Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Some results from an empirical study of computer software
ICSE '79 Proceedings of the 4th international conference on Software engineering
Third time charm: Stronger prediction of programmer performance by software complexity metrics
ICSE '79 Proceedings of the 4th international conference on Software engineering
Analyzing medium-scale software development
ICSE '78 Proceedings of the 3rd international conference on Software engineering
IEEE Transactions on Software Engineering
The program dependence graph in a software development environment
SDE 1 Proceedings of the first ACM SIGSOFT/SIGPLAN software engineering symposium on Practical software development environments
Metrics in software quality assurance
ACM '81 Proceedings of the ACM '81 conference
Software size prediction before coding
ACM SIGSOFT Software Engineering Notes
Complexity metrics for manufacturing control architectures based on software and information flow
Computers and Industrial Engineering
Complexity metrics for manufacturing control architectures based on software and information flow
Computers and Industrial Engineering
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There has appeared in the literature a great number of metrics that attempt to measure the effort or complexity in developing and understanding software(1). There have also been several attempts to independently validate these measures on data from different organizations gathered by different people(2). These metrics have many purposes. They can be used to evaluate the software development process or the software product. They can be used to estimate the cost and quality of the product. They can also be used during development and evolution of the software to monitor the stability and quality of the product. Among the most popular metrics have been the software science metrics of Halstead, and the cyclomatic complexity metric of McCabe. One question is whether these metrics actually measure such things as effort and complexity. One measure of effort may be the time required to produce a product. One measure of complexity might be the number of errors made during the development of a product. A second question is how these metrics compare with standard size measures, such as the number of source lines or the number of executable statements, i.e., do they do a better job of predicting the effort or the number of errors? Lastly, how do these metrics relate to each other?