A Review and Evaluation of Software Science
ACM Computing Surveys (CSUR)
Studying programmer behavior experimentally: the problems of proper methodology
Communications of the ACM
Some results from an empirical study of computer software
ICSE '79 Proceedings of the 4th international conference on Software engineering
IEEE Transactions on Software Engineering
An Experiment in Software Error Data Collection and Analysis
IEEE Transactions on Software Engineering
Chief programmer team management of production programming
IBM Systems Journal
Measuring programming quality and productivity
IBM Systems Journal
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As the cost of programming becomes a major component of the cost of computer systems, it becomes imperative that program development and maintenance be better managed. One measurement a manager could use is programming complexity. Such a measure can be very useful if the manager is confident that the higher the complexity measure is for a programming project, the more effort it takes to complete the project and perhaps to maintain it. Until recently most measures of complexity were based only on intuition and experience. In the past 3 years two objective metrics have been introduced, McCabe's cyclomatic number v(G) and Halstead's effort measure E. This paper reports an empirical study designed to compare these two metrics with a classic size measure, lines of code. A fourth metric based on a model of programming is introduced and shown to be better than the previously known metrics for some experimental data.