Open Access Open Access  Restricted Access Subscription or Fee Access

Estimation of Cost for Software using Dashboard Framework

A. Schoeman, X.J. Temes


The popular parametric cost models in widespread use today allow size to be expressed as lines of code, function points, object-oriented metrics and other measures. Size of the software is provided in a number of available units, cost factors describe the overall environment and calibrations may take the form of coefficients adjusted for actual data or other types of factors that account for domain-specific attributes. The most frequently used models for estimating DoD software effort, cost and schedule is COCOMO I, COCOMO II, SLIM, etc. COCOMO is a public domain model that USC continually updates and is implemented in several commercial tools. SLIM is another parametric tool that uses a different approach to effort and schedule estimation. Dashboards are essential elements in the day to-day operation of modern enterprises, as they provide to the analysts the view of all the critical business metrics that reflect the performance of the business. In this paper, we claim that dashboard development can be fast and easy, while maintaining flexibility in the design, and without sacrificing versatility or performance. We propose a framework for dashboard design that is model-driven.


Software Cost Estimation (SCE), Dashboard Framework, COCOMO I, COCOMO II, Source Line of Code (SLOC).

Full Text:



Nikolaos Mittas and Lefteris Angelis Ranking and Clustering Software CostEstimation Models through a Multiple Comparisons Algorithm”

N. Mittas and L. Angelis, “Comparing Cost Prediction Models by Resampling Techniques,” J. Systems and Software, vol. 81, no. 5, pp. 616-632, May 2008.

Buse, Raymond PL, and Thomas Zimmermann. "Analytics for software development." In Proceedings of the FSE/SDP workshop on Future of software engineering research, pp. 77-80. ACM, 2010.

B. Kitchenham and E. Mendes, “Why Comparative Effort Prediction Studies May Be Invalid,” Proc. ACM Fifth Int’l Conf. Predictor Models in Software Eng., pp. 1-5, May 2009.

S. Lessmann, B. Baesens, C. Mues, and S. Pietsch, “Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings,” IEEE Trans. Software Eng., vol. 34, no. 4, pp. 485-496, July/Aug. 2008.

J. Antony, Design of Experiments for Engineers and Scientists. Butterworth-Heinenmann, 2003.

Simon, Daniel, Kai Fischbach, and Detlef Schoder. "Application portfolio management--an integrated framework and a software tool evaluation approach."Communications of the Association for Information Systems 26, no. 1 (2010): 3.

J. Sayyad Shirabad and T. Menzies, “The PROMISE Repository of Software Engineering Databases,” School of Information Technology and Eng., Univ. of Ottawa, SERepository. 2005.

Sweta Kumari , Shashank Pushkar ,” Performance Analysis of the Software Cost Estimation Methods: A Review”Volume 3, Issue 7, July 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper

COCOMO II Model definition manual, version 1.4, University of Southern California.

Caper Jones, ―Estimating software cost tata Mc- Graw -Hill Edition 2007.

Liming Wu ―The Comparison of the Software Cost Estimating Methods University of Calgary.

Dudley, Joel T., Yannick Pouliot, Rong Chen, Alexander A. Morgan, and Atul J. Butte. "Translational bioinformatics in the cloud: an affordable alternative."Genome medicine 2, no. 8 (2010): 51.Oscar Marbán, Antonio de Amescua, Juan J. Cuadrado, Luis García ―A cost model to estimate the effort of data mining projects, Universidad Carlos III deMadrid (UC3M),Volume33, Issue 1, pp.133-150, March, 2008

Magne Jorgensen, “Realism in Assessment of Effort Estimation Uncertainty: It Matters How you ask”, IEEE Transaction on Software Engineering, vol. 30, no. 4, 2004

Bhowmick Kiran, “Integration of AI models with empirical Models for software estimation”, M.Tech. Thesis. NMIMS university, 2008.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.