Automatic Means of Identifying Evolutionary Events in Software Development

Abstract

The software development process patterns in open source software projects are not well known. Consequently, the longevity of new open source software projects is left up to subjective experiences of the development team. In this study, we are investigating a data mining approach for identifying relevant patterns in software development process. We demonstrate the capabilities of wavelet analysis on 27 open source software projects for identifying similar evolutionary patterns or events in different projects. The analysis identified close to 1000 evolutionary patterns common to multiple projects. The analysis of some of the patterns shows that the end of source code evolution of a project is determined early in the project. In addition, strong fluctuations of activity in sequential periods are identified as good indicators of problems in projects. In conclusion, the analysis reveals that wavelet analysis can be a powerful and objective tool for identifying evolutionary events that can be used as estimation basis or management guide in software projects.

Software evolution: the lifetime of fine-grained elements

“…Many empirical studies focus on predictive models of software projects' evolution at macroscopic scale. Relevant studies have looked at long-term sustainability factors in the evolution of LibreOffice (Gamalielsson & Lundell, 2014), the change of program dependencies in the Apache ecosystem (Bavota et al, 2013), and the early identification of source code evolution pattern in open source projects (Karus, 2013). Additionally, many empirical studies examine software evolution at the microscopic level, considering the evolution of source-code elements such as methods.…”

Open Source Software Evolution

“…Another research study (Canfora et al, 2010) suggests the potential of multivariate time series analysis to identify change couplings complementary to those provided by association rules. Karus (2013) has used wavelet analysis for frequent pattern analysis of time series data collected for 27 open source projects. The technique is preferred as it is able to detect small anomalies in series and also makes series, of different lengths or scale, comparable.…”