mlsanityData to 7 Oct 2026

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

arXiv 2510.05441 ↗First posted 6 Oct 2025cs.SE cs.AIcited by 1

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 35 references that are in the map. First posted on arXiv 6 Oct 2025.

Builds on

13 of 35 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2024Agent AI: Surveying the Horizons of Multimodal Interactioncited by 167
  3. 2025SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?cited by 99
  4. 2024Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLMcited by 63
  5. 2024CoverUp: Effective High Coverage Test Generation for Pythoncited by 36
  6. 2022Code Red: The Business Impact of Code Quality -- A Quantitative Study of 39 Proprietary Production Codebasescited by 8
  7. 2025Evaluating LLM-Based Regression Test Generationcited by 6
  8. 2019The Evolution of Empirical Methods in Software Engineeringcited by 3
  9. 2021Model Checking C++ Programscited by 3
  10. 2022A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learningcited by 2
  11. 2023Back to the Future! Studying Data Cleanness in Defects4J and its Impact on Fault Localizationcited by 2
  12. 2020TracerX: Dynamic Symbolic Execution with Interpolationcited by 1
1 more
  1. 2023OrdinalFix: Fixing Compilation Errors via Shortest-Path CFL Reachabilitycited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Partial Contracts Suffice: Sound, LLM-Inferred Regression Verificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
  2. 2026Grounding AI Agents in Contracts: An Empirical Evaluation of Spec-Driven Test Generation
  3. 2026Agent-Based Test Assertion Generation via Diverse Perspective Aggregation
  4. 2026Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows
  5. 2026VibeCheck: Assessing the Quality of LLM-Generated Unit Tests: A Multi-agent Empirical Study across Heterogeneous Repositories
  6. 2026Beyond Test Presence: Assessing the Quality and Robustness of Agent-Generated Tests in Open-Source Projects
  7. 2026AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection
  8. 2024An Empirical Study on LLM-based Agents for Automated Bug Fixing