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.

10.4.2 Convergence of Expert Opinion via the Wideband Delphi Method: An Application in Cost Estimation Models

“…This calibration approach ensures that the cost model is influenced both by expert opinion and historical data to yield a calibrated a posteori model (Chulani, Boehm & Steece 1999). Parametric models such as this need to be updated periodically so that their calibration is representative of the latest trends in technology, processes, and personnel experience (Kemerer 1987). The next section provides more information about the strengths and limitations of the Wideband Delphi method.…”

ONTOCOM Revisited: Towards Accurate Cost Predictions for Ontology Development Projects

“…Experiences in related engineering areas [1,9,20] let us assume that the most significant cost driver is the size of the ontology. This is reflected in Equation 1 in the parameter Size that corresponds to the size of the ontology, that is, the number of kilo primitives which are expected to result from the conceptualization phase (including fragments built by reuse or other knowledge acquisition methods).…”

“…Cost estimation has a long-standing tradition in more mature engineering disciplines such as software engineering or industrial production [1,9,21]. Although the importance of cost issues is well-acknowledged in the semantic technologies community, as to the best of our knowledge, no cost estimation model for ontology development besides ONTOCOM has been proposed so far.…”

Metric framework for object-oriented real-time systems specification languages

“…The values of these drivers are identified according to the development context, i.e., type of computer, qualification of personnel staffing, etc. Other techniques following the same path have been compared in Kemerer (1987) and Laranjeira (1990). The drivers usually adopted cannot be easily tuned for tailoring the method for measuring the specifications of real-time systems in formal languages.…”