Applying Machine Learning to Identify Maintenance Level for Software Releases
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Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
STUART, CHRISTOFFER
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Machine learning , supervised learning , unsupervised learning , defect prediction , cumulative failure prediction