文献类型：期刊文献

英文题名：Aligning XAI explanations with software developers' expectations: A case study with code smell prioritization

作者：Huang, Zijie[1];Yu, Huiqun[1];Fan, Guisheng[1];Shao, Zhiqing[1];Li, Mingchen[1];Liang, Yuguo[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：238

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20234014831473);WOS:【SCI-EXPANDED(收录号:WOS:001086902300001)】；

基金：star This work was partially supported by the National Natural Science Foundation of China (No. 62372174) , the Natural Science Foundation of Shanghai, China (No. 21ZR1416300) , the Capacity Building Project of Local Universities Science and Technology Commission of Shanghai Municipality, China (No. 22010504100) , the Research Programme of National Engineering Laboratory for Big Data Distribution and Exchange Technologies, China, and the Shanghai Municipal Special Fund for Promoting High Quality Development, China (No. 2021-GYHLW-01007) .

语种：英文

外文关键词：Code smell; Software quality assurance; Explainable artificial intelligence; Empirical software engineering

摘要：EXplainable Artificial Intelligence (XAI) aims at improving users' trust in black-boxed models by explaining their predictions. However, XAI techniques produced unreasonable explanations for software defect prediction since expected outputs (e.g., causes of bugs) were not captured by features used to build models. To set aside feature engineering limitations and evaluate whether XAI could adapt to developers, we exploit XAI for code smell prioritization (i.e., predicting criticalities of sub-optimal coding practices and design choices), whose features could capture developers' major expectations. We assess the gap between XAI explanations and developers' expectations in terms of (1) the accuracy of prediction, (2) the coverage of explanations on expectations, and (3) the complexity of explanations. We also narrow the gap by preserving the features related to developers' expectations as much as possible in feature selection. We find that XAI can explain smells with simpler causes in top 3 to 5 features. Complex smells can be explained in around 10 features, which need more expertise to interpret. Selecting features adapting to the developers' expectations improves coverage by 5% to 29%, with almost no negative impact on accuracy and complexity. Results also highlight the need of dividing coarse-grained prediction targets and developing fine-grained feature engineering.