The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Methodology for Collecting Valid Software Engineering Data
Abstract
An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. The data to be collected are based on the changes made to the software during development, and are obtained when the changes are made. To ensure accuracy of the data, validation is performed concurrently with software development and data collection. Validation is based on interviews with those people supplying the data. Results from using the methodology show that data validation is a necessary part of change data collection. Without it, as much as 50 percent of the data may be erroneous. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments. The application showed that the methodology was both feasible and useful.
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Cites
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Cited by
Software Engineering Economics
Boehm, B. W. (2001) · Pioneers and Their Contributions to Software Engineering · doi:10.1007/978-3-642-48354-7_5 · Cited by 5,981
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
Two case studies of open source software development: Apache and Mozilla
Mockus, A., Fielding, R. T., & Herbsleb, J. D. (2002) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/567793.567795 · Cited by 1,685
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Detecting Automatic Software Plagiarism via Token Sequence Normalization
Sağlam, T., Brödel, M., Schmid, L., & Hahner, S. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering · doi:10.1145/3597503.3639192 · Cited by 9
Metadata
- Authors
- Basili, V. R., & Weiss, D. M.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 1984-11
- Volume
- SE-10
- Issue
- 6
- Pages
- 728-738
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- 2326-3881
- OpenAlex
- W2049624350
- Cited by
- 1,149
- References
- 1
- FWCI
- 39.05
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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.