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Título:	Software development metrics prediction using time series methods
Autor/a:	Choras, Michal; Kozik, Rafal; Pawlicki, Marek; Holubowicz, Witold; Franch Gutiérrez, Javier
Otros autores:	Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació; Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering
Abstract:	The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it’s declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software’s release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.
Abstract:	Peer Reviewed
Materia(s):	-Àrees temàtiques de la UPC::Informàtica::Enginyeria del software -Computer software -- Development -Computer software -- Quality control -Software engineering -Software development -Prediction -Metrics -Time series -Programari -- Control de qualitat -Programari -- Desenvolupament
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Tipo de documento:	Artículo - Versión presentada Objeto de conferencia
Editor:	Springer
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