Using Context Distance Measurement to Analyze Results across Studies

Providing robust decision support for software engineering (SE) requires the collection of data across multiple contexts so that one can begin to elicit the context variables that can influence the results of applying a technology. However, the task of comparing contexts is complex due to the large number of variables involved. This works extends a previous one in which we proposed a practical and rigorous process for identifying evidence and context information from SE papers. The current work proposes a specific template to collect context information from SE papers and an interactive approach to compare context information about these studies. It uses visualization and clustering algorithms to help the exploration of similarities and differences among empirical studies. This paper presents this approach and a feasibility study in which the approach is applied to cluster a set of papers that were independently grouped by experts.