---
title: "Multi-Agent Change Impact Analysis and Test Optimization for AI-Enabled Software Systems"
description: "An overview of multi-agent change impact analysis and risk-aware test optimization for complex AI-enabled software systems."
author: "Nariman Mani"
date: "August 6, 2026"
canonical: "https://manistechmind.com/posts/multi-agent-change-impact-analysis-test-optimization"
keywords: "multi-agent systems, change impact analysis, test optimization, AI-enabled software, regression testing, AITest 2026"
---

> Machine-readable edition. The canonical article is https://manistechmind.com/posts/multi-agent-change-impact-analysis-test-optimization.

# Multi-Agent Change Impact Analysis and Test Optimization for AI-Enabled Software Systems

# Multi-Agent Change Impact Analysis and Test Optimization for AI-Enabled Software Systems

## Publication at a glance

- **Paper:** *Multi-Agent Change Impact Analysis and Test Optimization for AI-Enabled Software Systems*
- **Authors:** Nariman Mani, Amr S. Abdelfattah, Shakthi Weerasinghe, Xiaozhou Li, and Tomas Cerny
- **Status:** Accepted for publication
- **Venue:** 8th IEEE International Conference on Artificial Intelligence Testing (IEEE AITest 2026), part of the IEEE CISOSE 2026 Congress
- **Location and dates:** Fukuoka, Japan, July 27–30, 2026
- **Conference:** [IEEE AITest 2026](https://cisose.fit.ac.jp/aitest/)

## The research problem

AI-enabled systems combine application code, models, data pipelines, prompts, services, and infrastructure. A change in any one of these elements can affect behavior elsewhere, making conventional file-level impact analysis incomplete. At the same time, running every available test after every change is slow and expensive.

The central challenge is therefore twofold: identify the parts of an AI-enabled system that a change can influence, and select the tests that provide the strongest evidence about those risks.

## The paper's central contribution

This work investigates a multi-agent approach in which specialized agents collaborate on change impact analysis and test optimization. Rather than asking one component to interpret every signal, the approach separates responsibilities and combines their findings into an actionable testing decision.

That separation is useful in heterogeneous systems. Source changes, model dependencies, service relationships, and test histories provide different kinds of evidence and can be examined by agents suited to each concern.

## How the approach works

At a high level, the workflow connects three activities:

1. **Change interpretation** identifies what changed and gathers relevant technical context.
2. **Impact reasoning** traces likely effects through the dependencies of the AI-enabled system.
3. **Test optimization** prioritizes tests according to the identified impact and risk.

The resulting process focuses validation effort without treating test reduction as the only goal. The more important objective is to preserve confidence while directing limited test resources toward the most relevant behavior.

## Why this publication matters

Change impact analysis is especially difficult when system behavior depends on learned components and evolving data as well as deterministic code. A collaborative agent architecture offers a way to organize that complexity, retain traceable intermediate reasoning, and adapt test selection to the nature of each change.

The work is relevant to regression testing, continuous integration, AI engineering, and quality assurance for systems whose dependencies cross traditional software and machine-learning boundaries.

## Scope and responsible interpretation

Optimized test selection should not become an excuse to ignore unmodeled risk. Dependency information can be incomplete, agent conclusions can be uncertain, and novel failures may fall outside historical evidence. Practical use therefore requires conservative fallbacks, auditable decisions, coverage monitoring, and periodic full regression runs.

Readers should consult the published paper for the precise architecture, research method, evaluation setup, and results. Bibliographic details such as a DOI and page numbers will be added after IEEE assigns them.

## How to cite this paper

> N. Mani, A. S. Abdelfattah, S. Weerasinghe, X. Li, and T. Cerny, “Multi-Agent Change Impact Analysis and Test Optimization for AI-Enabled Software Systems,” in *Proceedings of the 8th IEEE International Conference on Artificial Intelligence Testing (AITest 2026)*, Fukuoka, Japan, 2026. Accepted for publication.

Before submitting a manuscript, verify the latest metadata on the [IEEE AITest 2026 conference site](https://cisose.fit.ac.jp/aitest/). Please cite the original publication rather than this explanatory post.

## Related research on this weblog

- Browse the complete [Nariman Mani publications list](/publications).
- Read about [dependency-aware adaptive test healing with graph neural networks](/posts/gnn-dependency-aware-adaptive-test-healing).
- Explore [adaptive test healing with LLMs and reinforcement learning](/posts/adaptive-test-healing-llm-reinforcement-learning).