Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey
Lakshit Arora, Sanjay Surendranath Girija, Shashank Kapoor, Aman Raj, Dipen Pradhan, Ankit Shetgaonkar

TL;DR
This paper provides the first comprehensive survey of explainable AI techniques applied across all phases of the Software Development Lifecycle, highlighting current applications, gaps, and future directions for integrating XAI in software engineering.
Contribution
It systematically reviews XAI methods used in SDLC phases, identifying gaps and promoting practical adoption of explainable AI in software engineering.
Findings
68% of XAI research focuses on maintenance
Only 8% of research addresses requirements and management phases
First survey covering all SDLC phases with XAI techniques
Abstract
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,…
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Taxonomy
MethodsSoftmax · Attention Is All You Need
