Software evolution prediction using seasonal time analysis: a comparative study

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

A Software Evolution Process Model: Analysis of Software Failure Causes

This paper presents a study on the degree of impact of several components on the evolvability of software systems. In particular, it focuses on failure rates, testing, and other factors which force the evolution of a software system. Also, it studies the evolution of software systems in the presence of various failure scenarios. Unlike previous studies based on the system dynamic (SD) model, this study is modeled on the basis of actor-network theory (ANT) of software evolution, using the system dynamic environment. The main index used in this study is the destabilization period after the recovery from any failure scenario. The results show that more testing and quick recovery after failure are keys to a fast system return to stability