{"total":16,"rows":[{"id":"2405.18649","title":"LeDex: Training LLMs to Better Self-Debug and Explain Code","created":"2024-05-28","cited_by_count":40,"area":null},{"id":"2406.07737","title":"The Future of AI-Driven Software Engineering","created":"2024-06-11","cited_by_count":23,"area":null},{"id":"2502.10802","title":"CoCoEvo: Co-Evolution of Programs and Test Cases to Enhance Code Generation","created":"2025-02-15","cited_by_count":14,"area":null},{"id":"2603.15611","title":"Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning","created":"2026-03-16","cited_by_count":8,"area":null},{"id":"2411.01789","title":"Generating executable oracles to check conformance of client code to requirements of JDK Javadocs using LLMs","created":"2024-11-04","cited_by_count":6,"area":null},{"id":"2510.12702","title":"Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?","created":"2025-10-14","cited_by_count":6,"area":null},{"id":"2506.14640","title":"Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey","created":"2025-06-17","cited_by_count":2,"area":null},{"id":"2509.25043","title":"Large Language Models for Software Testing: A Research Roadmap","created":"2025-09-29","cited_by_count":2,"area":null},{"id":"2601.05542","title":"Understanding LLM-Driven Test Oracle Generation","created":"2026-01-09","cited_by_count":2,"area":null},{"id":"2608.01000","title":"Judging Is Not Enumerating: Silent Omissions in LLM-Authored Acceptable Sets","created":"2026-08-02","cited_by_count":2,"area":null},{"id":"2504.16472","title":"Harden and Catch for Just-in-Time Assured LLM-Based Software Testing: Open Research Challenges","created":"2025-04-23","cited_by_count":1,"area":null},{"id":"2601.22832","title":"Just-in-Time Catching Test Generation at Meta","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2605.29822","title":"Inferring Code Correctness from Specification","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2607.05032","title":"Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2607.11342","title":"Fail-Aware and Explainable Test Oracle Prediction","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.22883","title":"Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests","created":"2026-07-24","cited_by_count":0,"area":null}],"next_cursor":null}