A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research
CoRR(2024)
摘要
The remarkable achievements of Artificial Intelligence (AI) algorithms,
particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their
extensive deployment across multiple sectors, including Software Engineering
(SE). However, due to their black-box nature, these promising AI-driven SE
models are still far from being deployed in practice. This lack of
explainability poses unwanted risks for their applications in critical tasks,
such as vulnerability detection, where decision-making transparency is of
paramount importance. This paper endeavors to elucidate this interdisciplinary
domain by presenting a systematic literature review of approaches that aim to
improve the explainability of AI models within the context of SE. The review
canvasses work appearing in the most prominent SE AI conferences and
journals, and spans 63 papers across 21 unique SE tasks. Based on three key
Research Questions (RQs), we aim to (1) summarize the SE tasks where XAI
techniques have shown success to date; (2) classify and analyze different XAI
techniques; and (3) investigate existing evaluation approaches. Based on our
findings, we identified a set of challenges remaining to be addressed in
existing studies, together with a roadmap highlighting potential opportunities
we deemed appropriate and important for future work.
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