Models and Measurements for Quality Assessment of Software
ACM Computing Surveys (CSUR)
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
A model for program complexity analysis
ICSE '78 Proceedings of the 3rd international conference on Software engineering
Psychological complexity of computer programs: an experimental methodology
ACM SIGPLAN Notices
Assessing the quality of programs: a topic for the CS2 course
SIGCSE '87 Proceedings of the eighteenth SIGCSE technical symposium on Computer science education
Evaluating Software Complexity Measures
IEEE Transactions on Software Engineering
An Empirical Study of a Model for Program Error Prediction
IEEE Transactions on Software Engineering
Cyclomatic Complexity Density and Software Maintenance Productivity
IEEE Transactions on Software Engineering
An empirical study of a model for program error prediction
ICSE '85 Proceedings of the 8th international conference on Software engineering
Software engineering economics
Software pioneers
Complexity measures for assembly language programs
Journal of Systems and Software
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The program complexity measure currently seems to be the most capable measure for both quantitative and objective control of the software project. Five program complexity measures (step count, McCabe's V(G), Halstead's E, Weighted Statement Count and Process V(G)) were assessed from such a viewpoint. This empirical study was done with the data collected through a practical software project. All of these measures have highly significant correlations with the management data. Application of complexity measures to software development management is discussed and a method for the detection of anomalous modules in a program is proposed.