This Article 
 Bibliographic References 
 Add to: 
Software Metrics Knowledge and Databases for Project Management
January/February 1999 (vol. 11 no. 1)
pp. 255-264

Abstract—Construction and maintenance of large, high-quality software projects is a complex, error-prone, and difficult process. Tools employing software database metrics can play an important role in efficient execution and management of such large projects. In this paper, we present a generic framework to address this problem. This framework incorporates database and knowledge-base tools, a formal set of software test and evaluation metrics, and a suite of advanced analytic techniques for extracting information and knowledge from available data. The proposed combination of critical metrics and analytic tools can enable highly efficient and cost-effective management of large and complex software projects. The framework has potential for greatly reducing venture risks and enhancing the production quality in the domain of large software project management.

[1] "Department of the Army Pamphlet (DA Pam) 73-1," Part 7, Software T&E Guidelines, Sept. 1992.
[2] V. Basili and D.M. Weiss, "A Methodology for Collecting Valid Software Engineering Data," IEEE Trans. Software Eng., vol. 10, no. 6, pp. 728-738, Nov. 1984.
[3] L.A. Belady, "Software is the Glue in Large Systems," IEEE Comm., vol. 27, no. 8, pp. 33-36, Aug. 1989.
[4] B. Boehm, Software Economics, 1991.
[5] N.E. Fenton, Software Metrics, A Rigorous Approach. Chapman&Hall, 1991.
[6] A. Goel, K. Ozturk, and R. Paul, "Statistical Analysis of Metrics Data," U.S. Army OPTECH Technical Report 94-1, Jan. 1994.
[7] R.B. Grady, “Successfully Applying Software Metrics,” IEEE Computer, vol. 27, no. 9, pp. 18–25, Sept. 1994.
[8] S. Grey, "Project Cost and Schedule Risk Analysis," Digest Colloqium Risk Analysis Methods and Tools, no. 134, London, IEE, pp. 7/1-4, 1992.
[9] D.L. Lanning, “Modeling the Relationship Between Source Code Complexity and Maintenance Difficulty,” Computer, vol. 27, no. 9, Sept. 1994.
[10] R.K. Madsen and R.W. Selby, "Metric Driven Classification Models for Analyzing Large-Scale Software," Arcadia Technical Report UCI-91-0, Dept. of Computer Science, Univ. of California, Irvine, Jan. 1991.
[11] S.G. Mallat,“A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, 1989.
[12] NASA Johnson Space Center, DA3 Software Development Metrics Handbook, Version 2.1, JSC-25519, Houston, 1992.
[13] M. Ouyang and M. Golay, "An Integrated Formal Approach for Developing High Quality Software of Safety-Critical Systems," Technical Report MIT-ANP-TR-035, Massachusetts Inst. of Tech nology, Cambridge, Mass., 1991.
[14] A. Porter and R. Selby, "Empirically Guided Software Development Using Metric-Based Classification Trees," IEEE Software, no. 7, pp. 46-54, 1990.
[15] H.D. Rombach and B.T. Ulery, “Improving Software Maintenance through Measurement,” Proc. IEEE, vol. 77, no. 4, pp. 581-595, Apr. 1989.
[16] "Software Engineering Tools Experiment," final report, vol. 1, Dept. of Defense, Strategic Defense Initiative, Washington, D.C., 1990.
[17] G. Stark and R.C. Durst, "Using Metrics in Management Decision Making," Computer, pp. 42-48, Sept. 1994.
[18] I. Thomas and B. Nejmeh, "Definitions of Tool Integration for Environments," IEEE Software, pp. 29-34, Mar. 1992.
[19] E.F. Weller, "Lessons from Three Years of Inspection Data," IEEE Software, pp. 38-45, Sept. 1993.
[20] W. Zage and M. Zage, "Evaluating Design Metrics on Large Scale Software," IEEE Software, vol. 10, no. 4, pp. 75-80, July 1993.

Index Terms:
Knowledge-based tools for software project management, efficiency and robustness in large-scale software development, knowledge abstraction from raw data, software metrics databases, test and evaluation, cost-effective project management.
Raymond A. Paul, Tosiyasu L. Kunii, Yoshihisa Shinagawa, Muhammad F. Khan, "Software Metrics Knowledge and Databases for Project Management," IEEE Transactions on Knowledge and Data Engineering, vol. 11, no. 1, pp. 255-264, Jan.-Feb. 1999, doi:10.1109/69.755633
Usage of this product signifies your acceptance of the Terms of Use.