Modern software systems intensively use the Agile software development processes for their development and maintenance. The Agile development methodology encourages customer satisfaction, early incremental delivery, and overall development simplicity. Agile development methods accept changes in requirements and technology and use a more adaptive or iterative approach to planning. With the adaptation of Agile process models in the development of modern software systems, there is a need for continuous improvement in the Agile processes. Agile development processes are modified and upgraded by utilizing software metrics. There are several proposed software metrics for measuring performance and quality in Agile software development. These include customer satisfaction, story point estimation, velocity, test coverage, defects in production, and other metrics. Each software metric has its own merits and demerits. This research aims to provide a comprehensive review of published research work on software metrics. Specifically, it summarizes the software metrics used for measuring the performance of Agile process models. This research paper will help understand the usefulness of various software metrics used in Agile development, and it will serve as a foundation for future research in software metrics for Agile software development.

 Software requirements engineering is the process of eliciting, analyzing, negotiating, specifying, documenting, and validating software requirements. These requirements are gathered from user needs, stakeholder needs, domain information, existing system information, organizational standards, and external regulations. Traditionally, the requirement engineering process has been driven by stakeholdersâ\euro™ needs. However, the advent of the Internet of Things (IoT) devices, mobile devices, social networks, and other big data technologies have caused a shift from the traditional mode of the requirements acquisition process to new data-driven and automatic modes of requirement elicitation by utilizing data mining techniques. The traditional methodologies of requirement engineering processes are inadequate for modern-day software applications due to their inability to effectively capture user requirements from the ever-increasing volume of data, customer requirements, and feedback. Consequently, the utilization of data mining techniques is gaining popularity in the field of requirement engineering. These techniques are employed to extract valuable insights from data and elicit customer requirements, providing a more robust approach to addressing the challenges posed by modern software development. This research paper comprehensively reviews various data mining techniques and methodologies used for gathering, eliciting, and analyzing software requirements. It offers valuable insights into the significant areas of data mining, requirements analysis, and elicitation that have undergone extensive research in recent years. By providing a thorough understanding of requirements engineering with data mining, the paper serves as a valuable resource for researchers venturing into the realm of requirement analysis and elicitation with data mining and seeking potential avenues for future research.Â

The emergence of modern information systems, social media platforms, Internet of Things (IoT) devices, web and mobile applications, and other new technologies have resulted in the generation of huge amounts of data. Big data refers to extensive and diverse datasets that are generated from a wide range of sources and pose challenges in processing and analyzing using traditional data tools. Software systems that are designed to handle large and complex datasets are known as big data software systems. These are a collection of tools, technologies, and platforms designed to handle, process, analyze, and extract insights from big data. This research paper presents a comprehensive review of various aspects of big data software engineering, development tools, literature reviews, techniques, and terminologies. It offers valuable insights into the significant areas of big data that have undergone extensive research in recent years. By providing a thorough understanding of big data software systems, the paper serves as a valuable resource for researchers venturing into the realm of big data and seeking potential avenues for future research.Â

Software quality definition encompasses six key factors. These are low levels of defects, high reliability, efficient structure that minimizes insertion of new defects during defect repairs, effective customer support, high user satisfaction, and a high defect repair rate, especially for high-severity defects. Several methods have been proposed for measuring software quality. These include fuzzy logic based frameworks, neuro fuzzy models, unsupervised learning methods, queuing models, and software quality metrics. This research paper reviews different frameworks, models, metrics, and evaluation techniques that have been published in various research papers for estimating and assessing software quality. Research publications related to software quality measurement, estimation, and assessment were collected for the literature review. These publications were accessed from online research databases that consist of open access (OA) journals.