| name | agentic-information-fusion-test-maintenance |
| description | Multi-agent framework (MAST) for predicting which test cases require maintenance after production code changes. Uses agentic information fusion across code diffs, test history, and semantic analysis to identify tests needing updates. Activation: test maintenance, multi-agent testing, agentic information fusion, MAST, test prediction, code evolution. |
| metadata | {"arxiv_id":"2607.04786","published":"2026-07-06","authors":"Jingxiong Liu, Nasser Mohammadiha, Gregory Gay","tags":["test-maintenance","multi-agent","information-fusion","software-engineering","test-prediction","mast"]} |
Agentic Information Fusion for Test Maintenance Prediction
Overview
Test maintenance is a critical, yet costly, activity — particularly as codebases rapidly evolve. This paper presents 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 automating test maintenance itself.
Key Problem
Test Maintenance Cost
- As codebases evolve, test suites must be updated to reflect production code changes
- Manually identifying which tests need maintenance is time-consuming and error-prone
- Test maintenance is a significant portion of software engineering effort
- Automated prediction of maintenance-needing tests is a prerequisite for automation
Key Innovations
Multi-Agent Framework (MAST)
- Multiple specialized agents each focus on different aspects of the code-test relationship
- Agents analyze code diffs, test history, semantic relationships, and dependency graphs
- Information fusion across agents produces a unified maintenance prediction
Agentic Information Fusion
- Combines signals from multiple analysis perspectives (syntactic, semantic, historical)
- Each agent contributes a different lens on whether a test needs maintenance
- Fusion mechanism aggregates agent outputs into a final prediction
Methodology
- Code Change Analysis: Agents analyze production code diffs to understand impact
- Test Dependency Mapping: Map which tests depend on changed code elements
- Historical Pattern Analysis: Use test maintenance history to inform predictions
- Semantic Analysis: Agents reason about semantic relationships between code and tests
- Fusion: Combine agent outputs to predict which tests need maintenance
Implications
- Multi-agent approach to software engineering tasks beyond code generation
- Information fusion as a paradigm for combining diverse analysis perspectives
- Practical tool for reducing test maintenance burden in large codebases
- Demonstrates agentic AI for DevOps and quality assurance workflows
Pitfalls
- Prediction accuracy depends on the quality of code-test dependency mapping
- Historical patterns may not predict novel types of changes
- Multi-agent fusion adds complexity that may not always improve over single-agent approaches
- Evaluation on diverse codebases needed to validate generalization
Activation Keywords
test maintenance, multi-agent testing, agentic information fusion, MAST, test prediction, code evolution, software engineering agents, test suite maintenance, DevOps agents
Paper Reference
arXiv:2607.04786 - "An Exploration of Agentic Information Fusion for Test Maintenance Prediction" (Jul 2026)
