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Systematic literature studies have received much attention in empirical software engineering in recent years. They have become a powerful tool to collect and structure reported knowledge in a systematic and reproducible way. We distinguish systematic literature reviews to systematically analyze reported evidence in depth, and systematic mapping studies to structure a field of interest in a broader, usually quantified manner. Due to the rapidly increasing body of knowledge in software engineering, researchers who want to capture the published work in a domain often face an extensive amount of publications, which need to be screened, rated for relevance, classified, and eventually analyzed. Although there are several guidelines to conduct literature studies, they do not yet help researchers coping with the specific difficulties encountered in the practical application of these guidelines. In this article, we present an experience-based guideline to aid researchers in designing systematic literature studies with special emphasis on the data collection and selection procedures. Our guideline aims at providing a blueprint for a practical and pragmatic path through the plethora of currently available practices and deliverables capturing the dependencies among the single steps. The guideline emerges from various mapping studies and literature reviews

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Organizations are introducing agile and lean software development techniques in operations to increase the pace of their software development process and to improve the quality of their software. They use the term DevOps, a portmanteau of development and operations, as an umbrella term to describe their efforts. In this paper, we describe the ways in which organizations implement DevOps and the outcomes they experience. We first summarize the results of a systematic literature review that we performed to discover what researchers have written about DevOps. We then describe the results of an exploratory interview‐based study involving 6 organizations of various sizes that are active in various industries. As part of our findings, we observed that all organizations were positive about their experiences and only minor problems were encountered while adopting DevOps.

General theories of software engineering must balance between providing full understanding of a single case and providing partial understanding of many cases. In this paper we argue that for theories to be useful in practice, they should give sufficient understanding of a sufficiently large class of cases, without having to be universal or complete. We provide six strategies for developing such theories of the middle range.In lab-to-lab strategies, theories of laboratory phenomena are developed and generalized to other laboratory phenomena. This is a characteristic strategy for basic science. In lab-to-field strategies, theories are developed of artifacts that first operate under idealized laboratory conditions, which are then scaled up until they can operate under uncontrolled field conditions. This is the characteristic strategy for the engineering sciences.In case-based strategies, we generalize about components of real-world cases, that are supposed to exhibit less variation than the cases as a whole. In sample-based strategies, we generalize about the aggregate behavior of samples of cases, which can exhibit patterns not visible at the case level. We discuss three examples of sample-based strategies.Throughout the paper, we use examples of theories and generalization strategies from software engineering to illustrate our analysis. The paper concludes with a discussion of related work and implications for empirical software engineering research.