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Program reduction is a prevalent technique to facilitate compilers' debugging by automatically minimizing bug-triggering programs.
Simplifying and isolating failure-inducing input
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Test Case Reduction: Beyond Bugs
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Test-case reduction and deduplication almost for free with transformation-based compiler testing. In Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation . 1017–1032
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Multipl-e: A scalable and extensible approach to benchmarking neural code generation
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OpenAI API
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OpenAI API: Temperature
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Large language models can be easily distracted by irrelevant context. In International Conference on Machine Learning . PMLR, 31210–31227
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SMT Solver Validation Empowered by Large Pre-Trained Language Models. In 38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023, Luxembourg, September 11-15, 2023 . IEEE, 1288–1300
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Is ChatGPT the Ultimate Programming Assistant–How far is it?
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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 . 423–435
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Large Language Models for Software Engineering: Survey and Open Problems. In 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) . IEEE Computer Society, Los Alamitos, CA, USA, 31–53
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LLM-Based Code Generation Method for Golang Compiler Testing. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2023, San Francisco, CA, USA, December 3-9, 2023 , Satish Chandra, Kelly Blincoe, and Paolo Tonella (Eds.). ACM, 2201–2203
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An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE Computer Society, 1162–1174
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Program Reconditioning: Avoiding Undefined Behaviour When Finding and Reducing Compiler Bugs
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HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models. In The 2023 Conference on Empirical Methods in Natural Language Processing
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Jia Le Tian, Mengxiao Zhang, Zhenyang Xu, Yongqiang Tian, Yiwen Dong, and Chengnian Sun. 2023d · 2023
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Revisiting the Evaluation of Deep Learning-Based Compiler Testing. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China . ijcai.org, 4873–4882
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On the Caching Schemes to Speed Up Program Reduction
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Compilation Consistency Modulo Debug Information. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (Vancouver, BC, Canada) (ASPLOS 2023) . Association for Computing Machinery, New York, NY, USA, 146–158
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Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2023, San Francisco, CA, USA, December 3-9, 2023 , Satish Chandra, Kelly Blincoe, and Paolo Tonella (Eds.). ACM, 172–184
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How Effective Are Neural Networks for Fixing Security Vulnerabilities. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (<conf-loc>, <city>Seattle</city>, <state>WA</state>, <country>USA</country>, </conf-loc>) (ISSTA 2023) . Association for Computing Machinery, New York, NY, USA, 1282–1294
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Revisiting the Plastic Surgery Hypothesis via Large Language Models
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Automated program repair in the era of large pre-trained language models. In Proceedings of the 45th International Conference on Software Engineering (ICSE 2023). Association for Computing Machinery
Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang. 2023b · 2023
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Pushing the Limit of 1-Minimality of Language-Agnostic Program Reduction
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PPR: Pairwise Program Reduction. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 338–349
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A study on robustness and reliability of large language model code generation
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Artifact for "LPR: Large Language Models-Aided Program Reduction"
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