What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I worked in collaboration with during my post-doc). Given a background in software engineering research, what cutting-edge techniques and tools from academia did I use in my daily work when developing and testing the systems of these companies? Regarding validation and verification (my main area of research), the answer is rather short: as far as I can tell, only FindBugs. In this paper, I report on why this was the case, and discuss all the challenging, complex open problems we face in industry and which somehow are “neglected” in the academic circles. In particular, I will first discuss what actual tools I could use in my daily work, such as JaCoCo and Selenium. Then, I will discuss the main open problems I faced, particularly related to environment simulators, unit and web testing. After that, popular topics in academia are presented, such as UML, regression and mutation testing. Their lack of impact on the type of projects I worked on in industry is then discussed. Finally, from this industrial experience, I provide my opinions about how this situation can be improved, in particular related to how academics are evaluated, and advocate for a greater involvement into open-source projects.
More recently, @cite_13 made a survey about industry-academia collaborations in computer science and software engineering, involving 60 academics and 66 people in industry. One conclusion was: There is a lack of communication and understanding between academia and industry ( @math ) there is a lot of mistrust of academics among those in industry ( @math ) Both sides, however, seem to be open to collaboration and would love to see stronger relationships''.
@cite_13
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IT-driven innovation is an enormous factor in the worldwide economic leadership of the United States. It is larger than finance, construction, or transportation, and it employs nearly 6 of the US workforce. The top three companies, as measured by market capitalization, are IT companies - Apple, Google (now Alphabet), and Microsoft. Facebook, a relatively recent entry in the top 10 list by market capitalization has surpassed Walmart, the nation's largest retailer, and the largest employer in the world. The net income of just the top three exceeds $80 billion - roughly 100 times the total budget of the NSF CISE directorate which funds 87 of computing research. In short, the direct return on federal research investments in IT research has been enormously profitable to the nation. The IT industry ecosystem is also evolving. The time from conception to market of successful products has been cut from years to months. Product life cycles are increasingly a year or less. This change has pressured companies to focus industrial R&D on a pipeline or portfolio of technologies that bring immediate, or almost immediate, value to the companies. To defeat the competition and stay ahead of the pack, a company must devote resources to realizing gains that are shorter term, and must remain agile to respond quickly to market changes driven by new technologies, new startups, evolving user experience expectations, and the continuous consumer demand for new and exciting products. Amidst this landscape, the Computing Community Consortium convened a round-table of industry and academic participants to better understand the landscape of industry-academic interaction, and to discuss possible actions that might be taken to enhance those interactions. We close with some recommendations for actions that could expand the lively conversation we experienced at the round-table to a national scale.