Echo: Graph-Enhanced Retrieval and Execution Feedback for Issue Reproduction Test Generation
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Abstract (arXiv)
Identifying the root cause of a bug remains difficult for many developers because bug reports often lack a bug reproducing test case that reliably triggers the failure. Manually writing such test cases is time-consuming and requires substantial effort to understand the codebase and isolate the failing behavior. To address this challenge, we propose Echo, an agent for generating issue reproducing test cases, which advances previous work in several ways. During generation, Echo strengthens context retrieval by leveraging a code graph and a novel automatic query-refinement strategy. Echo also improves upon previous tools by automatically executing generated test cases, a first-of-its-kind feature that seamlessly integrates into practical development workflows. In addition, Echo generates potential patches and uses the patched version to validate whether a candidate test meets the fail-to-pass criterion and to provide actionable feedback for refinement. Unlike prior bug-reproduction agents that sample and rank multiple candidate tests, Echo generates a single test per issue, offering a better cost-performance trade-off. Experiments on SWT-Bench Verified show that Echo establishes a new state of the art among open-source approaches, achieving a 66.28% success rate.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2026.
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Citing papers per year: 2026: 4.
4 papers.
- 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixescited by 2
- 2026ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reportscited by 1
- 2026Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?cited by 0
- 2026DPIAgent: Divide, Protocol, Isolate for Agentic Reproduction Test Generationcited by 0
- 2026DPIAgent: Divide, Protocol, Isolate for Agentic Reproduction Test Generation24 Aug 2026
- 2026Validation Evidence in LLM Repair Agents: How Much of What Passes Actually Tests the Bug?30 Jul 2026
- 2026Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes22 Jul 2026
- 2026ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reports10 Jul 2026