Antecedents of open source software defects: A data mining approach to model formulation, validation and testing

Abstract

This paper develops tests and validates a model for the antecedents of open source software (OSS) defects, using Data and Text Mining. The public archives of OSS projects are used to access historical data on over 5,000 active and mature OSS projects. Using domain knowledge and exploratory analysis, a wide range of variables is identified from the process, product, resource, and end-user characteristics of a project to ensure that the model is robust and considers all aspects of the system. Multiple Data Mining techniques are used to refine the model and data is enriched by the use of Text Mining for knowledge discovery from qualitative information. The study demonstrates the suitability of Data Mining and Text Mining for model building. Results indicate that project type, enduser activity, process quality, team size and project popularity have a significant impact on the defect density of operational OSS projects. Since many organizations, both for profit and not for profit, are beginning to use Open Source Software as an economic alternative to commercial software, these results can be used in the process of deciding what software can be reasonably maintained by an organization.

The attraction of contributors in free and open source software projects

“…These stages in the life-cycle are planning, pre-alpha, alpha, beta, production and mature. FOSP make their software stage available to the public, informing their maturity level condition and influencing decisions to adopt and contribute (Crowston and Scozzi, 2002;Raja and Tretter, 2006), as well as the level of development activity .…”

“…In defence of their usefulness, researchers have used downloads to count users and user-interest Wiggins et al, 2009), and members to represent developers (Fershtman and Gandal, 2011). The FOSP literature frequently adopts these measures (e.g., Crowston et al, 2005;Raja and Tretter, 2006;Stewart and Gosain, 2006;Sauer, 2007).…”

“…These roles are reflected in the ways the success of FOSP has been measured (Crowston et al, 2005;Long, 2006), including developers' contribution to source code modularity (Shaikh and Cornford, 2003), number of lines of code generated (Mockus et al, 2000), velocity of closing bugs (Herbsleb and Mockus, 2003), and the number of downloads (Crowston et al, 2004;Krishnamurthy, 2002;Grewal et al, 2006). Raja and Tretter (2006), Crowston and Scozzi (2002), and Comino et al (2007) viewed success as the ability of a project to advance through development phases (e.g., from alpha to beta, and from beta to stable). Koch (2004) and Crowston and Howison (2006) suggested the use of community size (i.e., number of members) as a proxy for success.…”

A Study of the Relationships between Source Code Metrics and Attractiveness in Free Software Projects

“…This status can influence a developer's decision to join and contribute to a project, as well as a user's decision to adopt its software [8]. It also can affect members' motivations to work in order to release a new version, affecting productivity rates [21], [6]. We propose to insert source code attributes as a project characteristic in this theoretical cause and effect model of Free Software attractiveness, shown in Figure 1.…”

“…The meaning of "success" to Free Software projects was discussed from different perspectives in previous research: (i) source code modularity [22]; (ii) number of lines of code generated [23]; (iii) velocity of closing bugs [6]; (iv) ability of a project to advance through development phases (e.g., from alpha to beta to stable) [21], [20]; (v) number of downloads [24]; (vi) number of members [25].…”