Software components selection: An optimized selection criterion for component-based software engineering (CBSE)

Abstract

Component-based software engineering (CBSE) is becoming the frequently used approach for software development concerning time and cost constraints. While most people’s lifestyle depends on the use of software applications, the development cost and time of software applications are becoming more difficult to achieve. Also, high-quality and competent applications that fit users’ needs became essential. Therefore, most software development organizations are reusing commercial-off-the-shelf (COTS) to reduce development costs and time. However, most organizations and developers face problems selecting components that fit customer needs to integrate with the target system. So, decisions regarding software component selection are hard to consider regarding the entire quality of the software system. This study aims to investigate the most important matters to software industry practitioners and experts involved in component selection. First, the author asked the practitioners to select the most important quality criteria for an online bookstore from a list by providing their subjective judgment and evaluation grades; after that, utilizing the evidential reasoning (ER) approach for software component selection problems due to its ability to analyze decisions with multilevel evaluations and information uncertainty. Moreover, the main features of the ER approach, such as weight normalization, probability assessment, dealing with uncertainty, and utility intervals, offer several advantages for COTS selection problems, including cost and time minimizing, improved software reliability, effectiveness, and efficiency. This study concentrated on assessing the quality criteria for an online bookstore using the ER approach. Analysis results are provided depending on the computational steps of this approach. Finally, the findings show the rank of the four components according to their weights, evaluation grades, and belief degrees for the selection.