The Community for Technology Leaders
RSS Icon
Issue No.01 - January/February (2009 vol.26)
pp: 41-49
Richard W. Selby , Northrop Grumman Space Technology
Mining software repositories using analytics-driven dashboards provides a unifying mechanism for understanding, evaluating, and predicting the development, management, and economics of large-scale systems and processes. Dashboards enable measurement and interactive graphical displays of complex information and support flexible analytic capabilities for user customizability and extensibility. Dashboards commonly include system requirements and design metrics because they provide leading indicators for project size, growth, and volatility. This article focuses on dashboards that have been used on actual large-scale software projects as well as example empirical relationships revealed by the dashboards. The empirical results focus on leading indicators for requirements and designs of large-scale software systems based on insights from two sets of software projects containing 14 systems and 23 systems.
leading indicators, requirements, designs, defects, empirical analysis, metrics
Richard W. Selby, "Analytics-Driven Dashboards Enable Leading Indicators for Requirements and Designs of Large-Scale Systems", IEEE Software, vol.26, no. 1, pp. 41-49, January/February 2009, doi:10.1109/MS.2009.4
1. F.P. Brooks, The Mythical Man-Month, Addison-Wesley, 1975.
2. G. Bockle et al., "Calculating ROI for Software Product Lines," IEEE Software, vol. 21, no. 3, 2004, pp. 23–31.
3. B.W. Boehm, Software Engineering Economics, Prentice Hall 1981.
4. J. Klein, B. Price, and D. Weiss, "Industrial-Strength Software Product-Line Engineering," Proc. 25th Int'l Conf. Software Eng. (ICSE 03), IEEE CS Press, 2003, pp. 751–752.
5. A. Engel and S. Shachar, "Measuring and Optimizing Systems' Quality Costs and Project Duration," Systems Engineering, vol. 9, no. 3, 2006, pp. 259–280.
6. R.W. Selby et al., "Metric-Driven Analysis and Feedback Systems for Enabling Empirically Guided Software Development," Proc. 13th Int'l Conf. Software Eng. (ICSE 91), ACM Press, 1991, pp. 288–298.
7. R.W. Selby and A.A. Porter, "Learning from Examples: Generation and Evaluation of Decision Trees for Software Resource Analysis," IEEE Trans. Software Eng., vol. se-14, no. 12, 1988, pp. 1743–1757.
8. H. Scheffe, The Analysis of Variance, John Wiley &Sons, 1959.
9. B. Boehm, "Industrial Software Metrics Top 10 List," IEEE Software, vol. 4, no. 5, 1987, pp. 84–85.
10. L.J. Osterweil, "Software Processes Are Software Too," Proc. 9th Int'l Conf. Software Eng. (ICSE 87), ACM Press, 1987, pp. 2–13.
11. R. Smaling and O. de Weck, "Assessing Risks and Opportunities of Technology Infusion in System Design," Systems Eng., vol. 10, no. 1, 2007, pp. 1–25.
12. W.S. Humphrey, "Characterizing the Software Process: A Maturity Framework," IEEE Software, vol. 5, no. 2, 1988, pp. 73–79.
13. J. Bosch, "Architecture-Centric Software Engineering," Proc. 24th Int'l Conf. Software Eng. (ICSE 02), ACM Press, 2002, pp. 681–682.
14. D. Harel, "Statecharts: A Visual Formulation for Complex Systems," Science of Computer Programming, vol. 8, no. 3, 1987, pp. 231–274.
15. Software Engineering Laboratory: Database Organization and User's Guide, Revision 1, tech. report SEL-81-102, Software Eng. Laboratory, NASA/Goddard Space Flight Center, July 1983.
16. R.W. Selby, "Enabling Reuse-Based Software Development of Large-Scale Systems," IEEE Trans. Software Eng., vol. se-31, no. 6, 2005, pp. 495–510.
25 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool