Manifesting Implicit Dependencies in Requirements Task: An Automated Method Integrating Code Awareness and Change Analysis

Abstract

In modern software development, accurately assessing the impact scope of requirement changes is crucial for software quality, yet this task faces two major challenges. First, highly simplified requirement descriptions (one-sentence requirements) tend to conceal complex business logic and extensive system dependencies. Second, commits often intertwine code changes for multiple requirements, leading to impact assessments that are both incomplete and time-consuming. Traditional manual assessment methods are not only inefficient but also prone to cognitive biases, which often result in the omission of critical impact points. To address these problems, this paper proposes AIST (Analysis of the Impact Scope of the Task), an automated impact scope analysis approach based on Large Language Models (LLMs). AIST leverages the semantic understanding and planning capabilities of LLMs to automatically parse requirement descriptions, retrieve relevant code context from the codebase, and integrate code change information, thereby generating a comprehensive impact scope assessment report. We evaluated AIST on 40 historical requirement changes from three real-world industrial projects. The experimental results show that it achieves a precision of 80.19 %, a recall of 94.95 %, and an F1-score of 86.35 %, thereby significantly improving the completeness of impact scope identification and demonstrating high sensitivity in detecting tangled code changes. In a user survey, the novelty score was 6.32 out of 7.00, and the overall satisfaction score was 6.36 out of 7.00, indicating that users perceive that the tool effectively improves efficiency and reduces the risk of omissions.