This study investigates the incorporation of artificial intelligence (AI)-driven technologies, in facilitating automation, intelligent decision-making, predictive analytics, and responsive urban management, as essentials to the change inherent in developing smart cities. This need for efficiency, sustainability, and improved quality of life has led to a radical change in contemporary urban planning as urban areas are gradually developing into smart cities. Through networked systems and real-time data analysis, smart cities use cutting-edge technologies to optimize governance, infrastructure, and services. Smart energy grids, waste optimization, adaptive traffic control, and customized citizen services are all made possible by these technologies. However, an agile and iterative approach to technology implementation is necessary due to the dynamic and complex nature of urban systems. This study examines Agile methodology as a strategic framework for the creation and application of AI-driven tools in smart city settings. Agile's fundamental principles, namely collaborative development, incremental delivery, and responsiveness to change, align well with the demands of adjusting to changing user needs, technological advancements, and sociopolitical factors. This study examines how Agile practices support stakeholder coordination, iterative prototyping, and quick adaptation in AI-based urban solutions through a mixed-methods research design that includes case studies from a few chosen smart city initiatives. The results show that the proposed Nigeria Agile Integration for Smart City Transformation (NAI-SMART) framework showed that Abuja, Enugu, and Ibadan are more technology-compliant at the time of this study than a few other cities used in this research. NAI-SMART was able to achieve this via AI-based technologies namely GRU time series model, Reinforcement learning, XGBoost, and Scikit-learn that were employed during NAI-SMART Urban Intelligence Engine (NAI-SMART UIE) model creation. This research therefore, contributes to both theory and practice by proposing a step-by-step framework for AI-powered smart city development using Agile principles. Ultimately, the study underscores the synergistic potential of AI and Agile in actualizing the vision of inclusive, data-driven, and adaptive smart cities in Nigeria. 

A software product is expected to be subjected to critical evaluation on its quality attributes in order to ascertain that target quality requirements are met, and that those quality attributes responsible for revealing software quality are not omitted in the software development process. Software metrics are essential to accomplish the task. This paper has carried out an exploratory study of software measurement and software metrics in tandem. The study took into cognizance the interwoven nature of the duo in measuring and revealing software quality. The study formulated a model that expressed the mutual bonding that propels both measurement and metrics to describing software quality in numeric quantities of software attributes. The study identified six software attributes whose values are considered enough quantities to reveal the quality of a software product. The identification enabled the study to create a model equation aimed at giving a numeric value for the complete evaluation of a software system. The result of the implementation of the six software attributes into the model equation showed that two software products employed in the study are of high-quality, having quality values of 0.93 and 0.86 respectively. The attributes produced values that confirmed the maintainability (25 seconds & 20 seconds respectively) and reliability (0.78 & 0.80 respectively) of both software products among other differing features that characterize them.