A Systematic Literature Review of Explainable AI for Software Engineering

Context: In recent years, leveraging machine learning (ML) techniques has become one of the main solutions to tackle many software engineering (SE) tasks, in research studies (ML4SE). This has been achieved by utilizing state-of-the-art models that tend to be more complex and black-box, which is led to less explainable solutions that reduce trust and uptake of ML4SE solutions by professionals in the industry. Objective: One potential remedy is to offer explainable AI (XAI) methods to provide the missing explainability. In this paper, we aim to explore to what extent XAI has been studied in the SE community (XAI4SE) and provide a comprehensive view of the current state-of-the-art as well as challenge and roadmap for future work. Method: We conduct a systematic literature review on 24 (out of 869 primary studies that were selected by keyword search) most relevant published studies in XAI4SE. We have three research questions that were answered by meta-analysis of the collected data per paper. Results: Our study reveals that among the identified studies, software maintenance (\%68) and particularly defect prediction has the highest share on the SE stages and tasks being studied. Additionally, we found that XAI methods were mainly applied to classic ML models rather than more complex models. We also noticed a clear lack of standard evaluation metrics for XAI methods in the literature which has caused confusion among researchers and a lack of benchmarks for comparisons. Conclusions: XAI has been identified as a helpful tool by most studies, which we cover in the systematic review. However, XAI4SE is a relatively new domain with a lot of untouched potentials, including the SE tasks to help with, the ML4SE methods to explain, and the types of explanations to offer. This study encourages the researchers to work on the identified challenges and roadmap reported in the paper.