Software traceability is a fundamentally important task in software engineering. The need for automated traceability increases as projects become more complex and as the number of artifacts increases. We propose an automated technique that combines traceability with a machine learning technique known as topic modeling. Our approach automatically records traceability links during the software development process and learns a probabilistic topic model over artifacts. The learned model allows for the semantic categorization of artifacts and the topical visualization of the software system. To test our approach, we have implemented several tools: an artifact search tool combining keyword-based search and topic modeling, a recording tool that performs prospective traceability, and a visualization tool that allows one to navigate the software architecture and view semantic topics associated with relevant artifacts and architectural components. We apply our approach to several data sets and discuss how topic modeling enhances software traceability, and vice versa. Categories and Subject Descriptors

propose models or extensions to existing models. This includes system models, such as UML models, and project management artifacts, such as bug reports or tasks. For evaluating a research approach these models have to be implemented. Existing tools often lack the required extensibility and do not support data collection and analysis. Therefore, many researchers have to spend significant effort on the implementation. In this paper, we identify common requirements for a platform to build research tools on and we evaluate them based on potential use for recent publications. To evaluate the relevancy of the requirements we reviewed 274 research papers from the software engineering category of two major conferences over the last two years. We found 38 research approaches among those that would have benefited from a common platform with the presented requirements.

Abstract. Reconciling personalization with privacy has been a continuing interest in the user modeling community. In prior work, we proposed a dynamic privacy-enhancing user modeling framework based on a software product line architecture (PLA). Our system dynamically selects personalization methods during runtime that respect users ’ current privacy preferences as well as the prevailing privacy laws and regulations. One major concern about our approach is its performance since dynamic architectural reconfiguration during runtime is usually resource-intensive. In this paper, we describe four implementations of our system that vary two factors, and an in-depth performance evaluation thereof under realistic workload conditions. Our study shows that a customized version performs better than the original PLA implementation, that a multi-level caching mechanism improves both versions, and that the customized version with caching performs best. The average handling time per user session is less than 0.2 seconds for all versions except the original PLA implementation. Overall, our results demonstrate that with a reasonable number of networked hosts in a cloud computing environment, an internationally operating website can use our dynamic PLA-based user modeling approach to personalize their user services, and at the same time respect the individual privacy desires of their users as well as the privacy norms that may apply. 1

www.isr.uci.edu www.isr.uci.edu/tech-reports.html 1 Architecture-Centric Traceability for