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Towards an Evidence-Based Understanding of Electronic Data Sources
Open accessAbstract
Systematic Literature Reviews and Systematic Mapping Studies are relatively new forms of secondary studies in software engineering. Identifying relevant papers from various Electronic Data Sources (EDS) is one of the key activities of conducting these kinds of studies. Hence, the selection of EDS for searching the potentially relevant papers is an important decision, which can affect a study’s coverage of relevant papers. Researchers usually select EDS mainly based on personal knowledge, experience, and preferences and/or recommendations by other researchers. We believe that building an evidence-based understanding of EDS can enable researchers to make more informed decisions about the selection of EDS. This paper reports our initial effort towards this end. We propose an initial set of metrics for characterizing the EDS from the perspective of the needs of secondary studies. We explain the usage and benefits of the proposed metrics using the data gathered from two secondary studies. We also tried to synthesize the data from the two studies and that from literature to provide initial evidence-based heuristics for EDS selection.
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“All the team members of the group were using key steps of coding scheme, i.e. code, sub-categories, categories and framework/ theory, of Grounded theory approach [28], [85] as followed by other researchers in their studies [30], [86], [87].”
From Prioritization based taxonomy of DevOps security challenges using PROMETHEE (2020) · cites this paper as [28]
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Metadata
- Authors
- Chen, L., Babar, M. A., & Zhang, H.
- Affiliations
- University of Limerick (IE)
- University of Copenhagen (DK)
- UNSW Sydney (AU)
- Venue
- Electronic Workshops in Computing
- Publisher
- BCS Learning & Development
- Published
- 2010
- ISSN
- 1477-9358 (ISSN-L)
- OpenAlex
- W1877420198
- Cited by
- 191
- References
- 12
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 11 · 2023: 23 · 2022: 24 · 2021: 25 · 2020: 32 · 2019: 15 · 2018: 12 · 2017: 14 · 2016: 6 · 2015: 5 · 2014: 4 · 2013: 2 · 2012: 2
- FWCI
- 4.13
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Electronic workshops in computing (publishedVersion, cc-by)
- University of Limerick Institutional Repository (University of Limerick) (acceptedVersion, cc-by-nc-sa)
- University of Limerick Institutional Repository (University of Limerick) (acceptedVersion)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion, cc-by-nc-sa)
- IT University Of Copenhagen (IT University of Copenhagen) (submittedVersion)
- University of Limerick Institutional Repository (University of Limerick)
- Funders
- Science Foundation Ireland (03/CE2/I303_1)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- systematic reviews, systematic mapping, secondary studies, database selection, search strategies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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