An empirical validation of software cost estimation models
Communications of the ACM
A mathematical perspective for software measures research
Software Engineering Journal
Software engineering with formal metrics
Software engineering with formal metrics
Reliance on correlation data for complexity metric use and validation
ACM SIGPLAN Notices
Methodology for Validating Software Metrics
IEEE Transactions on Software Engineering
A validation of software metrics using many metrics and two resources
ICSE '85 Proceedings of the 8th international conference on Software engineering
Software Metrics: A Rigorous Approach
Software Metrics: A Rigorous Approach
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
An approach for pattern oriented software development based on a design handbook
Annals of Software Engineering
An Investigation into the Applicability and Validity ofObject-Oriented Design Metrics
Empirical Software Engineering
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The goal of software metrics is the improvement of the software process. Five principles are fundamental to the formal validation of models of software metrics:--Attribute Type--what attribute(s) of software behavior are we measuring?;The mathematical postulates of a measure function--a model must have the utility for comparison; this unique criterion must be passed by every effective model of measure;Metrical Vindication--applying the model directly to measure actual software module(s); how consistent and reliable are the results of the measures?;Feedback Effect--how does the metric help us to identify faults and errors (of design) or improve testing and maintenance; what is a metric if it is just a number?;Units of Measure--what are we measuring with respect to dimensions: size, length/depth, extent, degree of variation, degradation, etc.?; a measure without unit(s) is like a building without a roof.Without principles, there is nothing to validate. Every good science is a body of relevant principles. Some worked examples on some of these principles are presented. Some of these examples in a way, expose the failures or frustrations resulting from the applications of some models of measure presently being used in industry and science. Not every mathematical function can be used as a model of measurement.