mlsanityData to 14 Oct 2026

IntentTester: Intent-Driven Multi-agent Framework for Cross-Library Test Migration

arXiv 2606.25588 ↗First posted 24 Jun 2026cs.SEcited by 0

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

Abstract (arXiv)

Unit tests capture both functional checks and domain-specific knowledge, but this knowledge remains locked within individual projects and is rarely reused across libraries with overlapping functionality. Existing migration techniques based on structural code mappings (e.g., API signatures) often break down under divergent designs or cross-language settings, resulting in non-executable migrated tests. In this paper, we present IntentTester, a multi-agent framework for intent-driven test reuse. Instead of translating raw code, IntentTester abstracts tests into a language-agnostic Test Description Language (TDL), aligns them with semantically related entities and dependencies in a repository graph, and synthesizes executable tests through LLM-guided reasoning and iterative validation. This design enables cross-library and cross-language migration without manual intervention, producing migrated tests that existing structure-mapping approaches cannot achieve. We evaluate IntentTester on nine open-source projects across three domains (JSON, HTML, and Time) and two languages (Java and Python). IntentTester generates 2,776 syntactically correct tests with 85% correctness; in comparison, the two baselines achieve 51% and 43%. Among them, 2,410 tests executed successfully, yielding a 74% effectiveness rate. Beyond higher success rates, IntentTester also surfaced previously unknown defects including stack overflows, null dereferences, and parsing inconsistencies, several of which have been acknowledged or patched by maintainers. Our results show that intent-driven migration shifts the focus from code mappings to semantic alignment, allowing practical cross-library and cross-language test reuse while improving test quality and exposing implementation flaws.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 42 references that are in the map. First posted on arXiv 24 Jun 2026.

Builds on

14 of 42 references.

References · 14

  1. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  2. 2024AutoCodeRover: Autonomous Program Improvementcited by 278
  3. 2023An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generationcited by 194
  4. 2023Software Testing with Large Language Models: Survey, Landscape, and Visioncited by 166
  5. 2023Structured Chain-of-Thought Prompting for Code Generationcited by 138
  6. 2023ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generationcited by 32
  7. 2023CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Modelscited by 28
  8. 2022Characterizing Python Library Migrationscited by 4
  9. 2024Automated Unit Test Refactoringcited by 4
  10. 2020FrUITeR: A Framework for Evaluating UI Test Reusecited by 3
  11. 2021How Developers Engineer Test Cases: An Observational Studycited by 3
  12. 2024Automated Test Transfer Across Android Apps Using Large Language Modelscited by 3
2 more
  1. 2023Test Reuse Based on Adaptive Semantic Matching across Android Mobile Applicationscited by 2
  2. 2026Every Maintenance Has Its Exemplar: The Future of Software Maintenance through Migrationcited by 1

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  2. 2026Sakura: An Approach for Generating Complex Tests from Natural Language Test Descriptions ↗
  3. 2025TENET: One Step Toward Test-Driven Development for Repository-Level Code Generation
  4. 2026TDD-Agent: Test-Driven Reasoning for Code Generation
  5. 2026WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing
  6. 2026Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills
  7. 2026MuMuTestUp: Mutation-based Multi-Agent Test Case Update
  8. 2025TAI3: Testing Agent Integrity in Interpreting User Intent