Applying Machine Learning to Identify Maintenance Level for Software Releases
Download
Date
Authors
Type
Examensarbete för masterexamen
Programme
Model builders
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Maintenance is the single largest cost in software development. Therefore it is important
to understand what causes maintenance, and if it can be predicted. Many
studies have shown that certain ways of measuring the complexity of developed
programs can create decent prediction models to determine the likelihood of maintenance
due to failures in the software. Most have been prior to release and often
requires specific, object-oriented, metrics of the software to set up the models. These
metrics are not always available in the software development companies. This study
determines that cumulative software failure levels after release can be determined
using available data at a software development company and machine learning algorithms.
Description
Keywords
Machine learning, supervised learning, unsupervised learning, defect prediction, cumulative failure prediction