"From Black-Box to Glass-Box: Explainable AI for Applied Intelligent Software Systems Across the Software Development Life Cycle," by Thi-Hong-Cuc Le and Dr. Xuan-Bach Le, won the Best Paper Award at IEA/AIE 2026 in Kuala Lumpur, Malaysia, July 2026. Artificial intelligence, and large language models in particular, is being embedded ever deeper into every stage of the software development life cycle. But the opacity of these systems limits trust, verification and certification — most acutely in safety-critical and regulated domains. The paper presents a systematic mapping study of explainable AI in software engineering, spanning classical machine learning, deep learning and LLM-based tools. The picture that emerges is lopsided: XAI research clusters around implementation and testing, while requirements, design, maintenance and operations remain thinly covered. Faithfulness validation and human-centred evaluation are both limited, and LLM-generated explanations introduce fresh risks around over-trust and pipeline opacity. The authors close with a research agenda built on three pillars: faithfulness by design, persona-aware explanations, and certification-ready XAI pipelines. Paper: https://lexuanbach.github.io/publication/IEA2026a.pdf Slides: https://lexuanbach.github.io/slides/IEA2026a_slides.pdf DOI: https://doi.org/10.1007/978-981-92-2891-1_8