing (PRDC'06). 39--46.

Digital Library

[7]

Rui Abreu, Peter Zoeteweij, and Arjan JC Van Gemund. 2007. On the accuracy of spectrum-based fault localization. In Testing: Academic and Industrial Conference Practice and Research Techniques-MUTATION (TAICPART-MUTATION 2007). IEEE, 89--98.

Digital Library

[8]

Milan Aggarwal, Aarushi Arora, Shagun Sodhani, and Balaji Krishnamurthy. 2018. Improving Search Through A3C Reinforcement Learning Based Conversational Agent. In 18th International Conference on Computational Science. 273--286.

Digital Library

[9]

Samuel Benton, Xia Li, Yiling Lou, and Lingming Zhang. 2020. On the Effectiveness of Unified Debugging: An Extensive Study on 16 Program Repair Systems. In ASE. to appear.

[10]

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

Digital Library

[11]

Bor-Yuh Evan Chang, Adam Chlipala, George C. Necula, and Robert R. Schneck. 2005. Type-based verification of assembly language for compiler debugging. In Proceedings of TLDI'05: 2005 ACM SIGPLAN International Workshop on Types in Languages Design and Implementation. 91--102.

[12]

Junjie Chen. 2018. Learning to accelerate compiler testing. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings. 472--475.

Digital Library

[13]

Junjie Chen, Yanwei Bai, Dan Hao, Yingfei Xiong, Hongyu Zhang, and Bing Xie. 2017. Learning to prioritize test programs for compiler testing. In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). IEEE, 700--711.

Digital Library

[14]

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 2016 IEEE International Conference on Software Testing, Verification and Validation. 266--277.

[15]

Junjie Chen, Yanwei Bai,Dan Hao, Lingming Zhang, Lu Zhang, Bing Xie, and Hong Mei. 2016. Supporting oracle construction via static analysis. In 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 178--189.

Digital Library

[16]

Junjie Chen, Jiaqi Han, Peiyi Sun, Lingming Zhang, Dan Hao, and Lu Zhang. 2019. Compiler bug isolation via effective witness test program generation. In Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2019, Tallinn, Estonia, August 26--30, 2019. 223--234.

Digital Library

[17]

Junjie Chen, Wenxiang Hu, Dan Hao, Yingfei Xiong, Hongyu Zhang, and Lu Zhang. 2019. Static duplicate bug-report identification for compilers. SCIENTIA SINICA Informationis 49, 10 (2019), 1283--1298.

[18]

Junjie Chen, Wenxiang Hu, Dan Hao, Yingfei Xiong, Hongyu Zhang, Lu Zhang, and Bing Xie. 2016. An empirical comparison of compiler testing techniques. In Proceedings of the 38th International Conference on Software Engineering. 180--190.

Digital Library

[19]

Junjie Chen, Jibesh Patra, Michael Pradel, Yingfei Xiong, Hongyu Zhang, Dan Hao, and Lu Zhang. 2020. A Survey of Compiler Testing. ACM Computing Surveys (CSUR) 53 (02 2020), 1--36.

[20]

Junjie Chen, Guancheng Wang, Dan Hao, Yingfei Xiong, Hongyu Zhang, and Lu Zhang. 2019. History-Guided Configuration Diversification for Compiler Test-Program Generation. In 34th IEEE/ACM International Conference on Automated Software Engineering. 305--316.

[21]

Junjie Chen, Guancheng Wang, Dan Hao, Yingfei Xiong, Hongyu Zhang, Lu Zhang, and XIE Bing. 2018. Coverage prediction for accelerating compiler testing. IEEE Transactions on Software Engineering (2018).

Digital Library

[22]

Yang Chen, Alex Groce, Chaoqiang Zhang, Weng-Keen Wong, Xiaoli Z. Fern, Eric Eide, and John Regehr. 2013. Taming compiler fuzzers. In ACM SIGPLAN Conference on Programming Language Design and Implementation. 197--208.

Digital Library

[23]

Yuting Chen, Ting Su, and Zhendong Su. 2019. Deep differential testing of JVM implementations. In Proceedings of the 41st International Conference on Software Engineering. 1257--1268.

Digital Library

[24]

Yuting Chen, Ting Su, Chengnian Sun, Zhendong Su, and Jianjun Zhao. 2016. Coverage-directed differential testing of JVM implementations. In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation. 85--99.

Digital Library

[25]

Siddhartha Chib and Edward Greenberg. 1995. Understanding the metropolis-hastings algorithm. The american statistician 49, 4 (1995), 327--335.

[26]

Tung Dao, Lingming Zhang, and Na Meng. 2017. How does execution information help with information-retrieval based bug localization?. In Proceedings of the 25th International Conference on Program Comprehension. 241--250.

Digital Library

[27]

Nicholas DiGiuseppe and James A. Jones. 2011. On the Influence of Multiple Faults on Coverage-Based Fault Localization. In Proceedings of the 2011 International Symposium on Software Testing and Analysis. 210--220.

[28]

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

[29]

Ivo Grondman, Lucian Busoniu, Gabriel AD Lopes, and Robert Babuska. 2012. A survey of actor-critic reinforcement learning: Standard and natural policy gradients. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, 6 (2012), 1291--1307.

Digital Library

[30]

Chris Hathhorn, Chucky Ellison, and Grigore Rosu. 2015. Defining the undefinedness of C. In Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation. 336--345.

Digital Library

[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 11th IEEE Symposium on Field-Programmable Custom Computing Machines. 228.

[32]

Satia Herfert, Jibesh Patra, and Michael Pradel. 2017. Automatically reducing tree-structured test inputs. In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering. 861--871.

Digital Library

[33]

Josie Holmes and Alex Groce. 2018. Causal Distance-Metric-Based Assistance for Debugging after Compiler Fuzzing. In 29th IEEE International Symposium on Software Reliability Engineering. 166--177.

[34]

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 Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering. 464--475.

Digital Library

[35]

Dennis Jeffrey, Neelam Gupta, and Rajiv Gupta. 2008. Fault localization using value replacement. In Proceedings of the 2008 international symposium on Software testing and analysis. 167--178.

Digital Library

[36]

James A. Jones and Mary Jean Harrold. 2005. Empirical Evaluation of the Tarantula Automatic Fault-Localization Technique. In Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering. 273--282.

[37]

Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. 1996. Reinforcement learning: A survey. Journal of artificial intelligence research 4 (1996), 237--285.

Digital Library

[38]

Pavneet Singh Kochhar, Xin Xia, David Lo, and Shanping Li. 2016. Practitioners' expectations on automated fault localization. In Proceedings of the 25th International Symposium on Software Testing and Analysis. 165--176.

Digital Library

[39]

Nico Krebs and Lothar Schmitz. 2014. Jaccie: A Java-based compiler-compiler for generating, visualizing and debugging compiler components. Sci. Comput. Program. 79 (2014), 101--115.

Digital Library

[40]

Tien-Duy B. Le, David Lo, Claire Le Goues, and Lars Grunske. 2016. A learning-to-rank based fault localization approach using likely invariants. In Proceedings of the 25th International Symposium on Software Testing and Analysis. 177--188.

Digital Library

[41]

Vu Le, Mehrdad Afshari, and Zhendong Su. 2014. Compiler validation via equivalence modulo inputs. In ACM SIGPLAN Conference on Programming Language Design and Implementation. 216--226.

Digital Library

[42]

Vu Le, Chengnian Sun, and Zhendong Su. 2015. Finding deep compiler bugs via guided stochastic program mutation. In Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications. 386--399.

Digital Library

[43]

Juneyoung Lee, Yoonseung Kim, Youngju Song, Chung-Kil Hur, Sanjoy Das, David Majnemer, John Regehr, and Nuno P. Lopes. 2017. Taming undefined behavior in LLVM. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation. 633--647.

[44]

Xia Li, Wei Li, Yuqun Zhang, and Lingming Zhang. 2019. DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. 169--180.

Digital Library

[45]

Xia Li and Lingming Zhang. 2017. Transforming programs and tests in tandem for fault localization. Proc. ACM Program. Lang. 1, OOPSLA (2017), 92:1--92:30.

Digital Library

[46]

Yiling Lou, Ali Ghanbari, Xia Li, Lingming Zhang, Haotian Zhang, Dan Hao, and Lu Zhang. 2020. Can Automated Program Repair Refine Fault Localization? A Unified Debugging Approach. In ISSTA. to appear.

[47]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing Atari with Deep Reinforcement Learning. (2013).

[48]

Seokhyeon Moon, Yunho Kim, Moonzoo Kim, and Shin Yoo. 2014. Ask the mutants: Mutating faulty programs for fault localization. In 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation. IEEE, 153--162.

Digital Library

[49]

Kazunori Ogata, Tamiya Onodera, Kiyokuni Kawachiya, Hideaki Komatsu, and Toshio Nakatani. 2006. Replay compilation: improving debuggability of a just-in-time compiler. In Proceedings of the 21th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications. 241--252.

Digital Library

[50]

Mike Papadakis and Yves Le Traon. 2012. Using Mutants to Locate "Unknown" Faults. In Fifth IEEE International Conference on Software Testing, Verification and Validation. 691--700.

Digital Library

[51]

Mike Papadakis and Yves Le Traon. 2015. Metallaxis-FL: mutation-based fault localization. Softw. Test. Verification Reliab. 25, 5--7 (2015), 605--628.

Digital Library

[52]

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 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). IEEE, 609--620.

Digital Library

[53]

John Regehr, Yang Chen, Pascal Cuoq, Eric Eide, Chucky Ellison, and Xuejun Yang. 2012. Test-case reduction for C compiler bugs. In ACM SIGPLAN Conference on Programming Language Design and Implementation. 335--346.

Digital Library

[54]

Anthony M. Sloane. 1999. Debugging Eli-Generated Compilers With Noosa. In Compiler Construction, 8th International Conference, CC'99, Held as Part of the European Joint Conferences on the Theory and Practice of Software. 17--31.

[55]

Jeongju Sohn and Shin Yoo. 2017. FLUCCS: using code and change metrics to improve fault localization. In Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis. 273--283.

Digital Library

[56]

Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, and Michael Bowling. 2018. Actor-critic policy optimization in partially observable multiagent environments. In Advances in neural information processing systems. 3422--3435.

[57]

Pei-Hao Su, Pawel Budzianowski, Stefan Ultes, Milica Gasic, and Steve Young. 2017. Sample-efficient actor-critic reinforcement learning with supervised data for dialogue management. arXiv preprint arXiv:1707.00130 (2017).

[58]

Chengnian Sun, Vu Le, Qirun Zhang, and Zhendong Su. 2016. Toward understanding compiler bugs in GCC and LLVM. In Proceedings of the 25th International Symposium on Software Testing and Analysis. 294--305.

Digital Library

[59]

Chengnian Sun, Yuanbo Li, Qirun Zhang, Tianxiao Gu, and Zhendong Su. 2018. Perses: syntax-guided program reduction. In Proceedings of the 40th International Conference on Software Engineering. 361--371.

Digital Library

[60]

Richard Sutton, David Mcallester, Satinder Singh, and Yishay Mansour. 2000. Policy Gradient Methods for Reinforcement Learning with Function Approximation. Adv. Neural Inf. Process. Syst 12 (02 2000).

[61]

R. S. Sutton and A. G. Barto. 1998. Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks 9, 5 (1998), 1054--1054.

Digital Library

[62]

Konda Vijay, R. and Tsitsiklis John, N. 2000. Actor-critic Algorithms. SIAM Journal on Control and Optimization (April 2000).

[63]

Mnih Volodymyr, Badia Adria, Puigdomènech, Mirza Mehdi, and Graves Alex. 2016. Asynchronous Methods for Deep Reinforcement Learning. In ICML2016. 1928--1937.

[64]

Song Wang, Taiyue Liu, and Lin Tan. 2016. Automatically learning semantic features for defect prediction. In Proceedings of the 38th International Conference on Software Engineering. 297--308.

Digital Library

[65]

Xi Wang, Nickolai Zeldovich, M. Frans Kaashoek, and Armando Solar-Lezama. 2013. Towards optimization-safe systems: analyzing the impact of undefined behavior. In ACM SIGOPS 24th Symposium on Operating Systems Principles. 260--275.

Digital Library

[66]

Zan Wang, Ming Yan, Junjie Chen, Shuang Liu, and Dongdi Zhang. 2020. Deep Learning Library Testing via Effective Model Generation. In The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. to appear.

Digital Library

[67]

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

Digital Library

[68]

Jifeng Xuan and Martin Monperrus. 2014. Learning to Combine Multiple Ranking Metrics for Fault Localization. In 30th IEEE International Conference on Software Maintenance and Evolution. 191--200.

Digital Library

[69]

Jifeng Xuan and Martin Monperrus. 2014. Test case purification for improving fault localization. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. 52--63.

Digital Library

[70]

Xuejun Yang, Yang Chen, Eric Eide, and John Regehr. 2011. Finding and understanding bugs in C compilers. In Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation. 283--294.

Digital Library

[71]

Andreas Zeller. 2002. Isolating cause-effect chains from computer programs. In Proceedings of the Tenth ACM SIGSOFT Symposium on Foundations of Software Engineering. 1--10.

Digital Library

[72]

Andreas Zeller and Ralf Hildebrandt. 2002. Simplifying and Isolating Failure-Inducing Input. IEEE Trans. Software Eng. 28, 2 (2002), 183--200.

Digital Library

[73]

Lingming Zhang, Miryung Kim, and Sarfraz Khurshid. 2011. Localizing failure-inducing program edits based on spectrum information. In 2011 27th IEEE International Conference on Software Maintenance (ICSM). 23--32.

Digital Library

[74]

Lingming Zhang, Lu Zhang, and Sarfraz Khurshid. 2013. Injecting mechanical faults to localize developer faults for evolving software. In Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages & Applications. 765--784.

Digital Library

[75]

Qirun Zhang, Chengnian Sun, and Zhendong Su. 2017. Skeletal program enumeration for rigorous compiler testing. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation. 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
Ma HZhang WShen QTian YChen JCheung SChristakis MPradel M(2024)Towards Understanding the Bugs in Solidity CompilerProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680362(1312-1324)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680362
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.2024.339782250:7(1768-1788)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3397822
Show More Cited By

Index Terms

Enhanced compiler bug isolation via memoized search
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software notations and tools
    1. Compilers
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

Recommendations

Enriching Compiler Testing with Real Program from Bug Report
ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

Researchers have proposed various approaches to generate test programs. The state-of-the-art approaches can be roughly divided into random-based and mutation-based approaches: random-based approaches generate random programs and mutation-based ...
Compiler bug isolation via effective witness test program generation
ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

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 ...
Compiler Bug Isolation via Enhanced Test Program Mutation
ASE '24: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering

Compilers are one of the most fundamental software systems. A large number of software systems rely on compilers for execution. Compiler bugs can significantly hinder software developers from diagnosing issues within their software. Therefore, it is ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

December 2020

1449 pages

ISBN:9781450367684

DOI:10.1145/3324884

General Chair:
John Grundy,
Program Chairs:
Claire Le Goues,
David Lo

Copyright © 2020 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]

Sponsors

In-Cooperation

IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundatio
National Science Foundation
Alibaba

Conference

ASE '20

Sponsor:

ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering

December 21 - 25, 2020

Virtual Event, Australia

Acceptance Rates

Overall Acceptance Rate 82 of 337 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
330
Total Downloads

Downloads (Last 12 months)62
Downloads (Last 6 weeks)3

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

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
Ma HZhang WShen QTian YChen JCheung SChristakis MPradel M(2024)Towards Understanding the Bugs in Solidity CompilerProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680362(1312-1324)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680362
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.2024.339782250:7(1768-1788)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3397822
Song YXie XXu B(2024)When debugging encounters artificial intelligence: state of the art and open challengesScience China Information Sciences10.1007/s11432-022-3803-967:4Online publication date: 21-Feb-2024
https://doi.org/10.1007/s11432-022-3803-9
Mindom PNikanjam AKhomh F(2024)Harnessing pre-trained generalist agents for software engineering tasksEmpirical Software Engineering10.1007/s10664-024-10597-830:1Online publication date: 11-Dec-2024
https://doi.org/10.1007/s10664-024-10597-8
Rahman ABose DBarsha FPandita R(2023)Defect Categorization in Compilers: A Multi-vocal Literature ReviewACM Computing Surveys10.1145/362631356:4(1-42)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3626313
Wu MOuyang YLu MChen JZhao YCui HYang GZhang YChandra SBlincoe KTonella P(2023)SJFuzz: Seed and Mutator Scheduling for JVM FuzzingProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616277(1062-1074)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616277
Zhang MXu ZTian YJiang YSun CChandra SBlincoe KTonella P(2023)PPR: Pairwise Program ReductionProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616275(338-349)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616275
Yang CChen JFan XJiang JSun JJust RFraser G(2023)Silent Compiler Bug De-duplication via Three-Dimensional AnalysisProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598087(677-689)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3597926.3598087
Lim HDebray SVerbrugge CLhoták OShen X(2023)Automatically Localizing Dynamic Code Generation Bugs in JIT Compiler Back-EndProceedings of the 32nd ACM SIGPLAN International Conference on Compiler Construction10.1145/3578360.3580260(145-155)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3578360.3580260
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.

Media

Figures

Other

Tables

View Table of Contents