Existing research is unclear on how to generate lessons learned for defect prediction and effort estimation. Should we seek lessons that are global to multiple projects or just local to particular projects? This paper aims to comparatively evaluate local versus global lessons learned for effort estimation and defect prediction. We applied automated clustering tools to effort and defect datasets from the PROMISE repository. Rule learners generated lessons learned from all the data, from local projects, or just from each cluster.The results indicate that the lessons learned after combining small parts of different data sources (i.e., the clusters) were superior to either generalizations formed over all the data or local lessons formed from particular projects. We conclude that when researchers attempt to draw lessons from some historical data source, they should 1) ignore any existing local divisions into multiple sources, 2) cluster across all available data, then 3) restrict the learning of lessons to the clusters from other sources that are nearest to the test data.
We conducted an industrial case study of a distributed team in the USA and the Czech Republic that used Extreme Programming. Our goal was to understand how this globally-distributed team created a successful project in a new problem domain using a methodology that is dependent on informal, face-to-face communication. We collected quantitative and qualitative data and used grounded theory to identify four key factors for communication in globally-distributed XP teams working within a new problem domain. Our study suggests that, if these critical enabling factors are addressed, methodologies dependent on informal communication can be used on global software development projects.
A longitudinal case study evaluating the effects of adopting the Extreme Programming (XP) methodology was performed at Sabre Airline Solutions™. The Sabre team was a characteristically agile team in that they had no need to scale or re-scope XP for their project parameters and organizational environment. The case study compares two releases of the same product. One release was completed just prior to the team's adoption of the XP methodology, and the other was completed after approximately two years of XP use. Comparisons of the new release project results to the old release project results show a 50% increase in productivity, a 65% improvement in pre-release quality, and a 35% improvement in post-release quality. These findings suggest that, over time, adopting the XP process can result in increased productivity and quality.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.