What Evidence We Would Miss If We Do Not Use Grey Literature?

TL;DR

This study assesses how Grey Literature (GL) contributes to Multivocal Literature Reviews (MLRs) in Software Engineering, highlighting that GL provides unique evidence, mainly from practitioners, that would be missed if not included.

Contribution

It offers an empirical analysis of the extent and nature of GL's contribution to MLR studies in Software Engineering, emphasizing its importance and challenges.

Findings

GL provides evidence not found in Traditional Literature

Most GL sources are practitioner-generated like blogs and presentations

Inclusion of GL prevents omission or weakening of findings

Abstract

Context: Over the last years, Grey Literature (GL) is gaining increasing attention in Secondary Studies in Software Engineering (SE). Notably, Multivocal Literature Review (MLR) studies, that search for evidence in both Traditional Literature (TL) and GL, is particularly benefiting from this raise of GL content. Despite the growing interest in MLR-based studies, the literature assessing how GL has contributed to MLR studies is still scarce. Objective: This research aims to assess how the use of GL contributed to MLR studies. By contributing, we mean, understanding to what extent GL is providing evidence that is indeed used by an MLR to answer its research question. Method: We conducted a tertiary study to identify MLR studies published between 2017 and 2019, selecting nine MLRs studies. Using qualitative and quantitative analysis, we identified the GL used and assessed to what extent these MLRs are contributing to MLR studies. Results: Our analysis identified that 1) GL provided evidence not found in TL, 2) most of the GL sources were used to provide recommendations to solve problems, explain a topic, and classify the findings, and 3) 19 different GL types were used in the studies; these GLs were mainly produced by SE practitioners (including blog posts, slides presentations, or project descriptions). Conclusions: We evidence how GL contributed to MLR studies. We observed that if these GLs were not included in the MLR, several findings would have been omitted or weakened. We also described the challenges involved when conducting this investigation, along with potential ways to deal with them, which may help future SE researchers.