Explainable AI In Software Engineering: Enhancing Developer-AI Collaboration

Abstract

Artificial Intelligence (AI) tools are increasingly integrated into software engineering tasks such as code generation, defect prediction, and project planning. However, widespread adoption is hindered by developers’ skepticism toward opaque AI models that lack transparency. This paper explores the integration of Explainable AI (XAI) into software engineering to foster a “developer-in-the-loop” paradigm that enhances trust, understanding, and collaboration between developers and AI agents. We review existing research on XAI techniques applied to feature planning, debugging, and refactoring, and identify the challenges of embedding XAI in real-world development workflows. A modular framework and system architecture are proposed to integrate explanation engines with AI models, IDEs, dashboards, and CI tools. A case study on explainable code review demonstrates how transparent AI suggestions can improve developer trust and team learning. We conclude by highlighting future directions, including personalization of explanations, cross-SDLC integration, and human-AI dialogue mechanisms, positioning XAI as essential to the next generation of intelligent development environments.

Topics & Concepts