Communication and collaboration is a major challenge for large-scale software development. Cognitive and psychological distance between individuals and teams affect this collaboration and can cause communications gaps. We propose a novel method for assessing distance between teams, and explore potential explanations for these distances. In an exploratory case study of the quality assurance department at Axis Communications, we used interactive posters to collect data and obtained a 52% response rate from the 175 test engineers. We identified low psychological distance as an indicator of a company culture with open and frequent communication, and of a team with good social networking skills and well-functioning points of contact. We found that low cognitive distance is an indicator of differences regarding the responsibilities of a team; within the same system and between different types of systems. We also found correlations between psychological and cognitive distance. Large organisations may apply our concept to assess distance between teams, and our finding can be useful in interpreting these distances. Furthermore, our results provide a basis for further research on how the concept of distances can be used to assess collaboration within large organisations.

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The ability to collaboratively edit data in distributed environments is essential in our information-based industry. Typically users or systems concurrently access and modify data from different locations for different purposes. Each purpose might require its own representation and subset of the shared data (i.e., a model), for the editor to be productive. Consequently, a multi-model system results, which requires multi-directional synchronization. Although some approaches exist to realize such systems, none of these supports concurrent modifications. To overcome this limitation, we extend previous work on role-oriented model synchronization with a novel conflict detection and resolution approach. Role-oriented programming has been shown to be an adequate paradigm to realize multi-model systems, as it offers separation of concerns at the level of object collaborations and allows to express context-dependent behavior. We evaluate our approach using an employee data management case study and assess the introduced performance overhead.

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Source code search is frequently needed and important in software development. Keyword search for source code is a widely used but a limited approach. This paper presents CodeKōan, a scalable engine for searching millions of online code examples written by the worldwide programmers' community which uses data parallel processing to achieve horizontal scalability. The search engine relies on a token-based, programming language independent algorithm and, as a proof-of-concept, indexes all code examples from Stack Overflow for two programming languages: Java and Python. This paper demonstrates the benefits of extracting crowd knowledge from Stack Overflow by analyzing well-known open source repositories such as OpenNLP and Elasticsearch: Up to one third of the source code in the examined repositories reuses code patterns from Stack Overflow. It also shows that the proposed approach recognizes similar source code and is resilient to modifications such as insertion, deletion and swapping of statements. Furthermore, evidence is given that the proposed approach returns very few false positives among the search results.

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