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Citation indices providing information on backward citation (BWC) and forward citation (FWC) links are essential for literature discovery, bibliographic analysis, and knowledge synthesis, especially when language barriers impede document identification. However, the suitability of citation indices varies. While some have been analyzed, the majority, whether new or established, lack comprehensive evaluation. Therefore, this study evaluates the citation coverage of the citation indices of 59 databases, encompassing the widely used Google Scholar, Scopus, and Web of Science alongside many others never previously analyzed, such as the emerging Lens, Scite, Dimensions, and OpenAlex or the subject‐specific PubMed and JSTOR. Through a comprehensive analysis using 259 journal articles from across disciplines, this research aims to guide scholars in selecting indices with broader document coverage and more accurate and comprehensive backward and forward citation links. Key findings highlight Google Scholar, ResearchGate, Semantic Scholar, and Lens as leading options for FWC searching, with Lens providing superior download capabilities. For BWC searching, the Web of Science Core Collection can be recommended over Scopus for accuracy. BWC information from publisher databases such as IEEE Xplore or ScienceDirect was generally found to be the most accurate, yet only available for a limited number of articles. The findings will help scholars conducting systematic reviews, meta‐analyses, and bibliometric analyses to select the most suitable databases for citation searching. [ABSTRACT FROM AUTHOR]

Static analysis tools have showcased their importance and usefulness in automated detection of defects. However, the tools are known to generate a large number of alarms which are warning messages to the user. The large number of alarms and cost incurred by their manual inspection have been identified as two major reasons for underuse of the tools in practice. To address these concerns plentitude of studies propose postprocessing of alarms: processing the alarms after they are generated. These studies differ greatly in their approaches to postprocess alarms. A comprehensive overview of the postprocessing approaches is, however, missing. In this article, we review 130 primary studies that propose postprocessing of alarms. The studies are collected by combining keywords-based database search and snowballing. We categorize approaches proposed by the collected studies into six main categories: clustering, ranking, pruning, automated elimination of false positives, combination of static and dynamic analyses, and simplification of manual inspection. We provide overview of the categories and sub-categories identified for them, their merits and shortcomings, and different techniques used to implement the approaches. Furthermore, we provide (1) guidelines for selection of the postprocessing techniques by the users/designers of static analysis tools; and (2) directions that can be explored by the researchers. [ABSTRACT FROM AUTHOR]

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.

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Systematic literature reviews represent an important methodology in Evidence-Based Software Engineering. To define the methodological route in these type of studies, in which a review of quantitative and qualitative aspects of primary studies is carried out to summarize the existing information regarding a particular topic, researchers use protocols that guide the construction of knowledge from research questions. This article presents a process that uses forward Snowballing, which identifies the articles cited in the paper under study and the number of citations as inclusion criteria to complement systematic literature reviews. A process that relies on software tools was designed to apply the Snowballing strategy and to identify the most cited works and those who cite them. To validate the process, a review identified in the literature was used. After comparing the results, new works that were not taken into account but made contributions to the subject of study emerged. The citation index represents the number of times a publication has been referenced in other documents and is used as a mechanism to analyze, measure, or quantitatively assess the impact of said publication on the scientific community. The present study showed how applying Snowballing along with other strategies enables the emergence of works that may be relevant for an investigation given the citations rate. That is, implementing this proposal will allow updating or expanding systematic literature studies through the new works evidenced.

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. [ABSTRACT FROM AUTHOR]