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, for preparing the background of the study, several researches have been studied about “Software Development Projects”, “Software Development Process” and “Software Development Measurement and Evaluation”. Also, with this literature study, the common criteria set about measurement and evaluation of software development projects has been created, generated and presented. In addition, some information has been got and taken from 105 software experts (software analyzers, software developers and managers) with 55 different software companies so as to evaluate the use of the common criteria in real work life, and to identify criteria which are not seen in researches before, but used in real work life. Accordingly, a measurement and evaluation criteria set (software parameters) about the software development projects has been created based on the data mining algorithm – “Association Rule Mining Apriori Algorithm” – with its 12 inferences. This set has also consisted of 10 software parameters with 6 dual relationships. With the light of these data, the designed and developed software parameters have had high validation with more than 75 percent accuracy rate. As a natural result of this, the study will have had a positive effect on software engineering by shedding light on its working domain – software development.