Heterogeneous Prompting and Execution Feedback for SWE Issue Test Generation and Selection
Toufique Ahmed, Jatin Ganhotra, Avraham Shinnar, Martin Hirzel
Abstract
A software engineering issue (SWE issue) is easier to resolve when accompanied by a reproduction test. Unfortunately, most issues do not come with functioning reproduction tests, so this paper explores how to generate them automatically. One technique that helps with this task is inference scaling, but traditional temperature-based scaling tends to lack diversity. This paper introduces heterogeneous prompting to address this problem. Another technique that helps with this task is execution feedback, but this is hampered by the fact that the new code (after issue resolution) to execute does not yet exist. This paper introduces novel approaches to get around that problem. We implemented our techniques in a new reproduction test generator called e-Otter++. Experiments show that e-Otter++ represents a leap ahead in the state-of-the-art for this problem, generating tests with an average fail-to-pass rate of 63% on the TDD-Bench Verified benchmark.
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Cited by top-tier papers2
- iCoRe: An Iterative Correlation-Aware Retriever for Bug Reproduction Test GenerationJunyi Wang, Jialun Cao, Zhongxin LiuFSE 2026
- Can Old Tests Do New Tricks for Resolving SWE Issues?Yang Chen, Toufique Ahmed, Reyhaneh Jabbarvand, Martin HirzelFSE 2026
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- SWE-bench: Can Language Models Resolve Real-world Github Issues?Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao et al.ICLR 2024 · 2,082 citations
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- SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code AgentsNiels Mündler, Mark Niklas Müller, Jingxuan He, Martin T. VechevNeurIPS 2024 · 172 citations
- Large Language Models are Few-shot Testers: Exploring LLM-based General Bug ReproductionSungmin Kang, Juyeon Yoon, Shin YooICSE 2023 · 163 citations
- AutoCodeRover: Autonomous Program ImprovementYuntong Zhang, Haifeng Ruan, Zhiyu Fan, Abhik RoychoudhuryISSTA 2024 · 96 citations
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