arXiv:2605. 17561v2 Announce Type: replace-cross Abstract: Issues faced when using software are reported in the form of bug reports.
arXiv:2604. 11950v2 Announce Type: replace-cross Abstract: While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation, limiting the practicality of automated bug detection.
arXiv:2607. 00990v1 Announce Type: cross Abstract: Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories.
arXiv:2607. 28587v2 Announce Type: replace-cross Abstract: SWE-bench-like benchmarks are widely used for evaluating LLM's issue resolution capability.
arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.
arXiv:2605. 17965v2 Announce Type: replace-cross Abstract: Bug localization remains a key bottleneck for large language model (LLM)-based software maintenance, where accurately identifying faulty code is essential for debugging, root cause analysis, triage, and automated program repair (APR).
The paper presents a systematic analysis of five state‑of‑the‑art automated program repair agents, tracing their decision‑making across 500 real‑world repair tasks. It finds that while the agents perform well on simple fixes, they struggle with logic‑intensive bugs, often producing verbose, overfitted patches that pass tests without addressing root causes. Key bottlenecks identified include poor test generation, limited regression test selection, and reliance on primitive tooling without access to debuggers or advanced program analysis tools.
arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.
arXiv:2601.19066v3 Announce Type: replace-cross Abstract: Bug Reproduction Tests (BRTs) have been used in many Automated Program Repair (APR) systems, primarily for validating fixes and aiding fix ge...
ExecRetrieval is a new benchmark for code‑embedding retrieval that contains 939 Python tasks, each with a verified correct implementation and up to four single‑edit buggy distractors generated mechanically. The dataset allows direct testing of a retriever’s ability to functionally discriminate correct code from near‑clone incorrect code, rather than relying on lexical similarity. Experiments on 23 dense embeddings and BM25 show that while the best system can retrieve the correct code within the top 10 results, it often fails to rank the correct implementation first, with rank‑1 errors dominated by paired buggy variants.
PatchBench introduces a benchmark to evaluate AI agents on realistic vulnerability patching tasks, addressing two key threats to validity: patch memorization and surface-level fixes that merely suppress crashes. The study finds that 25% of agent patches resemble historical developer patches, and that PoC-only validation inflates success rates by 1.83× on average. PatchBench mitigates these issues by selecting vulnerabilities whose true fixes lie outside the crash stack, migrating historical vulnerabilities into new contexts, and employing rigorous validation for security and semantic correctness.
arXiv:2607. 25873v1 Announce Type: cross Abstract: Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent.
arXiv:2605. 17450v2 Announce Type: replace-cross Abstract: As software systems grow increasingly complex, automated vulnerability repair (AVR) remains difficult because the materials available to a repair system are usually failure artifacts rather than repair guidance.