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An empirical validation of software cost estimation models
Abstract
Practitioners have expressed concern over their inability to accurately estimate costs associated with software development. This concern has become even more pressing as costs associated with development continue to increase. As a result, considerable research attention is now directed at gaining a better understanding of the software-development process as well as constructing and evaluating software cost estimating tools. This paper evaluates four of the most popular algorithmic models used to estimate software costs (SLIM, COCOMO, Function Points, and ESTIMACS). Data on 15 large completed business data-processing projects were collected and used to test the accuracy of the models' ex post effort estimation. One important result was that Albrecht's Function Points effort estimation model was validated by the independent data provided in this study [3]. The models not developed in business data-processing environments showed significant need for calibration. As models of the software-development process, all of the models tested failed to sufficiently reflect the underlying factors affecting productivity. Further research will be required to develop understanding in this area.
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Cited by
Digital Innovation as a Fundamental and Powerful Concept in the Information Systems Curriculum1
Fichman, R. G., Dos Santos, B. L., & Zheng, Z. (. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.2.01 · Cited by 935
Cost models for future software life cycle processes: COCOMO 2.0
Boehm, B., Clark, B., Horowitz, E., Westland, C., Madachy, R., & Selby, R. (1995) · Annals of Software Engineering · doi:10.1007/bf02249046 · Cited by 648
Systematic literature review of machine learning based software development effort estimation models
Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012) · Information and Software Technology · doi:10.1016/j.infsof.2011.09.002 · Cited by 526
Applying Systematic Reviews to Diverse Study Types: An Experience Report
Dyba, T., Dingsoyr, T., & Hanssen, G. K. (2007) · First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007) · doi:10.1109/esem.2007.59 · Cited by 391
Samareh Moosavi, S. H., & Khatibi Bardsiri, V. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.01.006 · Cited by 309
Metadata
- Authors
- Kemerer, C. F.
- Venue
- Communications of the ACM
- Published
- 1987-05
- Volume
- 30
- Issue
- 5
- Pages
- 416-429
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2075848956
- Cited by
- 896
- References
- 0
- FWCI
- 37.35
- Type
- journal article
- Language
- en
- Open access
- closed
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