Miguel Goulão
Associate Professor
Departamento de Informática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre, P-2829-516 CAPARICA, Portugal (email)
Software Evolution Prediction Using Seasonal Time Analysis: a Comparative Study
in
- Citation:
- Goulão, M., N. Fonte, M. Wermelinger, and F. B. Abreu, "Software Evolution Prediction Using Seasonal Time Analysis: a Comparative Study", 16th European Conference on Software Maintenance and Reengineering (CSMR 2012), Szeged, Hungary, IEEE Computer Society, 30 Mar., 2012.
Date Presented:
30 Mar.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.
