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

arXiv: https://arxiv.org/abs/2606.25588 · HTML: https://mlsanity.com/paper/2606.25588/

## 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.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 14 of 14)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Structured Chain-of-Thought Prompting for Code Generation](https://mlsanity.com/paper/2305.06599/) (2023)
- [ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation](https://mlsanity.com/paper/2307.00588/) (2023)
- [CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models](https://mlsanity.com/paper/2302.00288/) (2023)
- [Characterizing Python Library Migrations](https://mlsanity.com/paper/2207.01124/) (2022)
- [Automated Unit Test Refactoring](https://mlsanity.com/paper/2409.16739/) (2024)
- [FrUITeR: A Framework for Evaluating UI Test Reuse](https://mlsanity.com/paper/2008.03427/) (2020)
- [How Developers Engineer Test Cases: An Observational Study](https://mlsanity.com/paper/2103.01783/) (2021)
- [Automated Test Transfer Across Android Apps Using Large Language Models](https://mlsanity.com/paper/2411.17933/) (2024)
- [Test Reuse Based on Adaptive Semantic Matching across Android Mobile Applications](https://mlsanity.com/paper/2301.00530/) (2023)
- [Every Maintenance Has Its Exemplar: The Future of Software Maintenance through Migration](https://mlsanity.com/paper/2602.14046/) (2026)

## 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.