A Systematic Literature Review Of Artificial Intelligence In The This systematic literature review (slr) investigates the application of xai techniques in se. it is based on empirical studies published between january 2020 and september 2022 to analyze the. 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.
Explainable Artificial Intelligence And Cybersecurity A Systematic 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. The document discusses a systematic literature review of explainable artificial intelligence (xai) techniques in software engineering. it analyzes 131 studies published between 2020 2022 to understand how xai has been applied. In this thesis, we aim to explore to what extent xai has been studied in the se domain (xai4se) and provide a comprehensive view of the current state of the art as well as challenges and a roadmap for future work. Therefore, this paper presents a systematic literature review (slr) on the recent developments of xai methods and evaluation metrics concerning different application domains and tasks.
Pdf Recent Applications Of Explainable Ai Xai A Systematic In this thesis, we aim to explore to what extent xai has been studied in the se domain (xai4se) and provide a comprehensive view of the current state of the art as well as challenges and a roadmap for future work. Therefore, this paper presents a systematic literature review (slr) on the recent developments of xai methods and evaluation metrics concerning different application domains and tasks. 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. 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. In software engineering, explainable ai has been recently studied in the domain of defect prediction (i.e., a classification model to predict if a file class method will be defective in the future or not). Explainable artificial intelligence (xai) techniques, which have emerged to make ml models more transparent and interpretable, can address the lack of interpretability challenge. they shed.