Software engineering: a practitioner's approach (2nd ed.)
Software engineering: a practitioner's approach (2nd ed.)
An Empirical Study of Software Metrics
IEEE Transactions on Software Engineering
Software complexity assessment: annotated bibliography
ACM SIGSOFT Software Engineering Notes
A simple measure of software complexity
ACM SIGPLAN Notices
Decompilation of Control Structures by Means of Graph Transformations
CAAP '85 Proceedings of the International Joint Conference on Theory and Practice of Software Development (TAPSOFT), Volume 1: Colloquium on Trees in Algebra and Programming: Mathematical Foundations of Software Development
Software metrics, measurement theory, and viewpoints
ACM SIGPLAN Notices
A first step towards an evaluation framework taxonomy
ACM SIGOIS Bulletin
The mathematical validity of software metrics
ACM SIGSOFT Software Engineering Notes
IEEE Transactions on Software Engineering
Foundations of object-oriented software measures
METRICS '96 Proceedings of the 3rd International Symposium on Software Metrics: From Measurement to Empirical Results
Perceived vs. measured quality of conceptual schemas: an experimental comparison
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
Evaluating the adaptivity of computing systems
Performance Evaluation
Hi-index | 0.00 |
Over the last decade many software metrics have been introduced by researchers and many software tools have been developed using software metrics to measure the "quality" of programs. These metrics for measuring productivity, reliability, maintainability, and complexity, for example, are vital to software development planning and management. In this paper a new approach is presented to describe the properties of the software metrics and their scales using measurement theory. Methods are shown to describe a software complexity metric as an ordinal, an interval or a ratio scale. The use of this concept is shown by application to the Metric of McCabe. These results are very important for selecting appropriate software metrics for software measurement and for developing tools which use software metrics to evaluate the "quality" of software.