The applications of Artificial Intelligence (AI) methods especially machine learning techniques have increased in recent years. Classification algorithms have been successfully applied to different problems such as requirement classification. Although these algorithms have good performance, most of them cannot explain how they make a decision. Explainable Artificial Intelligence (XAI) is a set of new techniques that explain the predictions of machine learning algorithms. In this work, the applicability of XAI for software requirement classification is studied. An explainable software requirement classifier is presented using the LIME algorithm. The explainability of the proposed method is studied by applying it to the PROMISE software requirement dataset. The results show that XAI can help the analyst or requirement specifier to better understand why a specific requirement is classified as functional or non-functional. The important keywords for such decisions are identified and analyzed in detail. The experimental study shows that the XAI can be used to help analysts and requirement specifiers to better understand the predictions of the classifiers for categorizing software requirements. Also, the effect of the XAI on feature reduction is analyzed. The results showed that the XAI model has a positive role in feature analysis.

翻译：近年来，人工智能方法尤其是机器学习技术的应用日益广泛。分类算法已成功应用于需求分类等多个问题领域。尽管这些算法性能优异，但多数算法无法解释其决策过程。可解释人工智能是一类用于解释机器学习算法预测结果的新兴技术。本研究探讨了可解释人工智能在软件需求分类中的适用性，并基于LIME算法构建了可解释的软件需求分类器。通过将该方法应用于PROMISE软件需求数据集，验证了所提方法的可解释性。实验结果表明，可解释人工智能能够帮助分析师或需求规范制定者更深入地理解特定需求被归类为功能性或非功能性需求的原因。研究识别并详细分析了影响此类决策的关键词汇。实验研究表明，可解释人工智能可辅助分析师与需求规范制定者更好地理解分类器对软件需求进行分类的预测机制。此外，本文还分析了可解释人工智能对特征约简的影响，结果显示该模型在特征分析中发挥了积极作用。