Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair
- URL: http://arxiv.org/abs/2601.19066v1
- Date: Tue, 27 Jan 2026 01:02:57 GMT
- Title: Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair
- Abstract summary: Bug Reproduction Tests (BRTs) have been used in many agentic Automated Program Repair (APR) systems.<n>In practice, when developers submit a patch, they often implement the BRT alongside the fix.<n>Cogeneration allows the APR agent to generate BRTs for at least as many bugs as a dedicated BRT agent.
- Score: 4.994693185232122
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Bug Reproduction Tests (BRTs) have been used in many agentic Automated Program Repair (APR) systems, primarily for validating promising 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 similarly desire a BRT within AI-generated patches to increase their confidence. However, canonical APR systems tend to generate BRTs and fixes separately, or 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 information to select patches with plausible fixes (and plausible BRTs). Finally, we analyze the root causes of failed cogeneration trajectories. Importantly, 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.
Related papers
- ExecCritic: Learn to Test, Test to Improve for Coding Agents [68.42713285082912]
Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue.<n>We introduce ExecCritic, combining a test--verify--revise scaffold with a role-specific reinforcement learning recipe for training agents within it.<n>The scaffold separates test construction from source-code repair: a Test agent independently generates repository-native tests, a fail-closed harness qualifies and freezes them, and a Repair agent revises source code from their execution feedback without changing the tests.
arXiv Detail & Related papers (2026-09-08T17:53:37Z) - Independent Patch Verification for Coding Agents with a Bidirectional Reconstruct-and-Verify Framework [49.16055123488827]
coding agents powered by large language models can now generate code patches directly from bug reports.<n>No mechanism independently verifies whether it truly resolves the reported problem.<n>We propose RETRACE, a training-free post-generation verification framework.
arXiv Detail & Related papers (2026-08-09T22:59:13Z) - Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes [55.114939648705395]
Large language models (LLMs) have made automated program repair increasingly practical for real-world bugs.<n>Bug reproduction tests (BRTs) help close this gap by turning a bug report into an executable, bug-specific signal.<n>We propose CoHarden, a co-generation framework that uses the Lax signal as an in-loop convergence criterion.
arXiv Detail & Related papers (2026-07-22T07:30:07Z) - SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests [18.192718828200782]
We propose SWE-Doctor, a software issue resolution agent that guides patch generation with runtime diagnoses derived from multi-faceted BRT executions.<n>SWE-Doctor consistently outperforms existing agents across all 10 LLM-benchmark combinations.
arXiv Detail & Related papers (2026-07-01T14:27:12Z) - Dynamic analysis enhances issue resolution [53.50448142467294]
DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
arXiv Detail & Related papers (2026-03-23T14:48:54Z) - DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems [48.971606069204825]
DoVer is an intervention-driven debug framework for large language model (LLM)-based multi-agent systems.<n>It augments hypothesis generation with active verification through targeted interventions.<n>DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses.
arXiv Detail & Related papers (2025-12-07T09:23:48Z) - Alita-G: Self-Evolving Generative Agent for Agent Generation [54.49365835457433]
We present ALITA-G, a framework that transforms a general-purpose agent into a domain expert.<n>In this framework, a generalist agent executes a curated suite of target-domain tasks.<n>It attains strong gains while reducing computation costs.
arXiv Detail & Related papers (2025-10-27T17:59:14Z) - REFINE: Enhancing Program Repair Agents through Context-Aware Patch Refinement [12.995571513415905]
Large Language Models (LLMs) have recently shown strong potential in automatic program repair (APR)<n>LLMs often struggle to produce correct fixes due to limited understanding of code context and over-reliance on incomplete test suites.<n>We propose a novel patch refinement framework, Refine, that systematically transforms Draft Patches into correct ones.
arXiv Detail & Related papers (2025-10-04T00:34:32Z) - Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair [7.118712516789191]
Agentic Automated Program Repair (APR) is increasingly tackling complex, repository-level bugs in industry.<n>Showing unlikely patches to developers can lead to substantial noise.<n>We introduce two complementary policies to reduce such noise: bug abstention and patch validation policies.
arXiv Detail & Related papers (2025-10-03T17:53:28Z) - Where LLM Agents Fail and How They can Learn From Failures [62.196870049524364]
Large Language Model (LLM) agents have shown promise in solving complex, multi-step tasks.<n>They amplify vulnerability to cascading failures, where a single root-cause error propagates through subsequent decisions.<n>Current systems lack a framework that can comprehensively understand agent error in a modular and systemic way.<n>We introduce the AgentErrorTaxonomy, a modular classification of failure modes spanning memory, reflection, planning, action, and system-level operations.
arXiv Detail & Related papers (2025-09-29T18:20:27Z) - Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving [62.71545696485824]
We introduce AGENT KB, a universal memory infrastructure enabling seamless experience sharing across heterogeneous agent frameworks without retraining.<n>AGENT KB aggregates trajectories into a structured knowledge base and serves lightweight APIs.<n>We validate AGENT across major frameworks on GAIA, Humanity's Last Exam, GPQA, and SWE-bench.
arXiv Detail & Related papers (2025-07-08T17:59:22Z) - Adversarial Reasoning for Repair Based on Inferred Program Intent [12.923634025700826]
We propose an approach called AdverIntent-Agent, based on critique and adversarial reasoning.<n>Our approach is novel to shift the focus from generating multiple APR patches to inferring multiple potential program intents.<n> AdverIntent-Agent was evaluated on two benchmarks: Defects4J 2.0 and HumanEval-Java.
arXiv Detail & Related papers (2025-05-19T11:51:56Z) - Agentic Bug Reproduction for Effective Automated Program Repair at Google [9.64193881099048]
This paper investigates automated BRT generation within an industry setting, specifically at Google.<n>We adapt and evaluate a state-of-the-art BRT generation technique, LIBRO, and present our agent-based approach, BRT Agent.<n>Our results show that providing BRTs to the APR system results in 30% more bugs with plausible fixes.
arXiv Detail & Related papers (2025-02-03T20:57:17Z) - RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic
Program Repair [75.40584530380589]
We propose a novel Retrieval-Augmented Patch Generation framework (RAP-Gen)
RAP-Gen explicitly leveraging relevant fix patterns retrieved from a list of previous bug-fix pairs.
We evaluate RAP-Gen on three benchmarks in two programming languages, including the TFix benchmark in JavaScript, and Code Refinement and Defects4J benchmarks in Java.
arXiv Detail & Related papers (2023-09-12T08:52:56Z) - Can Agents Run Relay Race with Strangers? Generalization of RL to
Out-of-Distribution Trajectories [88.08381083207449]
We show the prevalence of emphgeneralization failure on controllable states from stranger agents.
We propose a novel method called Self-Trajectory Augmentation (STA), which will reset the environment to the agent's old states according to the Q function during training.
arXiv Detail & Related papers (2023-04-26T10:12:12Z) - Improving Automated Program Repair with Domain Adaptation [0.0]
Automated Program Repair (APR) is defined as the process of fixing a bug/defect in the source code, by an automated tool.
APR tools have recently experienced promising results by leveraging state-of-the-art Neural Language Processing (NLP) techniques.
arXiv Detail & Related papers (2022-12-21T23:52:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.