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Compiler bugs pose a significant threat to safety-critical applications, and promptly as well as effectively isolating these bugs is crucial for assuring the quality of compilers.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in Proceedings of the International Conference On Machine Learning (ICML) , 2016, pp. 1928–1937
1937
Earlier work this paper cites.
T. J. McCabe, “A complexity measure,” IEEE Transactions on Software Engineering , no. 4, pp. 308–320, 1976
1976
Earlier work this paper cites.
L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement learning: A survey,” Journal of artificial intelligence research , vol. 4, pp. 237–285, 1996
1996
Earlier work this paper cites.
R. S. Sutton, D. McAllester, S. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” Advances in neural information processing systems , vol. 12, pp. 1057–1063, 1999
1999
Earlier work this paper cites.
V. Konda and J. Tsitsiklis, “Actor-critic algorithms,” Advances in neural information processing systems , vol. 12, pp. 1008–1014, 1999
1999
Earlier work this paper cites.
A. Vargha and H. D. Delaney, “A critique and improvement of the cl common language effect size statistics of mcgraw and wong,” Journal of Educational and Behavioral Statistics , vol. 25, no. 2, pp. 101–132, 2000
2000
Earlier work this paper cites.
A. Zeller, “Isolating cause-effect chains from computer programs,” ACM SIGSOFT Software Engineering Notes , vol. 27, no. 6, pp. 1–10, 2002
2002
Earlier work this paper cites.
M. Renieres and S. P. Reiss, “Fault localization with nearest neighbor queries,” in Proceedings of the 18th IEEE International Conference on Automated Software Engineering (ASE) , 2003, pp. 30–39
2003
Earlier work this paper cites.
B.-Y. E. Chang, A. Chlipala, G. C. Necula, and R. R. Schneck, “Type-based verification of assembly language for compiler debugging,” in Proceedings of the ACM International Workshop on Types in Languages Design and Implementation (PLDI) , 2005, pp. 91–102
2005
Earlier work this paper cites.
A. V. Aho, M. S. Lam, R. Sethi, and J. D. Ullman, Compilers: Principles, Techniques, and Tools (2nd Edition) . Addison-Wesley Longman Publishing Co., Inc., 2006
2006
Earlier work this paper cites.
R. Abreu, P. Zoeteweij, and A. J. Van Gemund, “On the accuracy of spectrum-based fault localization,” in Testing: Academic and industrial conference practice and research techniques-MUTATION (TAICPART-MUTATION) , 2007, pp. 89–98
2007
Earlier work this paper cites.
D. Jeffrey, N. Gupta, and R. Gupta, “Fault localization using value replacement,” in Proceedings of the 2008 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) , 2008, pp. 167–178
2008
Earlier work this paper cites.
C. D. Manning, Introduction to Information Retrieval . Syngress Publishing, 2008
2008
Earlier work this paper cites.
Y. Yu, J. A. Jones, and M. J. Harrold, “An empirical study of the effects of test-suite reduction on fault localization,” in Proceedings of the International Conference on Software Engineering (ICSE) , 2008, pp. 201–210
2008
Earlier work this paper cites.
X. Yang, Y. Chen, E. Eide, and J. Regehr, “Finding and understanding bugs in c compilers,” in Proceedings of the 32nd ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI) , 2011, pp. 283–294
2011
Earlier work this paper cites.
A. Arcuri and L. Briand, “A practical guide for using statistical tests to assess randomized algorithms in software engineering,” in Proceedings of the 33rd international conference on software engineering , 2011, pp. 1–10
2011
Earlier work this paper cites.
X. Xu, V. Debroy, W. Eric Wong, and D. Guo, “Ties within fault localization rankings: Exposing and addressing the problem,” International Journal of Software Engineering and Knowledge Engineering , vol. 21, no. 06, pp. 803–827, 2011
2011
Earlier work this paper cites.
I. Grondman, L. Busoniu, G. A. Lopes, and R. Babuska, “A survey of actor-critic reinforcement learning: Standard and natural policy gradients,” IEEE Transactions on Systems, Man, and Cybernetics , vol. 42, no. 6, pp. 1291–1307, 2012
2012
Earlier work this paper cites.
X. Wang, H. Chen, A. Cheung, Z. Jia, N. Zeldovich, and M. F. Kaashoek, “Undefined behavior: what happened to my code?” in Proceedings of the Asia-Pacific Workshop on Systems , 2012, pp. 1–7
2012
Earlier work this paper cites.
L. Gong, D. Lo, L. Jiang, and H. Zhang, “Interactive fault localization leveraging simple user feedback,” in the IEEE International Conference on Software Maintenance (ICSM) , 2012, pp. 67–76
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
X. Wang, N. Zeldovich, M. F. Kaashoek, and A. Solar-Lezama, “Towards optimization-safe systems: Analyzing the impact of undefined behavior,” in Proceedings of the ACM Symposium on Operating Systems Principles (SOSP) , 2013, pp. 260–275
2013
Earlier work this paper cites.
J. Xuan and M. Monperrus, “Test case purification for improving fault localization,” in Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE) , 2014, pp. 52–63
2014
Earlier work this paper cites.
S. Moon, Y. Kim, M. Kim, and S. Yoo, “Ask the mutants: Mutating faulty programs for fault localization,” in IEEE Seventh International Conference on Software Testing, Verification and Validation (ICST) , 2014, pp. 153–162
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Advances in neural information processing systems , vol. 27, pp. 1–9, 2014
2014
Earlier work this paper cites.
F. Kirchner, N. Kosmatov, V. Prevosto, J. Signoles, and B. Yakobowski, “Frama-c: A software analysis perspective,” Formal aspects of computing , vol. 27, no. 3, pp. 573–609, 2015
2015
Earlier work this paper cites.
W. E. Wong, R. Gao, Y. Li, R. Abreu, and F. Wotawa, “A survey on software fault localization,” IEEE Transactions on Software Engineering , vol. 42, no. 8, pp. 707–740, 2016
2016
Earlier work this paper cites.
C. D. Newman, T. Sage, M. L. Collard, H. W. Alomari, and J. I. Maletic, “srcslice: A tool for efficient static forward slicing,” in Proceedings of the 38th International Conference on Software Engineering Companion (SEC) , 2016, pp. 621–624
2016
Earlier work this paper cites.
C. Sun, V. Le, Q. Zhang, and Z. Su, “Toward understanding compiler bugs in gcc and llvm,” in Proceedings of the 25th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) , 2016, pp. 294–305
2016
Earlier work this paper cites.
W. Ling, P. Blunsom, E. Grefenstette, K. M. Hermann, T. Kočiskỳ, F. Wang, and A. Senior, “Latent predictor networks for code generation,” in Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) , 2016, pp. 599–609
2016
Earlier work this paper cites.
C. B. Harris and I. G. Harris, “Glast: Learning formal grammars to translate natural language specifications into hardware assertions,” in Design, Automation & Test in Europe Conference & Exhibition (DATE) , 2016, pp. 966–971
2016
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
S. Pearson, J. Campos, R. Just, G. Fraser, R. Abreu, M. D. Ernst, D. Pang, and B. Keller, “Evaluating and improving fault localization,” in Proceedings of the 39th IEEE/ACM International Conference on Software Engineering (ICSE) , 2017, pp. 609–620
2017
Cited alongside, same era.
J. Lee, Y. Kim, Y. Song, C.-K. Hur, S. Das, D. Majnemer, J. Regehr, and N. P. Lopes, “Taming undefined behavior in llvm,” ACM SIGPLAN Notices , vol. 52, no. 6, pp. 633–647, 2017
H. Tu, H. Jiang, Z. Zhou, Y. Tang, Z. Ren, L. Qiao, and L. Jiang, “Detecting C++ compiler front-end bugs via grammar mutation and differential testing,” IEEE Transactions on Reliability , pp. 1–15, 2022
2022
Later among the works it cites.
H. Tu, H. Jiang, X. Li, Z. Ren, Z. Zhou, and L. Jiang, “RemGen: Remanufacturing a random program generator for compiler testing,” in IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE) , 2022, pp. 529–540
2022
Later among the works it cites.
T. Theodoridis, M. Rigger, and Z. Su, “Finding missed optimizations through the lens of dead code elimination,” in Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) , 2022, pp. 697–709
2022
Later among the works it cites.
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto, “Stanford alpaca: An instruction-following llama model,” https://github.com/tatsu-lab/stanford_alpaca , 2023
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2017
Cited alongside, same era.
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction . MIT Press, 2018
2018
Cited alongside, same era.
G. Klees, A. Ruef, B. Cooper, S. Wei, and M. Hicks, “Evaluating fuzz testing,” in Proceedings of the 2018 ACM SIGSAC conference on computer and communications security , 2018, pp. 2123–2138
2018
Cited alongside, same era.
J. Holmes and A. Groce, “Causal distance-metric-based assistance for debugging after compiler fuzzing,” in IEEE 29th International Symposium on Software Reliability Engineering (ISSRE) , 2018, pp. 166–177
2018
Cited alongside, same era.
S. Iyer, I. Konstas, A. Cheung, and L. Zettlemoyer, “Mapping language to code in programmatic context,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2018, pp. 1643–1652
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Chen, J. Han, P. Sun, L. Zhang, D. Hao, and L. Zhang, “Compiler bug isolation via effective witness test program generation,” in Proceedings of the 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) , 2019, pp. 223–234
2019
Cited alongside, same era.
Y. Liu, “Fine-tune bert for extractive summarization,” arXiv preprint arXiv:1903.10318 , 2019
2019
Cited alongside, same era.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” Advances in neural information processing systems , vol. 32, pp. 1–11, 2019
2019
Cited alongside, same era.
2023
Closest in time.
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez et al. , “Vicuna: An open-source chatbot impressing gpt-4 with 90%* ChatGpt quality,” 2023
2023
Closest in time.
Y. Anand, Z. Nussbaum, B. Duderstadt, B. Schmidt, and A. Mulyar, “GPT4All: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo,” https://github.com/nomic-ai/gpt4all , 2023
2023
Closest in time.
2023
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
C. S. Xia, Y. Wei, and L. Zhang, “Automated program repair in the era of large pre-trained language models,” in Proceedings of the ACM/IEEE 45th International Conference on Software Engineering (ICSE) , 2023, pp. 1–12
2023
Closest in time.
Y. Deng, C. S. Xia, H. Peng, C. Yang, and L. Zhang, “Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,” in Proceedings of the 32nd ACM SIGSOFT international symposium on software testing and analysis (ISSTA) , 2023, pp. 423–435
2023
Closest in time.
F. Cassano, J. Gouwar, D. Nguyen, S. Nguyen, L. Phipps-Costin, D. Pinckney, M.-H. Yee, Y. Zi, C. J. Anderson, M. Q. Feldman et al. , “Multipl-e: a scalable and polyglot approach to benchmarking neural code generation,” IEEE Transactions on Software Engineering , pp. 1–17, 2023
2023
Closest in time.
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong, “Codegen: An open large language model for code with multi-turn program synthesis,” in Proceedings of the 11th International Conference on Learning Representations (ICLR) , 2023, pp. 1–25
2023
Closest in time.
2023
Closest in time.
R. Laboratories. (2023) Oclint. [Online]. Available: https://github.com/oclint
2023
Closest in time.
GNU. (2023) Gcov. [Online]. Available: https://gcc.gnu.org/onlinedocs/gcc/Gcov.html
2023
Closest in time.
P. Foundation. (2023) Pytorch. [Online]. Available: https://pytorch.org
2023
Closest in time.
2023
Closest in time.
E. Almazrouei, A. Cappelli, R. Cojocaru, M. Debbah, E. Goffinet, D. Heslow, J. Launay, Q. Malartic, B. Noune, B. Pannier, and G. Penedo, “Falcon-40b: an open large language model with state-of-the-art performance,” Hugging Face , 2023
2023
Closest in time.
2023
Closest in time.
H. Lim and S. Debray, “Automatically localizing dynamic code generation bugs in jit compiler back-end,” in Proceedings of the 32nd ACM SIGPLAN International Conference on Compiler Construction (CC) , 2023, pp. 145–155
2023
Closest in time.
2023
Closest in time.
S. Thakur, B. Ahmad, Z. Fan, H. Pearce, B. Tan, R. Karri, B. Dolan-Gavitt, and S. Garg, “Benchmarking large language models for automated verilog rtl code generation,” in Design, Automation & Test in Europe Conference & Exhibition (DATE) , 2023, pp. 1–6
2023
Closest in time.
C. Xu, Q. Sun, K. Zheng, X. Geng, P. Zhao, J. Feng, C. Tao, and D. Jiang, “Wizardlm: Empowering large language models to follow complex instructions,” arXiv e-prints , pp. 1–39, 2023
2023
Closest in time.
Stability-AI. (2023) Stablelm: Stability ai language models. [Online]. Available: https://github.com/Stability-AI/StableLM
2023
Closest in time.
Z. Zhang, J. Xue, D. Yang, and X. Mao, “Contextaug: model-domain failing test augmentation with contextual information,” Frontiers of Computer Science , vol. 18, no. 2, p. 182202, 2024
2024
Closest in time.
Z. Zhang, Y. Li, J. Xue, and X. Mao, “Improving fault localization with pre-training,” Frontiers of Computer Science , vol. 18, no. 1, p. 181205, 2024
2024
Closest in time.
Y. Yang, P. Huang, J. Cao, J. Li, Y. Lin, and F. Ma, “A prompt-based approach to adversarial example generation and robustness enhancement,” Frontiers of Computer Science , vol. 18, no. 4, p. 184318, 2024
2024
Closest in time.