系统梳理XAI在软件全生命周期各阶段的应用，解决AI决策难解释的问题。
Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey
- 按需求、设计、测试、维护等阶段分类整理XAI技术应用
- 发现68%研究聚焦维护阶段，管理与需求阶段仅占8%
- 首次全面覆盖SDLC各阶段的XAI方法，适合关注AI可解释性的开发者
人工智能（AI）正快速融入日常生活，用于自动化任务、辅助决策并提升效率。然而，复杂的AI模型因缺乏清晰解释（即“黑箱问题”）而限制了信任与广泛应用。可解释人工智能（XAI）应运而生，旨在提升AI系统的可解释性与透明度，使利益相关方能够信任、验证并基于AI结果采取行动。研究人员已开发多种XAI技术以支持软件工程各阶段。但现有研究存在明显不平衡：文献显示，68%的XAI在软件工程中的研究集中于维护阶段，而软件管理与需求阶段仅占8%。本文系统综述了概念解释、LIME、SHAP、规则提取、注意力机制、反事实解释和示例解释等XAI方法在软件开发生命周期（SDLC）各阶段——包括需求获取、设计与开发、测试与部署、演化——的应用。据我们所知，本工作是首个全面覆盖SDLC所有阶段的XAI应用综述，旨在推动可解释AI在软件工程中的落地，促进复杂AI模型在智能软件开发中的实际应用。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) is rapidly expanding and integrating more into daily life to automate tasks, guide decision making, and enhance efficiency. However, complex AI models, which make decisions without providing clear explanations (known as the "black-box problem"), currently restrict trust and widespread adoption of AI. Explainable Artificial Intelligence (XAI) has emerged to address the black-box problem of making AI systems more interpretable and transparent so stakeholders can trust, verify, and act upon AI-based outcomes. Researchers have developed various techniques to foster XAI in the Software Development Lifecycle. However, there are gaps in applying XAI techniques in the Software Engineering phases. Literature review shows that 68% of XAI in Software Engineering research is focused on maintenance as opposed to 8% on software management and requirements. In this paper, we present a comprehensive survey of the applications of XAI methods such as concept-based explanations, Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), rule extraction, attention mechanisms, counterfactual explanations, and example-based explanations to the different phases of the Software Development Life Cycle (SDLC), including requirements elicitation, design and development, testing and deployment, and evolution. To the best of our knowledge, this paper presents the first comprehensive survey of XAI techniques for every phase of the Software Development Life Cycle (SDLC). This survey aims to promote explainable AI in Software Engineering and facilitate the practical application of complex AI models in AI-driven software development.
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