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.

Additive manufacturing (AM) is gaining attention across various industries like healthcare, aerospace, and automotive. However, identifying defects early in the AM process can reduce production costs and improve productivity - a key challenge. This study explored the effectiveness of machine learning (ML) approaches, specifically transfer learning (TL) models, for defect detection in 3D-printed cylinders. Images of cylinders were analyzed using models including VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2. Performance was compared across two datasets using accuracy, precision, recall, and F1-score metrics. In the first study, VGG16, InceptionResNetV2, and MobileNetV2 achieved perfect scores. In contrast, ResNet50 had the lowest performance, with an average F1-score of 0.32. Similarly, in the second study, MobileNetV2 correctly classified all instances, while ResNet50 struggled with more false positives and fewer true positives, resulting in an F1-score of 0.75. Overall, the findings suggest certain TL models like MobileNetV2 can deliver high accuracy for AM defect classification, although performance varies across algorithms. The results provide insights into model optimization and integration needs for reliable automated defect analysis during 3D printing. By identifying the top-performing TL techniques, this study aims to enhance AM product quality through robust image-based monitoring and inspection.

According to Gartner, by 2020, AI technologies will be pervasive in almost every new product and service and will also be a top investment priority for CIO's. Tech giants such as Microsoft, Facebook, Google, Amazon and the like spent billions on their AI initiatives. We started noticing the rise of AI as an enterprise technology. It's now clear how AI brings new intelligence to everything it touches by exploiting the vast sea of data at hand. Influential voices also started talking about the paradigm shift that this technology would bring to the world of software development.