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The ABC of Software Engineering Research
Abstract
A variety of research methods and techniques are available to SE researchers, and while several overviews exist, there is consistency neither in the research methods covered nor in the terminology used. Furthermore, research is sometimes critically reviewed for characteristics inherent to the methods. We adopt a taxonomy from the social sciences, termed here the ABC framework for SE research, which offers a holistic view of eight archetypal research strategies. ABC refers to the research goal that strives for generalizability over Actors (A) and precise measurement of their Behavior (B), in a realistic Context (C). The ABC framework uses two dimensions widely considered to be key in research design: the level of obtrusiveness of the research and the generalizability of research findings. We discuss metaphors for each strategy and their inherent limitations and potential strengths. We illustrate these research strategies in two key SE domains, global software engineering and requirements engineering, and apply the framework on a sample of 75 articles. Finally, we discuss six ways in which the framework can advance SE research.
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Setamanit, S., Wakeland, W. W., & Raffo, D. (2007) · Software Process Improvement and Practice · doi:10.1002/spip.335 · Cited by 63
Fifteen years of psychology in software engineering: Individual differences and cognitive science
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Theory-oriented software engineering
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Self-citationSoftware Engineering: Facts and Fallacies
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Session details: Flexible and distributed software processes: old petunias in new bowls?
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Self-citationQualitative research in software engineering
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fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage
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Babar, M. A., Kitchenham, B. A., & Jeffery, R. (2007) · Empirical Software Engineering · doi:10.1007/s10664-007-9052-6 · Cited by 32
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Investigating the use of moving windows to improve software effort prediction: a replicated study
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Metric-based software reliability prediction approach and its application
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Guest Editorial Reflections on Requirements
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Self-citation
Metadata
- Authors
- Stol, K.-J., & Fitzgerald, B.
- Venue
- ACM Transactions on Software Engineering and Methodology
- Published
- 2018-07-31
- Volume
- 27
- Issue
- 3
- Pages
- 1-51
- ISSN
- 1049-331X
- 1557-7392
- Cited by
- 245
- References
- 232
- Language
- en
- Funders
- Science Foundation Ireland (15/SIRG/3293, 13/RC/2094)
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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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.