Mohammed O. Alannsary

arXiv2026

Citizens' Contentment with e-Government Solutions and Services in Saudi Arabia

Governments around the world have worked tirelessly to develop technological solutions, on the one hand to better serve their citizens and, on the other hand, to advance in the United Nations Electronic Government Development Index (EGDI). Thus, it is crucial to assess e-government solutions and services from different aspects. This study evaluates e-government solutions and services based on user expectations in four aspects: general satisfaction, satisfaction with features, trust, and pleasure. In this study, a questionnaire was developed to allow the evaluation of e-government solutions and services in Saudi Arabia, and could also be utilized to evaluate e-government in any other nation. The study included 276 valid participants, while the required sample size was calculated using a standard sample size estimation formula for large populations (95% confidence level, 5% margin of error). In addition, descriptive analysis was used to analyze participant responses. The results showed that e-government services in Saudi Arabia achieved a level of citizen contentment that is consistent with its score in the EGDI published in the year 2024.

cs.SE↗

arXiv2025

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

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.

cs.SE↗