Data Mining based Inferences about Software Parameters

Abstract

Up to now, several criteria (software parameters) have been determined in order to measure and evaluate software development projects: Productivity, engagement, attention to quality, code base knowledge and management, adherence to coding guidelines and techniques, learning and skills, personal responsibility and etc. However, there isn't any universally accepted criteria or a methodology to measure and evaluate software development projects. In this context, the common criteria set about measurement and evaluation of software development projects has been tried to be created in this study. In addition, some information has been taken from 105 software experts with 55 different software companies so as to evaluate the use of the common criteria in real work life. Accordingly, a measurement and evaluation criteria set (software parameters) about the software development projects has been formed based on the data mining algorithm -"Association Rule Mining Apriori Algorithm"with its 12 inferences. By the contribution of these data, the designed and developed software parameters have had more than 75 percent accuracy rate in the data mining algorithm. Finally, 8 important fuzzy-based rules about these 12 inferences have been formed and generated in this work.