Enhancing Software Development with Explainable Generative Artificial Intelligence: A Human-Centered Approach

Generative Artificial Intelligence has emerged as a transformative technology in software engineering by automating programming tasks such as code generation, debugging, testing, and documentation. Despite its impressive capabilities, the lack of transparency in AI-generated recommendations remains a significant challenge for developers, particularly in enterprise and safety-critical software systems. Developers often hesitate to rely on AI-generated code because they cannot easily understand the reasoning behind the generated outputs. This paper proposed a Human-Centered Explainable Generative Artificial Intelligence (HC-XGenAI) framework that integrates Explainable Artificial Intelligence (XAI) techniques into AI-assisted software development. Unlike conventional AI coding assistants, the proposed framework combines code generation with confidence estimation, natural language explanations, feature attribution, alternative solution generation, and continuous developer feedback. By incorporating these explainability mechanisms, the framework improves transparency, accountability, and collaboration between developers and AI systems. The performance evaluation demonstrated that the proposed approach significantly improves developer trust, code quality, explainability, debugging efficiency, and user satisfaction compared to conventional generative AI systems. The integration of continuous learning further enables the AI assistant to adapt to developer preferences and improve recommendation quality over time. These findings highlight the importance of designing AI systems that augment human expertise rather than replacing it. In conclusion, explainable generative AI represents a promising direction for the future of software engineering. By combining intelligent automation with transparent decisionmaking, the proposed framework supports reliable, trustworthy, and human-centered software development. Future advancements in explainable large language models, adaptive learning, and secure AI-assisted programming are expected to further strengthen collaboration between software developers and intelligent systems.

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