# REACCEPT: Automated Co-evolution of Production and Test Code Based on Dynamic Validation and Large Language Models

arXiv: https://arxiv.org/abs/2411.11033 · HTML: https://mlsanity.com/paper/2411.11033/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2024.

## Built on by (most cited first, 5 of 5)

- [CoCoEvo: Co-Evolution of Programs and Test Cases to Enhance Code Generation](https://mlsanity.com/paper/2502.10802/) (2025)
- [Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolution](https://mlsanity.com/paper/2605.06125/) (2026)
- [SPARC: Scenario Planning and Reasoning for Automated C Unit Test Generation](https://mlsanity.com/paper/2602.16671/) (2026)
- [MuMuTestUp: Mutation-based Multi-Agent Test Case Update](https://mlsanity.com/paper/2605.19265/) (2026)
- [An Exploration of Agentic Information Fusion for Test Maintenance Prediction](https://mlsanity.com/paper/2607.04786/) (2026)

## Abstract (arXiv)

Synchronizing production and test code, known as PT co-evolution, is critical for software quality in the software development lifecycle. Existing methods for automatic PT co-evolution either utilize predefined heuristic rules or rely on simple application of machine learning techniques. Due to the limitations of underlying techniques, existing methods either only partially automate PT co-evolution (e.g., only automate obsolete test code identification) or result in low accuracy. In this paper, we propose REACCEPT, a novel approach that leverages large language models and dynamic validation to fully automate PT co-evolution (i.e., capable of both identifying and updating obsolete test cases). REACCEPT relies on experience-based prompt template generation, dynamic validation, and retrieval-augmented generation techniques to accomplish automated PT co-evolution. To evaluate REACCEPT's effectiveness, we extensive experiments with a dataset of 537 Java projects and compared REACCEPT's performance with several state-of-the-art methods. Results show that REACCEPT achieved an update accuracy of 60.16% on correctly identified obsolete test code, surpassing the state-of-the-art technique CEPROT by 90%. This confirms that REACCEPT can effectively assist developers in maintaining test code, improving overall software quality and reducing maintenance effort.