mlsanityData to 7 Oct 2026

Guidelines for including grey literature and conducting multivocal literature reviews in software engineering

arXiv 1707.02553 ↗First posted 9 Jul 2017cs.SE cs.DLcited by 38

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Abstract (arXiv)

Context: A Multivocal Literature Review (MLR) is a form of a Systematic Literature Review (SLR) which includes the grey literature (e.g., blog posts and white papers) in addition to the published (formal) literature (e.g., journal and conference papers). MLRs are useful for both researchers and practitioners since they provide summaries both the state-of-the art and -practice in a given area. Objective: There are several guidelines to conduct SLR studies in SE. However, given the facts that several phases of MLRs differ from those of traditional SLRs, for instance with respect to the search process and source quality assessment. Therefore, SLR guidelines are only partially useful for conducting MLR studies. Our goal in this paper is to present guidelines on how to conduct MLR studies in SE. Method: To develop the MLR guidelines, we benefit from three inputs: (1) existing SLR guidelines in SE, (2), a literature survey of MLR guidelines and experience papers in other fields, and (3) our own experiences in conducting several MLRs in SE. All derived guidelines are discussed in the context of three examples MLRs as running examples (two from SE and one MLR from the medical sciences). Results: The resulting guidelines cover all phases of conducting and reporting MLRs in SE from the planning phase, over conducting the review to the final reporting of the review. In particular, we believe that incorporating and adopting a vast set of recommendations from MLR guidelines and experience papers in other fields have enabled us to propose a set of guidelines with solid foundations. Conclusion: Having been developed on the basis of three types of solid experience and evidence, the provided MLR guidelines support researchers to effectively and efficiently conduct new MLRs in any area of SE.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2017.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 4; 2024: 4; 2025: 8; 2026: 15.

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  9. 2024Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Reviewcited by 6
  10. 2021The impact of using biased performance metrics on software defect prediction researchcited by 4
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