Establishing a Search String to Detect Secondary Studies in Software Engineering
Bianca Minetto Napoleao, Katia Romero Felizardo, Erica Ferreira de, Souza, Fabio Petrillo, Nandamudi L. Vijaykumar, Elisa Yumi Nakagawa, Sylvain, Halle

TL;DR
This paper proposes and validates an effective search string for detecting secondary studies in Software Engineering, achieving over 90% recall and nearly 60% precision, facilitating easier identification of related reviews.
Contribution
It introduces a validated search string for secondary studies in SE, addressing challenges in recall, relevance, and precision, based on analysis of multiple tertiary studies.
Findings
Over 90% recall in retrieving secondary studies
High general precision of nearly 60%
Effective search string includes specific review-related terms
Abstract
Context: A tertiary study can be performed to identify related reviews on a topic of interest. However, the elaboration of an appropriate and effective search string to detect secondary studies is challenging for Software Engineering (SE) researchers. Objective: The main goal of this study is to propose a suitable search string to detect secondary studies in SE, addressing issues such as the quantity of applied terms, relevance, recall and precision. Method: We analyzed seven tertiary studies under two perspectives: (1) structure -- strings' terms to detect secondary studies; and (2) field: where searching -- titles alone or abstracts alone or titles and abstracts together, among others. We validate our string by performing a two-step validation process. Firstly, we evaluated the capability to retrieve secondary studies over a set of 1537 secondary studies included in 24 tertiary…
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