Software evolution prediction using seasonal time analysis: a comparative study

Goulão, Miguel; Fonte, Nelson; Wermelinger, Michel and Brito e Abreu, Fernando (2012). Software evolution prediction using seasonal time analysis: a comparative study. In: 16th European Conference on Software Maintenance and Reengineering, 27-30 Mar 2012, Szeged, Hungary.

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Abstract

Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data.

Item Type:	Conference or Workshop Item
Copyright Holders:	2012 IEEE
Extra Information:	Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Keywords:	software evolution; software maintenance; effort prediction; Eclipse; Bugzilla; ARIMA
Academic Unit/School:	Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group:	Centre for Research in Computing (CRC)
Item ID:	30293
Depositing User:	Michel Wermelinger
Date Deposited:	03 Feb 2012 14:17
Last Modified:	07 Dec 2018 21:14
URI:	http://oro.open.ac.uk/id/eprint/30293
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