Enhancing Human Aspect of Software Engineering using Bayesian Classifier
This addresses the challenge of optimizing human resources in software engineering for project managers, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of improving software project quality by analyzing project personnel competency and skill sets using a Bayesian classifier, resulting in a significant reduction in failure ratio and enhanced project performance.
IT industries in current scenario have to struggle effectively in terms of cost, quality, service or innovation for their subsistence in the global market. Due to the swift transformation of technology, software industries owe to manage a large set of data having precious information hidden. Data mining technique enables one to effectively cope with this hidden information where it can be applied to code optimization, fault prediction and other domains which modulates the success nature of software projects. Additionally, the efficiency of the product developed further depends upon the quality of the project personnel. The position of the paper therefore is to explore potentials of project personnel in terms of their competency and skill set and its influence on quality of project. The above mentioned objective is accomplished using a Bayesian classifier in order to capture the pattern of human performance. By this means, the hidden and valuable knowledge discovered in the related databases will be summarized in the statistical structure. This mode of predictive study enables the project managers to reduce the failure ratio to a significant level and improve the performance of the project using the right choice of project personnel.