arXiv subjects

Liang Xiao

Publications and source records attributed to Liang Xiao.

2 recordsLinked to original sources

arXiv2026

Self-Evolving Coding Agents

Large language models are increasingly embedded in software engineering workflows as coding agents that can inspect repositories, invoke tools, execute tests, debug failures, and generate patches. Yet most existing coding agents remain largely static after deployment, even though software development is a dynamic, feedback-rich process in which repositories evolve, dependencies change, tests fail, and repair attempts leave reusable experience. This tension has motivated a growing body of work on self-evolving coding agents, where the agent improves its future behavior by persistently updating its framework, memory, skills and tools, components, workflow and topology, or environment and context from prior coding interactions. In this survey, we provide a structured synthesis of this emerging area. We first define the concept of self-evolving coding agents and distinguish it from conventional coding agents and general self-evolving agents. We then develop a taxonomy centered on the targets of evolution, complemented by two orthogonal perspectives: when evolution occurs and which code-specific signals drive it. We further examine the benchmarks used to measure the effect of evolution and related coding products. Across the literature, we find that executable feedback, repository-level context, and coding trajectories make software engineering a natural domain for agent self-evolution, but also introduce challenges in feedback reliability, benchmark overfitting, reversibility, system complexity, safety, cost, and generalization. By organizing existing work around these dimensions, this survey aims to clarify the conceptual boundaries of self-evolving coding agents and provide a foundation for designing more adaptive, reliable, and software-aware agentic systems.

cs.SE↗

arXiv2026

EvoRepair: Enhancing Vulnerability Repair Agents Through Experience-Based Self-Evolution

Large Language Models (LLMs) have shown promise for automated vulnerability repair (AVR), but they still face several limitations, including the lack of intra-vulnerability experience accumulation and the lack of cross-vulnerability experience reuse. As a result, LLMs may repeatedly make similar mistakes during iterative repair and underutilize valuable repair knowledge from historical vulnerabilities. To address these challenges, we propose EvoRepair, the first experience-based self-evolving AVR agent framework that enables LLMs to accumulate, refine, and leverage domain-specific knowledge across long-horizon vulnerability repairs. EvoRepair follows a cyclic learn-and-repair process that retrieves relevant past experiences to guide repair, extracts new experiences from repair trajectories, and updates an experience bank using quality-aware scoring. We evaluate EvoRepair against 12 representative vulnerability repair baselines on PATCHEVAL and SEC-bench using GPT-5-mini. Results show that EvoRepair achieves the best overall performance, reaching 93.47% on PATCHEVAL, 87.00% on SEC-bench, and 90.46% overall. In particular, EvoRepair outperforms latest LLM-based baseline LoopRepair by 39.56% and 33.50% on PATCHEVAL and SEC-bench, respectively, and surpasses IntentFix by 70.86% and 50.50%. Across both benchmarks, EvoRepair also exceeds the recent self-evolving agent Live-SWE-Agent by 6.98% overall. Additional transfer experiments on VUL4J further demonstrate the robustness of EvoRepair across models, programming languages, and datasets. These findings demonstrate that experience-based self-evolution substantially strengthens agentic AVR and goes beyond existing self-evolving techniques.

cs.SE↗