arXiv AI
CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?
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arXiv AI
Jun 9
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios
arXiv:2509. 22097v5 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern.
arXiv AI
Oct 1
E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch
E2E-SWE is a benchmark that tests large language models’ ability to create complete, functional software repositories from scratch. It includes 186 tasks across 11 programming languages, each requiring an agent to build an installable project based solely on a natural‑language specification and an empty workspace, while passing a hidden test suite. The benchmark was crafted by software engineers and LLMs, then refined through iterative verification by autonomous agents to ensure clarity and solvability.
arXiv AI
Oct 27
Can Agentic Code Review Catch Vulnerabilities Before They Are Merged?
The paper introduces SecPRBench, a benchmark that evaluates large language model agents on real-world bug‑introducing commits from critical software systems such as Firefox, V8, and OpenSSL. It automates harvesting, building, and validating these commits, and uses an LLM‑as‑a‑judge to match findings with ground‑truth vulnerabilities. Across eight state‑of‑the‑art models, recall ranges from 32.5% to 78.1%, with Grok 4.6 and GPT‑6 sol performing best, while security‑focused harnesses like OpenAI’s Codex Security and Anthropic’s Defending Code influence recall differently.
arXiv AI
Aug 6
RepoProbe: Benchmarking Architecture-Aware Repository Comprehension with Checklists
arXiv:2608. 04783v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into software engineering has shifted the focus from function-level generation to repository-scale assistance.
arXiv Machine Learning
Jul 30
REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage
arXiv:2604. 01527v4 Announce Type: replace-cross Abstract: Production deployment of AI coding agents requires fast, reproducible evaluation signals.
arXiv AI
Jul 3
ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair
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.