Software reuse is a challenging and multifaceted topic. Significant research effort has been spent to address technical and organizational aspects. However, adoption of proposed practices and novel approaches often proceeds slowly. Additionally, little is known on how reuse is currently effected in practice and which solutions have proven useful. This paper aims to shed light on the matter by studying the current practice of reuse at Google. We conduct an exploratory study with a total of 49 participants of which 39 answered our online questionnaire and 10 participated in our 1h interviews. We assess reuse practices, success factors and challenges and collect ideas for improvement.We distill our findings to provide practitioners with examples of scalable reuse practices and detail on prerequisites required to implement/tailor a similar reuse approach. Furthermore, we point out open issues to support researchers and practitioners alike to align their efforts for developing solutions.

Testing and development are increasingly performed by different organizations, often in different countries and time zones. Since their distance complicates communication, close alignment between development and testing becomes increasingly challenging. Unfortunately, poor alignment between the two threatens to decrease test effectiveness or increases costs.In this paper, we propose a conceptually simple approach to assess test alignment by uncovering methods that were changed but never executed during testing. The paper's contribution is a large industrial case study that analyzes development changes, test service activity and field faults of an industrial business information system over 14 months. It demonstrates that the approach is suitable to produce meaningful data and supports test alignment in practice.

System behavior is often based on causal relations between certain events (e.g. If event1, then event2). Consequently, those causal relations are also textually embedded in requirements. We want to extract this causal knowledge and utilize it to derive test cases automatically and to reason about dependencies between requirements. Existing NLP approaches fail to extract causality from natural language (NL) with reasonable performance. In this paper, we describe first steps towards building a new approach for causality extraction and contribute: (1) an NLP architecture based on Tree Recursive Neural Networks (TRNN) that we will train to identify causal relations in NL requirements and (2) an annotation scheme and a dataset that is suitable for training TRNNs. Our dataset contains 212,186 sentences from 463 publicly available requirement documents and is a first step towards a gold standard corpus for causality extraction. We encourage fellow researchers to contribute to our dataset and help us in finalizing the causality annotation process. Additionally, the dataset can also be annotated further to serve as a benchmark for other RE-relevant NLP tasks such as requirements classification.

Software Product Lines (SPL) enable efficient derivation of products. SPL concepts have been applied successfully in many domains including interactive applications. However, the user interface (UI) part of applications has barely been addressed yet. While standard SPL concepts allow derivation of functionally correct UIs, there are additional nonfunctional requirements, like usability, which have to be considered. This paper presents a case study investigating UI variability found in variants of the commercial web-based information system HIS-GX/QIS. We analyze which aspects of a UI vary and to which degree. The results show that just tweaking the final UI (e.g., using stylesheets) is not sufficient but there is a need for more customization which must be supported by, e.g., UI-specific models.

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