Data Mining Approach to Effort Modeling On Agile Software Projects

Abstract

Software production is a complex process. Accurate estimation of the effort required to build the product, regardless of its type and applied methodology, is one of the key problems in the field of software engineering. This study presents the approach to effort estimation on agile software project using local data and data mining techniques, in particular k-nearest neighbor clustering algorithm. The applied process is iterative, meaning that in order to build predictive models, sets of data from previously executed project cycles are used. These models are then utilized to generate estimate for the next development cycle. Used data enrichment process, proved to be useful as results of effort prediction indicate decrease in estimation error compared to the estimates produced solely by the estimators. The proposed approach suggests that similar models can be built by other organizations as well, using the local data at hand and this way optimizing the management of the software product development.Povzetek: V prispevku je predstavljen pristop strojnega rudarjenja za modeliranje agilnih programskih projektov.

An empirical study of Aging Related Bug prediction using Cross Project in Cloud Oriented Software

“…Thus to handle class imbalance problem, CPDP is implemented on each cloud oriented software to predict ARBs. This study aims to compare the prediction results of the CPDP approach with WPDP of ARBs using machine learning classifiers [9]. The research paper is organized as follows: In Section 2, related work on the software aging problem is thoroughly discussed.…”