Software complexity assessment: annotated bibliography
ACM SIGSOFT Software Engineering Notes
Software metrics: an overview of recent results
Journal of Systems and Software
Evaluating Software Complexity Measures
IEEE Transactions on Software Engineering
A Study of the Applicability of Complexity Measures
IEEE Transactions on Software Engineering
The dimensionality of program complexity
ICSE '89 Proceedings of the 11th international conference on Software engineering
Elements of information theory
Elements of information theory
Methodology for Validating Software Metrics
IEEE Transactions on Software Engineering
An Entropy-Based Measure of Software Complexity
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Experimental software engineering: a report on the state of the art
Proceedings of the 17th international conference on Software engineering
Information theory and software measurement
Information theory and software measurement
Comments on "Towards a Framework for Software Measurement Validation"
IEEE Transactions on Software Engineering
Reply to: Comments on "Towards a Framework for Software Measurement Validation"
IEEE Transactions on Software Engineering
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)
Coping with Java Programming Stress
Computer
Towards a Framework for Software Measurement Validation
IEEE Transactions on Software Engineering
Software metrics: an introduction and annotated bibliography
ACM SIGSOFT Software Engineering Notes
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Information Sciences: an International Journal
Design and Analysis of Contracts for Software Outsourcing
Information Systems Research
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This paper presents an empirical case study that predicted faults in modules based on the total information content of the operators. This metric is closely related to Harrison's average information content classification (AICC), which is the entropy of the operators. Most information theory-based metrics proposed in the literature have not been subjected to empirical predictive studies of real-world software systems. In contrast, this study shows that a simple information theory-based metric can be more useful for prediction of software quality than comparable metrics based on counts in the context of a commercial software development organization.Three models were considered, all based on operators as an abstraction of software. The model based on information content of the operators made more accurate predictions than two similar models based on the number of operators and the number of unique operators. The purpose of this paper is a fair comparison of the three metrics, rather than developing an optimal model. We have long advocated multivariate models for industrial use. The case study considered three large commercial systems, written in assembly language, and developed consecutively by professional programmers. The first system was used to estimate parameters of the models. The subsequent two were used to evaluate the accuracy of model predictions.