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2 2022

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How good are my search strings? Reflections on using an existing review as a quasi-gold standard

Tran Huynh Khanh Vi, Börstler Jürgen, bin Ali Nauman, Unterkalmsteiner Michael

e-Informatica Software Engineering Journal

2022

Vol. 16, nr 1

art. no. 220103

Background: Systematic literature studies (SLS) have become a core research methodology in Evidence-based Software Engineering (EBSE). Search completeness, i.e., finding all relevant papers on the topic of interest, has been recognized as one of the most commonly discussed validity issues of SLSs. Aim: This study aims at raising awareness on the issues related to search string construction and on search validation using a quasi-gold standard (QGS). Furthermore, we aim at providing guidelines for search string validation. Method: We use a recently completed tertiary study as a case and complement our findings with the observations from other researchers studying and advancing EBSE. Results: We found that the issue of assessing QGS quality has not seen much attention in the literature, and the validation of automated searches in SLSs could be improved. Hence, we propose to extend the current search validation approach by the additional analysis step of the automated search validation results and provide recommendations for the QGS construction. Conclusion: In this paper, we report on new issues which could affect search completeness in SLSs. Furthermore, the proposed guideline and recommendations could help researchers implement a more reliable search strategy in their SLSs.

A Comparison of Citation Sources for Reference and Citation-Based Search in Systematic Literature Reviews

bin Ali Nauman, Tanveer Binish

e-Informatica Software Engineering Journal

2022

Vol. 16, nr 1

art. no. 220106

Context: In software engineering, snowball sampling has been used as a supplementary and primary search strategy. The current guidelines recommend using Google Scholar (GS) for snowball sampling. However, the use of GS presents several challenges when using it as a source for citations and references. Objective: To compare the effectiveness and usefulness of two leading citation databases (GS and Scopus) for use in snowball sampling search. Method: We relied on a published study that has used snowball sampling as a search strategy and GS as the citation source. We used its primary studies to compute precision and recall for Scopus. Results: In this particular case, Scopus was highly effective with 95% recall and had better precision of 5.1% compared to GS’s 2.8%. Moreover, Scopus found nine additional relevant papers. On average, one would read approximately 15 extra papers in GS than Scopus to identify one additional relevant paper. Furthermore, Scopus supports batch downloading of both citations and papers’ references, has better quality metadata, and does better source filtering. Conclusion: This study suggests that Scopus seems to be more effective and useful for snowball sampling than GS for systematic secondary studies attempting to identify peer-reviewed literature.