# An Exploration of Agentic Information Fusion for Test Maintenance Prediction

arXiv: https://arxiv.org/abs/2607.04786 · HTML: https://mlsanity.com/paper/2607.04786/

## 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 13 of its 45 references that are in the map.
- First posted on arXiv 6 Jul 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, 13 of 13)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [A Large-Scale Study on the Usage of Testing Patterns that Address Maintainability Attributes (Patterns for Ease of Modification, Diagnoses, and Comprehension)](https://mlsanity.com/paper/1704.08412/) (2017)
- [REACCEPT: Automated Co-evolution of Production and Test Code Based on Dynamic Validation and Large Language Models](https://mlsanity.com/paper/2411.11033/) (2024)
- [A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat](https://mlsanity.com/paper/2507.18515/) (2025)
- [Maintenance of Automated Test Suites in Industry: An Empirical study on Visual GUI Testing](https://mlsanity.com/paper/1602.01226/) (2016)
- [Graph-Grounded LLMs: Leveraging Graphical Function Calling to Minimize LLM Hallucinations](https://mlsanity.com/paper/2503.10941/) (2025)
- [A Systematic Literature Review of Test Breakage Prevention and Repair Techniques](https://mlsanity.com/paper/1909.10750/) (2019)
- [Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes](https://mlsanity.com/paper/2409.06416/) (2024)
- [SVA-ICL: Improving LLM-based Software Vulnerability Assessment via In-Context Learning and Information Fusion](https://mlsanity.com/paper/2505.10008/) (2025)
- [MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval](https://mlsanity.com/paper/2604.09552/) (2026)

## Abstract (arXiv)

Test maintenance is a critical, yet costly, activity - particularly as codebases rapidly evolve. To assist, we present MAST, a multi-agent framework that predicts which test cases require maintenance following changes to the production code. This identification task is necessary as a precondition to any subsequent maintenance activities, but remains challenging due to the complex relationships between production and test code. MAST advances the state-of-the-art by integrating multiple analyses -- including static, lexical, and semantic analyses - through an intelligent fusion and post-check procedure and by focusing on a realistic use and evaluation setting - i.e., standardized input formats, repository-level analyses, and the ability to infer relations between test and production artifacts rather than assuming a pre-existing mapping. We evaluated MAST on 21 industrial Java repositories from Ericsson AB, considering situations where test maintenance both was and was not required in the ground truth. MAST yielded superior precision to a state-of-the-art baseline - resulting in a higher accuracy, F1, and F2 score - with only some loss in recall. Our ablation study demonstrates the value of each analysis in producing the final recommendations. MAST illustrates the potential of multi-agent systems that can fuse multiple information sources when performing software testing tasks.