In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, Naïve Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: 75% percentage of correctly classified files, a recall of 80%, and a false positive rate

We describe an empirical study of open-source and closed-source software projects. The motivation for this research is to quantitatively investigate common perceptions about open-source projects, and to validate these perceptions through an empirical study. We investigate the hypothesis that open-source software grows more quickly, but does not find evidence to support this. The project growth is similar for all the projects in the analysis, indicating that other factors may limit growth. The hypothesis that creativity is more prevalent in open-source software is also examined, and evidence to support this hypothesis is found using the metric of functions added over time. The concept of open-source projects succeeding because of their simplicity is not supported by the analysis, nor is the hypothesis of open-source projects being more modular. However, the belief that defects are found and fixed more rapidly in open-source projects is supported by an analysis of the functions modified. We find support for two of the five common beliefs and conclude that, when implementing or switching to the open-source development model, practitioners should ensure that an appropriate metrics collection strategy is in place to verify the perceived benefits.

Purpose: In this paper the authors aim to investigate the importance of factors for the adoption of free/libre open source software (FLOSS) in the public sector. They seek to evaluate how different factors impact during the initiation and implementation phases of the adoption process.Design/methodology/approach: The authors base the methodological approach on two exploratory case studies with a contrasting result logic. They build a multi‐level framework grounded both on literature review, and feedback from stakeholders. They then apply the framework to two case studies to better frame the findings. They consider phases of adoption (initiation, implementation) and the levels of adoption (technological, organizational, environmental, individual).Findings: In the case studies, the authors found the importance of a strong and decision‐centric management board to give the impulse for the initiation phase of the process. As perceived by the stakeholders, a strong governmental support is of paramount importance to increase the adoption at the public level, although in the case studies examined the initiation stage started from the impulse of a championing management. Both case studies passed the initiation phase successfully. Continuous employees' training, organizational objectives consensus, and business process reengineering have been found important for the implementation phase. In the case study in which these factors were not in place, the implementation phase of adoption failed. Environmental factors – although relevant for the initiation of the adoption process – are less significant during the actual implementation of the adoption process, as the contrasting result logic from the case studies shows.Research limitations/implications: The study refers to two public organizations in a specific environmental setting. No causality among factors has been inferred. Quantitative objective data have been used to determine the success of adoption, for qualitative data multiple sources have been used when possible to limit threats to validity.Practical implications: The framework can be used by stakeholders in public organizations to better frame their adoption strategies and to compare results across institutions. Lessons learnt from the case studies can be useful to drive future adoptions of FLOSS.Originality/value: The framework combines phases of adoption and levels making it possible to frame the analysis of the case studies. It has been operationalized with a set of metrics, and with a protocol for the case studies to increase replicability value.