The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A survey of controlled experiments in software engineering
Open accessAbstract
The classical method for identifying cause-effect relationships is to conduct controlled experiments. This paper reports upon the present state of how controlled experiments in software engineering are conducted and the extent to which relevant information is reported. Among the 5,453 scientific articles published in 12 leading software engineering journals and conferences in the decade from 1993 to 2002, 103 articles (1.9 percent) reported controlled experiments in which individuals or teams performed one or more software engineering tasks. This survey quantitatively characterizes the topics of the experiments and their subjects (number of subjects, students versus professionals, recruitment, and rewards for participation), tasks (type of task, duration, and type and size of application) and environments (location, development tools). Furthermore, the survey reports on how internal and external validity is addressed and the extent to which experiments are replicated. The gathered data reflects the relevance of software engineering experiments to industrial practice and the scientific maturity of software engineering research.
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Citations
Cites
Confounding and Collapsibility in Causal Inference
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Statistical Science · doi:10.1214/ss/1009211805 · Cited by 933 · cited 1×
“For further discussions (including formal definitions) of concepts of confounding, see [21].”
Background
Cited by
Systematic Literature Reviews in Software Engineering—A Systematic Literature Review
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537
Guidelines for conducting and reporting case study research in software engineering
Runeson, P., & Höst, M. (2009) · Empirical Software Engineering · doi:10.1007/s10664-008-9102-8 · Cited by 3,862
Systematic Mapping Studies in Software Engineering
Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008) · Electronic Workshops in Computing · doi:10.14236/ewic/ease2008.8 · Cited by 3,239
“Therefore, one review study (Sjøberg et al. 2005) applied the classification scheme proposed by Glass et al (Glass et al.”
InfluentialEmpirical studies of agile software development: A systematic review
Dybå, T., & Dingsøyr, T. (2008) · Information and Software Technology · doi:10.1016/j.infsof.2008.01.006 · Cited by 2,668
Systematic literature reviews in software engineering – A tertiary study
Kitchenham, B., Pretorius, R., Budgen, D., Pearl Brereton, O., Turner, M., Niazi, M., & Linkman, S. (2010) · Information and Software Technology · doi:10.1016/j.infsof.2010.03.006 · Cited by 1,230
Metadata
- Authors
- Sjoeberg, D. I. K., Hannay, J. E., Hansen, O., Kampenes, V. B., Karahasanovic, A., Liborg, N.-K., & Rekdal, A. C.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2005-09
- Volume
- 31
- Issue
- 9
- Pages
- 733-753
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2111199933
- Cited by
- 682
- References
- 1
- FWCI
- 116.05
- Type
- journal article
- Language
- en
- Open access
- green
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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.
