Performance Analysis using Bayesian Classification-Case Study of Software Industry

There has been rapid improvement in the ability to construct software systems , firstly by developing reliable hardware and second by developing effective process oriented methodologies. However there has been a lack of emphasis in people component of software engineering. This paper examines the human component and brings out interesting patterns for enhancing human performance. IT companies are constantly reaching out to find ways for software success and quality and innovations for having a competitive edge over other companies. It has been found that human aspects seem to dominate the most expensive project failures. Therefore Human Aspect of Software Engineering has emerged as a new dimension in software engineering in recent years. IT industry have a large set of data having precious information related to project personnel. Data mining technique has the ability to discover knowledge from this unexplored data. In this paper, data mining techniques particularly Bayesian classification method is applied on the data related to team members of a software company to help an organization to find the right project personnel who will contribute largely to project success by performing well. This study will help the organization to reduce the failure ratio of software to a significant level and enhance the quality of the software by deploying the right person at the very start of all software process.