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Cited by 852

The DOI matches its Crossref record.

Building knowledge through families of experiments

Basili, V.R., Shull, F., Lanubile, F.·1999·IEEE Transactions on Software Engineering
DOI 10.1109/32.799939
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Abstract

Experimentation in software engineering is necessary but difficult. One reason is that there are a large number of context variables and, so, creating a cohesive understanding of experimental results requires a mechanism for motivating studies and integrating results. It requires a community of researchers that can replicate studies, vary context variables, and build models that represent the common observations about the discipline. The paper discusses the experience of the authors, based upon a collection of experiments, in terms of a framework for organizing sets of related studies. With such a framework, experiments can be viewed as part of common families of studies, rather than being isolated events. Common families of studies can contribute to important and relevant hypotheses that may not be suggested by individual experiments. A framework also facilitates building knowledge in an incremental manner through the replication of experiments within families of studies. To support the framework, the paper discusses the experiences of the authors in carrying out empirical studies, with specific emphasis on persistent problems encountered in experimental design, threats to validity, criteria for evaluation, and execution of experiments in the domain of software engineering.

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Metadata

Authors
Basili, V. R., Shull, F., & Lanubile, F.
Venue
IEEE Transactions on Software Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1999
Volume
25
Issue
4
Pages
456-473
ISSN
0098-5589 (ISSN-L)
DOI
10.1109/32.799939
OpenAlex
W2167926541
Cited by
852
References
0
FWCI
37.08
Type
journal article
Language
en
Open access
closed

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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.