Context: The global software industry and the software engineering (SE) academia are two large communities. However, unfortunately, the level of joint industry-academia collaborations in SE is still relatively very low, compared to the amount of activity in each of the two communities. It seems that the two 'camps' show only limited interest/motivation to collaborate with one other. Many researchers and practitioners have written about the challenges, success patterns (what to do, i.e., how to collaborate) and anti-patterns (what not do do) for industry-academia collaborations.Objective: To identify (a) the challenges to avoid risks to the collaboration by being aware of the challenges, (b) the best practices to provide an inventory of practices (patterns) allowing for an informed choice of practices to use when planning and conducting collaborative projects.Method: A systematic review has been conducted. Synthesis has been done using grounded-theory based coding procedures.Results: Through thematic analysis we identified 10 challenge themes and 17 best practice themes. A key outcome was the inventory of best practices, the most common ones recommended in different contexts were to hold regular workshops and seminars with industry, assure continuous learning from industry and academic sides, ensure management engagement, the need for a champion, basing research on real-world problems, showing explicit benefits to the industry partner, be agile during the collaboration, and the co-location of the researcher on the industry side.Conclusion: Given the importance of industry-academia collaboration to conduct research of high practical relevance we provide a synthesis of challenges and best practices, which can be used by researchers and practitioners to make informed decisions on how to structure their collaborations.

https://pure.tue.nl/ws/files/93075532/ChallengesandbestPracticesinIAColloborationsinSE.pdf

Challenges and best practices in industry-academia collaborations in software engineering

Background: A good search strategy is essential for a successful systematic literature study. Historically, database searches have been the norm, which has later been complemented with snowball searches. Our conjecture is that we can perform even better searches if combining the two search approaches, referred to as a hybrid search strategy. Objective: Our main objective was to compare and evaluate a hybrid search strategy. Furthermore, we compared some alternative hybrid search strategies to assess whether it was possible to identify more cost-efficient ways of searching for relevant primary studies. Method: To compare and evaluate the hybrid search strategy, we replicated an SLR on industry-academia collaboration in software engineering. The SLR used a more traditional approach to searching for relevant articles for an SLR, while the replication was conducted using a hybrid search strategy. Results: In our evaluation, the hybrid search strategy was superior in identifying relevant primary studies. It identified 30 percent more primary studies and even more when focusing only on peer-reviewed articles. To embrace individual viewpoints when assessing research articles and minimise the risk of missing primary studies, we introduced two new concepts, wild cards and borderline articles, when conducting systematic literature studies. Conclusions: The hybrid search strategy is a strong contender for being used when conducting systematic literature studies. Furthermore, alternative hybrid search strategies may be viable if selected wisely in relation to the start set for snowballing. Finally, the two new concepts were judged as essential to cater for different individual judgements and to minimise the risk of excluding primary studies that ought to be included. 

pdf/successful-combination-of-database-search-and-snowballing-1mdrygz8.pdf

Successful combination of database search and snowballing for identification of primary studies in systematic literature studies

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models. Software startup ...

/pdf/software-startups-a-research-agenda-16dpzk02zy.pdf

Software Startups - A Research Agenda

With software analytics, software practitioners explore and analyze data to obtain insightful, actionable information for tasks regarding software development, systems, and users. The StackMine project produced a software analytics system for Microsoft product teams. The project provided lessons on applying software analytics technologies to positively impact software development practice. The lessons include focusing on problems that practitioners care about, using domain knowledge for correct data understanding and problem modeling, building prototypes early to get practitioners' feedback, taking into account scalability and customizability, and evaluating analysis results using criteria related to real tasks.

/pdf/software-analytics-in-practice-4jz8ra9mxk.pdf

Software Analytics in Practice

The study has been undertaken to integrate two different aspects of the triple helix model: universities and the industry. Special attention has been paid to the prevailing difference between the two, hampering their working as a coherent unit. Integrating the existing knowledge in the study, we proposed the Academia-Industry Collaboration Plan (AICP) design model. The model comprises processes, methods or approaches, and tools. Processes serve as a road map to third parties for establishing collaboration between academia and the industry. It has all the essential process models and a series of steps that help minimize the organizational complexity of the collaboration process between academia and the industry. Methods or approaches serve the purpose of implementing those processes effectively. Finally, appropriate tools are selected to integrate possible collaboration improvements that lead to innovation.

Strengthening the Bridge Between Academic and the Industry Through the Academia-Industry Collaboration Plan Design Model

Collaboration between industry and academia supports improvement and innovation in industry and helps to ensure industrial relevance in academic research. This article presents an exploratory study of the factors for successful collaboration between industry and academia in software research.

/pdf/the-success-factors-powering-industry-academia-collaboration-p32n2e7u9d.pdf

The Success Factors Powering Industry-Academia Collaboration

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The Success Factors Powering Industry-Academia Collaboration