标题： 一种基于人工智能的软件系统缺陷分类框架（AI-ODC） 显示英文标题

标题： A Defect Classification Framework for AI-Based Software Systems (AI-ODC)

摘要： 人工智能最近引起了广泛关注，它已被应用于从日常活动（如回复电子邮件和安排会议）到制造和自动化工作活动的多个领域。 人工智能系统主要作为软件解决方案实现，使用缺陷分析来发现和消除软件缺陷以确保其质量，缺陷分析是促进软件质量的主要活动之一。 尽管基于人工智能的系统大量涌现，但当前的缺陷分析模型无法捕捉它们的独特属性。 本文提出了一种受正交缺陷分类（ODC）范式启发的框架，在识别人工智能系统的特殊属性和特征的同时，使其能够进行缺陷分析。 本研究展示了修改ODC以适用于人工智能系统进行缺陷分类的可行性。 ODC被调整以适应人工智能系统的数据、学习和思考方面，这些是新引入的分类维度。 这种调整包括在ODC属性中引入一个额外的属性，加入一个新的严重性级别，并用与人工智能系统相关的特征替代影响区域。 该框架通过将其应用于一个公开可用的机器学习错误数据集进行了展示，并通过单因素和双因素分析对结果进行了分析。 案例研究表明，在学习阶段发生的缺陷最为普遍，并且与高严重性分类显著相关。 相反，在思考阶段发现的缺陷对可信度和准确性有不成比例的影响。 这些发现说明AIODC能够识别高风险的缺陷类别并指导有针对性的质量保证措施。 显示英文摘要

摘要： Artificial Intelligence has gained a lot of attention recently, it has been utilized in several fields ranging from daily life activities, such as responding to emails and scheduling appointments, to manufacturing and automating work activities. Artificial Intelligence systems are mainly implemented as software solutions, and it is essential to discover and remove software defects to assure its quality using defect analysis which is one of the major activities that contribute to software quality. Despite the proliferation of AI-based systems, current defect analysis models fail to capture their unique attributes. This paper proposes a framework inspired by the Orthogonal Defect Classification (ODC) paradigm and enables defect analysis of Artificial Intelligence systems while recognizing its special attributes and characteristics. This study demonstrated the feasibility of modifying ODC for AI systems to classify its defects. The ODC was adjusted to accommodate the Data, Learning, and Thinking aspects of AI systems which are newly introduced classification dimensions. This adjustment involved the introduction of an additional attribute to the ODC attributes, the incorporation of a new severity level, and the substitution of impact areas with characteristics pertinent to AI systems. The framework was showcased by applying it to a publicly available Machine Learning bug dataset, with results analyzed through one-way and two-way analysis. The case study indicated that defects occurring during the Learning phase were the most prevalent and were significantly linked to high-severity classifications. In contrast, defects identified in the Thinking phase had a disproportionate effect on trustworthiness and accuracy. These findings illustrate AIODC's capability to identify high-risk defect categories and inform focused quality assurance measures.