Publication Type
Journal Article
Version
publishedVersion
Publication Date
3-1994
Abstract
Measurement of software development productivity is needed in order to control software costs, but it is discouragingly labor-intensive and expensive. Computer-aided software engineering (CASE) technologies-especially repository-based, integrated CASE-have the potential to support the automation of this measurement. We discuss the conceptual basis for the development of automated analyzers for function point and software reuse measurement for object-based CASE. Both analyzers take advantage of the existence of a representation of the application system that is stored within an object repository, and that contains the necessary information about the application system. We also discuss metrics for software reuse measurement, including reuse leverage, reuse value, and reuse classification that are motivated by managerial requirements and the efforts, within industry and the IEEE, to standardize measurement. The functionality and the analytical capabilities of state-of-the-art automated software metrics analyzers are illustrated in the context of an investment banking industry application that is similar to systems deployed at the New York City-based investment bank where these tools were developed and tested
Keywords
bank data processing, object-oriented programming, software metrics, software reusability, software tools
Discipline
Computer Sciences | Numerical Analysis and Scientific Computing
Research Areas
Information Systems and Management
Publication
IEEE Transactions on Software Engineering
Volume
20
Issue
3
First Page
169
Last Page
187
ISSN
0098-5589
Identifier
10.1109/32.268919
Publisher
IEEE
Citation
BANKER, R. D.; Kauffman, Robert J.; Wright, C.; and Zweig, D..
Automating output size and reuse metrics in a repository-based computer-aided software engineering (CASE) environment. (1994). IEEE Transactions on Software Engineering. 20, (3), 169-187.
Available at: https://ink.library.smu.edu.sg/sis_research/2155
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/32.268919