mlsanityData to 7 Oct 2026

Otter: Generating Tests from Issues to Validate SWE Patches

arXiv 2502.05368 ↗First posted 7 Feb 2025cs.SE cs.LGcited by 24

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Abstract (arXiv)

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-of-the-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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 7 Feb 2025.

Builds on

16 of 21 references.

References · 16

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  3. 2023Instruction Tuning with GPT-4cited by 676
  4. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  5. 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 400
  6. 2022CodeT: Code Generation with Generated Testscited by 369
  7. 2023An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generationcited by 193
  8. 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agentscited by 129
  9. 2022Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproductioncited by 110
  10. 2024SWE-Bench+: Enhanced Coding Benchmark for LLMscited by 95
  11. 2024CodeR: Issue Resolving with Multi-Agent and Task Graphscited by 89
  12. 2025CodeMonkeys: Scaling Test-Time Compute for Software Engineeringcited by 46
4 more
  1. 2024TDD-Bench Verified: Can LLMs Generate Tests for Issues Before They Get Resolved?cited by 32
  2. 2024AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptionscited by 15
  3. 2024LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issuescited by 10
  4. 2023Automatic Generation of Test Cases based on Bug Reports: a Feasibility Study with Large Language Modelscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 18.

24 papers.

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  1. 2025SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugscited by 22
  2. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  3. 2026Scaling Test-Time Compute for Agentic Codingcited by 11
  4. 2025Agentic Software Issue Resolution with Large Language Models: A Surveycited by 6
  5. 2026Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agentscited by 6
  6. 2025Code Reasoning for Software Engineering Tasks: A Survey and A Call to Actioncited by 4
  7. 2025PAGENT: Learning to Patch Software Engineering Agentscited by 4
  8. 2026SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositoriescited by 4
  9. 2026AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detectioncited by 4
  10. 2026SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Testscited by 4
  11. 2026Execution-State-Aware LLM Reasoning for Automated Proof-of-Vulnerability Generationcited by 3
  12. 2026CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generationcited by 2
12 more
  1. 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixescited by 2
  2. 2025AutoCodeSherpa: Symbolic Explanations in AI Coding Agentscited by 1
  3. 2026Compiling Large Multi-Modal Requirement Documents into Runnable Software Systems: From an Agentic Test-Driven Perspectivecited by 1
  4. 2026Program Analysis Guided LLM Agent for Proof-of-Concept Generationcited by 1
  5. 2026Unlocking Model Potentials Through Adaptive Multi-Agent Scaffolding for Efficient Issue Resolutioncited by 1
  6. 2026EvoOtter: Evolutionary Reproduction Test Generatorcited by 1
  7. 2026ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reportscited by 1
  8. 2026SWE-Spot: Building Small Repo-Experts with Repository-Centric Learningcited by 0
  9. 2026Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?cited by 0
  10. 2026DPIAgent: Divide, Protocol, Isolate for Agentic Reproduction Test Generationcited by 0
  11. 2026ExecCritic: Learn to Test, Test to Improve for Coding Agentscited by 0
  12. 2026TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolutioncited by 0