Otter: Generating Tests from Issues to Validate SWE Patches
Toufique Ahmed, Jatin Ganhotra, Rangeet Pan, Avraham Shinnar, Saurabh Sinha, Martin Hirzel
Abstract
While there has been plenty of work on generating tests from existing code, there has been limited work on generating tests from issues. A correct test must validate the code patch that resolves the issue. This paper focuses on the scenario where that code patch does not yet exist. Doing so supports two major use-cases. First, it supports TDD (test-driven development), the discipline of "test first, write code later" that has well-documented benefits for human software engineers. Second, it also validates SWE (software engineering) agents, which generate code patches for resolving issues. This paper introduces TDD-Bench-Verified, a benchmark for generating tests from issues, and Otter, an LLM-based solution for this task. Otter augments LLMs with rule-based analysis to check and repair their outputs, and introduces a novel self-reflective action planner. Experiments show Otter outperforming state-ofthe-art systems for generating tests from issues, in addition to enhancing systems that generate patches from issues. We hope that Otter helps make developers more productive at resolving issues and leads to more robust, well-tested code.
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Cited by top-tier papers7
- Heterogeneous Prompting and Execution Feedback for SWE Issue Test Generation and SelectionToufique Ahmed, Jatin Ganhotra, Avraham Shinnar, Martin HirzelICSE 2026 · 2 citations
- Comprehend, Imitate, and then Update: Unleashing the Power of LLMs in Test Suite EvolutionTangzhi Xu, Jianhan Liu, Yuan Yao, Cong Li et al.ASE 2025 · 1 citation
- Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based TestingKonstantinos Kitsios, Marco Castelluccio, Alberto BacchelliASE 2025 · 1 citation
- Compiling Large Multi-modal Requirement Documents into Runnable Software Systems: From an Agentic Test-Driven PerspectiveWeiyu Kong, Yun Lin, Xiwen Teoh, Duc-Minh Nguyen et al.ISSTA 2026 · 1 citation
- iCoRe: An Iterative Correlation-Aware Retriever for Bug Reproduction Test GenerationJunyi Wang, Jialun Cao, Zhongxin LiuFSE 2026
Builds on6
- 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
- SWE-agent: Agent-Computer Interfaces Enable Automated Software EngineeringJohn Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret et al.NeurIPS 2024 · 2,059 citations
- 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
- CodeT: Code Generation with Generated TestsBei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan et al.ICLR 2023 · 64 citations
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