EXPLORING THE EFFORT OF GENERAL SOFTWARE PROJECT ACTIVITIES WITH DATA MINING

Abstract

Software project effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, data mining is used to improve an organization's software process quality, e.g. the accuracy of effort estimations. Data is collected from projects, and data miners are used to discover beneficial knowledge. This paper reports a data mining experiment in which we examined 32 software projects to improve effort estimation. We examined three major categories of software project activities, and focused on the activities of the category which has got the least attention in research so far, the non-construction activities. The analysis is based on real software project data supplied by a large European software company. In our data mining experiment, we applied a range of machine learners. We found that the estimated total software project effort is a predictor in modeling and predicting the actual quality management effort of the project.