# Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair arXiv: https://arxiv.org/abs/2601.19066 ยท HTML: https://mlsanity.com/paper/2601.19066/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 23 of its 37 references that are in the map. - First posted on arXiv 27 Jan 2026. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 23 of 23) - [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025) - [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021) - [SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents](https://mlsanity.com/paper/2406.12952/) (2024) - [Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction](https://mlsanity.com/paper/2209.11515/) (2022) - [Establishing Best Practices for Building Rigorous Agentic Benchmarks](https://mlsanity.com/paper/2507.02825/) (2025) - [Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling](https://mlsanity.com/paper/2507.23370/) (2025) - [Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories](https://mlsanity.com/paper/2506.18824/) (2025) - [SpecRover: Code Intent Extraction via LLMs](https://mlsanity.com/paper/2408.02232/) (2024) - [Are "Solved Issues" in SWE-bench Really Solved Correctly? An Empirical Study](https://mlsanity.com/paper/2503.15223/) (2025) - [UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench](https://mlsanity.com/paper/2506.09289/) (2025) - [Evaluating Agent-based Program Repair at Google](https://mlsanity.com/paper/2501.07531/) (2025) - [TDD-Bench Verified: Can LLMs Generate Tests for Issues Before They Get Resolved?](https://mlsanity.com/paper/2412.02883/) (2024) - [Agentic Bug Reproduction for Effective Automated Program Repair at Google](https://mlsanity.com/paper/2502.01821/) (2025) - [AssertFlip: Reproducing Bugs via Inversion of LLM-Generated Passing Tests](https://mlsanity.com/paper/2507.17542/) (2025) - [Heterogeneous Prompting and Execution Feedback for SWE Issue Test Generation and Selection](https://mlsanity.com/paper/2508.06365/) (2025) - [InfCode: Adversarial Iterative Refinement of Tests and Patches for Reliable Software Issue Resolution](https://mlsanity.com/paper/2511.16004/) (2025) - [Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback](https://mlsanity.com/paper/2507.18755/) (2025) - [Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based Testing](https://mlsanity.com/paper/2509.01616/) (2025) - [Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair](https://mlsanity.com/paper/2510.03217/) (2025) - [SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories](https://mlsanity.com/paper/2601.13713/) (2026) - [A Dissection of the Test-Driven Development Process: Does It Really Matter to Test-First or to Test-Last?](https://mlsanity.com/paper/1611.05994/) (2016) ## Built on by (most cited first, 6 of 6) - [Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes](https://mlsanity.com/paper/2607.19843/) (2026) - [ExecCritic: Learn to Test, Test to Improve for Coding Agents](https://mlsanity.com/paper/2609.09133/) (2026) - [Groundability, Not Scale Alone: When Weak Reviewers Can Audit Strong Coding Agents](https://mlsanity.com/paper/2610.01023/) (2026) - [Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?](https://mlsanity.com/paper/2607.28871/) (2026) - [Grounding AI Agents in Contracts: An Empirical Evaluation of Spec-Driven Test Generation](https://mlsanity.com/paper/2608.17177/) (2026) - [Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills](https://mlsanity.com/paper/2609.12012/) (2026) ## Abstract (arXiv) Bug Reproduction Tests (BRTs) have been used in many Automated Program Repair (APR) systems, primarily for validating fixes and aiding fix generation. In practice, when developers submit a patch, they often implement the BRT alongside the fix. Our experience deploying agentic APR reveals that developers desire a BRT within AI-generated patches to increase their confidence. However, canonical APR systems tend to generate BRTs and fixes separately, and focus on producing only the fix in the final patch. In this paper, we study agentic APR in the context of cogeneration, where the APR agent is instructed to generate both a fix and a BRT in the same patch. We evaluate the effectiveness of different cogeneration strategies on 120 human-reported bugs at Google and characterize different cogeneration strategies by their influence on APR agent behavior. We develop and evaluate patch selectors that account for test change to select patches with plausible fixes (and plausible BRTs). Finally, we analyze the root causes of failed cogeneration trajectories. We show that cogeneration allows the APR agent to generate BRTs for at least as many bugs as a dedicated BRT agent, without compromising the generation rate of plausible fixes, thereby reducing engineering effort in maintaining and coordinating separate generation pipelines for fix and BRT at scale.