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  • 1.
    Jabbari, Ramtin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ali, Nauman bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Tanveer, Binish
    Fraunhofer Institute for Experimental Software Engineering IESE, DEU.
    Towards a benefits dependency network for DevOps based on a systematic literature review2018In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 30, no 11, article id e1957Article in journal (Refereed)
    Abstract [en]

    DevOps as a new way of thinking for software development and operations has received much attention in the industry, while it has not been thoroughly investigated in academia yet. The objective of this study is to characterize DevOps by exploring its central components in terms of principles, practices and their relations to the principles, challenges of DevOps adoption, and benefits reported in the peer-reviewed literature. As a key objective, we also aim to realize the relations between DevOps practices and benefits in a systematic manner. A systematic literature review was conducted. Also, we used the concept of benefits dependency network to synthesize the findings, in particular, to specify dependencies between DevOps practices and link the practices to benefits. We found that in many cases, DevOps characteristics, ie, principles, practices, benefits, and challenges, were not sufficiently defined in detail in the peer-reviewed literature. In addition, only a few empirical studies are available, which can be attributed to the nascency of DevOps research. Also, an initial version of the DevOps benefits dependency network has been derived. The definition of DevOps principles and practices should be emphasized given the novelty of the concept. Further empirical studies are needed to improve the benefits dependency network presented in this study. © 2018 John Wiley & Sons, Ltd.

  • 2.
    Jabbari, Ramtin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ali, Nauman Bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Tanveer, Binish
    What is DevOps?: A Systematic Mapping Study on Definitions and Practices2016Conference paper (Refereed)
  • 3.
    Marculescu, Bogdan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Jabbari, Ramtin
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Molléri, Jefferson Seide
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Perception of Scientific Evidence: Do Industry and Academia Share an Understanding?2016Conference paper (Refereed)
    Abstract [en]

    Context: Collaboration depends on communication and upon having a similar understanding of the notions that are being discussed, and a similar appraisal of their value. Existing work seems to show that the collaboration between industry and academia is hampered by a difference in values. In particular, academic work focuses more on generalizing on the basis of existing evidence, while industry prefers to particularize conclusions to individual cases. This has lead to the conclusion that industry values scientific evidence less than academia does. 

    Objective: This paper seeks to re-evaluate that conclusion, and investigate if industry and academia share a definition of scientific evidence. If evidence can be found of competing views, we propose a more finely grained model of empirical evidence and its role in building software engineering knowledge. Moreover, we seek to determine if a more nuanced look the notion of scientific evidence has an influence on how academics and industry practitioners perceive that notion. 

    Method: We have developed a model of key concepts related to understanding empirical evidence in software engineering. An initial validation has been conducted, consisting of a survey of master students, to determine if competing views of evidence exist at that level. The model will be validated by further literature study and semistructured interviews with industry practitioners. 

    Results: We propose a model of empirical evidence in software engineering, and initial validation of that model by means of a survey. The results of the survey indicate that conflicting opinions already exist in the student body regarding the notion of evidence, how trustworthy different sources of evidence and knowledge are, and which sources of evidence and types of evidence are more appropriate in various situations.

    Conclusion: Rather than a difference in how industry and academia value scientific evidence, we see evidence of misunderstanding, of different notions of what constitutes scientific evidence and what strength of evidence is required to achieve specific goals. We propose a model of empirical evidence, to provide a better understanding of what is required in various situations and a better platform for communication between industry and academia.

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