Computer Science ›› 2023, Vol. 50 ›› Issue (5): 3-11.doi: 10.11896/jsjkx.221100159

• Explainable AI • Previous Articles     Next Articles

Review of Software Engineering Techniques and Methods Based on Explainable Artificial Intelligence

XING Ying   

  1. School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Received:2022-11-19 Revised:2023-02-02 Online:2023-05-15 Published:2023-05-06
  • About author:XING Ying,born in 1978,Ph.D,is a senior member of China Computer Fe-deration.Her main research interests include software testing and deep lear-ning.

Abstract: In terms of information processing and decision-making,artificial intelligence(AI) methods have shown superior performance compared to traditional methods.However,when AI models are put into production,their output results are not guaranteed to be completely accurate,so the “unreliability” of AI technology has gradually become a major obstacle to the large-scale implementation of AI.As AI is gradually applied to software engineering,the drawbacks of over-reliance on historical data and non-transparent decision-making are becoming more and more obvious,so it is crucial to provide reasonable explanations for the decision results.This paper elaborates on the basic concepts of explainable AI(XAI) and the evaluation of explanation models,and explores the feasibility of combining software engineering with explainable AI.Meanwhile,it investigates relevant researches in software engineering,analyzes the four typical application directions of XAI,namely,malware detection,high-risk component detection,software load distribution,and binary code similarity analysis,to discuss how to reveal the correctness of the system output,thereby increasing the credibility of the software system.This paper also gives insights into the research direction in combining software engineering and explainable artificial intelligence.

Key words: Explainable artificial intelligence, Software engineering, Malware detection, Code similarity analysis

CLC Number: 

  • TP311
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