Application and Assessment of Classification Techniques on Programmers' Performance in Software Industry

Software companies seek to develop and maintain high quality products along with increasing reliability and maintainability. Therefore, companies look into every aspect for project success. Software companies try to improve efficiency in process by using different techniques. With best hardware, operating system and methodologies, still there have been disastrous software failures. It is found that most software failures are due to lack of focus on people working on the process. Human aspect of software engineering has thus become an emerging stream and it is identified as core factor for project success. Therefore, human aspect needs deep investigation in this ever changing field of software development. One of the strategies is to process and analyze previous data of software companies to predict future failures. Data mining techniques have the ability to uncover hidden patterns in large databases. Software companies can build models that predict with a high degree of accuracy the attributes required in human aspect for success. Through these predictive data mining models, companies can effectively address issues ranging from selection, retention to development and effectively manage the team members. The purpose of this paper is to use different data mining algorithms on project personnel data and compare the accuracy of these algorithms.