Refining high-level requirements into low-level ones is a common task, especially in safety-critical systems engineering. The objective is to describe every important aspect of the high-level requirement in a low-level requirement, ensuring a complete and correct implementation of the system's features. To this end, standards and regulations for safety-critical systems require reviewing the coverage of high-level requirements by all its low-level requirements to ensure no missing aspects.

Bug localization is the task of recommending source code locations (typically files) that probably contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components, e.g., similar reports, version history, code structure, to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports, i.e., feature requests and bug reports, to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, supporting of future more efficient and rapid replication and comparison, we conducted a replication study of this approach with the original data set and also on an extended data set. The extended data set includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. While we find that the TraceScore component as the core of ABLoTS produces comparable results with the extended data set, we also find that the ABLoTS approach no longer achieves promising results, due to an overlooked side effect of incorrectly choosing a cut-off date that led to training data leaking into test data with significant effects on performance.

Software engineers use requirement-to-method trace matrices to indicate the methods implementing different system requirements. Requirement-to-method trace matrices pinpoint the exact method implementing each requirement, which facilitates software maintenance and bug fixing. The code structure of a system can be used to make predictions about requirement-to-method traces. In this paper, we present a data set documenting the requirement-to-method traces as well as the code structure (methods, variables, etc.) for four open source systems. The code structure was obtained by parsing the systems under consideration and extracting the methods, variables, etc. The requirement-to-method trace matrices were obtained by resorting to students as well as to the original developers of the systems who provided us with the list of requirement-to-method traces.

Several researchers have studied that developers contributing to open source systems tend to self-organize in "emerging" teams. The structure of these latent teams has a significant impact on software quality, with development teams structure somewhat reflected in the way developers communicate and contribute in the subsystems of a system. Therefore, in order to study socio-technical interactions as well as the software evolution dynamics of open source systems, in this paper, we present a novel dataset, gathered from 20 open source projects, which report the developers' activities in the scope of commits and issues at the level of subsystems. Thus, the new, generated dataset comprises of emerging and explicit links among developers, commits, issues, and source code artifacts, with data grouped around the subsystems point of view, which can be used to better study the system dynamics behind the extracted sociotechnical interactions.

Web-based collaboration and virtual environments supported by various Web 2.0 concepts enable the application of numerous monitoring, mining and analysis tools to study human interactions and team formation processes. The composition of an effective team requires a balance between adequate skill fulfillment and sufficient team connectivity. The underlying interaction structure reflects social behavior and relations of individuals and determines to a large degree how well people can be expected to collaborate. In this paper we address an extended team formation problem that does not only require direct interactions to determine team connectivity but additionally uses implicit recommendations of collaboration partners to support even sparsely connected networks. We provide two heuristics based on Genetic Algorithms and Simulated Annealing for discovering efficient team configurations that yield the best trade-off between skill coverage and team connectivity. Our self-adjusting mechanism aims to discover the best combination of direct interactions and recommendations when deriving connectivity. We evaluate our approach based on multiple configurations of a simulated collaboration network that features close resemblance to real world expert networks. We demonstrate that our algorithm successfully identifies efficient team configurations even when removing up to 40% of experts from various social network configurations.