UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification

• URL: http://arxiv.org/abs/2510.05441v1
• Date: Mon, 06 Oct 2025 23:08:23 GMT
• Title: UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification
• Authors: Yiannis Charalambous, Claudionor N. Coelho Jr, Luis Lamb, Lucas C. Cordeiro,
• Abstract summary: UnitTenX is a state-of-the-art open-source AI multi-agent system designed to generate unit tests for legacy code.<n>Our results demonstrate the effectiveness of this approach in generating high-quality tests and identifying potential issues.
• Score: 1.6592567365278768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces UnitTenX, a state-of-the-art open-source AI multi-agent system designed to generate unit tests for legacy code, enhancing test coverage and critical value testing. UnitTenX leverages a combination of AI agents, formal methods, and Large Language Models (LLMs) to automate test generation, addressing the challenges posed by complex and legacy codebases. Despite the limitations of LLMs in bug detection, UnitTenX offers a robust framework for improving software reliability and maintainability. Our results demonstrate the effectiveness of this approach in generating high-quality tests and identifying potential issues. Additionally, our approach enhances the readability and documentation of legacy code.

Related papers

• Enhancing LLM-Based Test Generation by Eliminating Covered Code [2.2566909388480743]
  Large Language Models (LLMs) have shown promise in improving test generation.<n>We propose a scalable LLM-based unit test generation method.<n>Our approach outperforms state-of-the-art LLM-based and search-based methods.
  arXiv  Detail & Related papers  (2026-02-25T15:16:43Z)
• BOSQTGEN: Breaking the Sound Barrier in Test Generation [3.052470294814771]
  We introduce BOSQTGEN, a novel black-box and tool for API test generation.<n> BOSQTGEN utilizes a novel approach for decomposing API specifications into primitives, using LLMs to suggest coherent interactions for them, and employing testing to efficiently sample over these values.<n>The resulting BOSQTGEN system achieves an average of 82% of critical code coverage on benchmarks, often a 20% or more increase over prior state-of-the-art systems.
  arXiv  Detail & Related papers  (2025-10-22T17:11:30Z)
• ATGen: Adversarial Reinforcement Learning for Test Case Generation [78.48498301767079]
  Large Language Models (LLMs) excel at code generation, yet their outputs often contain subtle bugs.<n>Existing test generation methods rely on static datasets.<n>We introduce ATGen, a framework that trains a test case generator via adversarial reinforcement learning.
  arXiv  Detail & Related papers  (2025-10-16T12:49:25Z)
• Learning to Generate Unit Test via Adversarial Reinforcement Learning [33.82915303652549]
  Unit testing is a core practice in programming, enabling systematic evaluation of programs produced by human developers or large language models (LLMs)<n>We propose UTRL, a novel reinforcement learning framework that trains an LLM to generate high-quality unit tests given a programming instruction.<n>In our experiments, we demonstrate that unit tests generated by Qwen3-4B trained via UTRL show higher quality compared to unit tests generated by the same model trained via supervised fine-tuning.
  arXiv  Detail & Related papers  (2025-08-28T14:32:44Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
  Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
  arXiv  Detail & Related papers  (2025-05-28T17:57:47Z)
• TestForge: Feedback-Driven, Agentic Test Suite Generation [7.288137795439405]
  TestForge is an agentic unit testing framework designed to cost-effectively generate high-quality test suites for real-world code.<n>TestForge produces more natural and understandable tests compared to state-of-the-art search-based techniques.
  arXiv  Detail & Related papers  (2025-03-18T20:21:44Z)
• Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation [69.62857948698436]
  Recent advances in large language models (LLMs) have improved their performance on coding benchmarks.<n>However, improvement is plateauing due to the exhaustion of readily available high-quality data.<n>We propose Sol-Ver, a self-play solver-verifier framework that jointly improves a single model's code and test generation capacity.
  arXiv  Detail & Related papers  (2025-02-20T18:32:19Z)
• Commit0: Library Generation from Scratch [77.38414688148006]
  Commit0 is a benchmark that challenges AI agents to write libraries from scratch.<n>Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests.<n> Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate.
  arXiv  Detail & Related papers  (2024-12-02T18:11:30Z)
• CodeRAG-Bench: Can Retrieval Augment Code Generation? [78.37076502395699]
  We conduct a systematic, large-scale analysis of code generation using retrieval-augmented generation.<n>We first curate a comprehensive evaluation benchmark, CodeRAG-Bench, encompassing three categories of code generation tasks.<n>We examine top-performing models on CodeRAG-Bench by providing contexts retrieved from one or multiple sources.
  arXiv  Detail & Related papers  (2024-06-20T16:59:52Z)
• SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents [10.730852617039451]
  We investigate the capability of LLM-based Code Agents to formalize user issues into test cases.<n>We propose a novel benchmark based on popular GitHub repositories, containing real-world issues, ground-truth bug-fixes, and golden tests.<n>We find that LLMs generally perform surprisingly well at generating relevant test cases, with Code Agents designed for code repair exceeding the performance of systems designed for test generation.
  arXiv  Detail & Related papers  (2024-06-18T14:54:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.