Software complexity measurement
Communications of the ACM
An Empirical Study of Software Metrics
IEEE Transactions on Software Engineering
The visual display of quantitative information
The visual display of quantitative information
Software engineering metrics and models
Software engineering metrics and models
Software metrics: an overview of recent results
Journal of Systems and Software
The dimensionality of program complexity
ICSE '89 Proceedings of the 11th international conference on Software engineering
A Framework for the Automated Drawing of Data Structure Diagrams
IEEE Transactions on Software Engineering
A mathematical perspective for software measures research
Software Engineering Journal
Software complexity: measures and methods
Software complexity: measures and methods
Nonparametric methods for quantitative analysis (3rd ed.)
Nonparametric methods for quantitative analysis (3rd ed.)
Software Metrics: A Rigorous Approach
Software Metrics: A Rigorous Approach
Graphical Representation of Multivariate Data
Graphical Representation of Multivariate Data
ICSE '94 Proceedings of the 16th international conference on Software engineering
Experiences with criticality predictions in software development
ESEC '97/FSE-5 Proceedings of the 6th European SOFTWARE ENGINEERING conference held jointly with the 5th ACM SIGSOFT international symposium on Foundations of software engineering
IEEE Transactions on Software Engineering
Evaluation and Application of Complexity-Based Criticality Models
METRICS '96 Proceedings of the 3rd International Symposium on Software Metrics: From Measurement to Empirical Results
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One-dimensional statistical methods of scaling have been employed to present a distinct subjective criterion that is related to a measurable aspect of a software component. However, different aspects being measured and different software components being analyzed usually have some characteristics in common. Selected techniques for graphical representation permit a brief but nevertheless thorough view of complex relations among complicated sets of data. Several methods of visualizing and analyzing multidimensional data sets are presented and discussed. The underlying goals of such techniques are to find unknown structures and dependencies among measures, to represent different data sets in order to improve communication and comparability of distinct analyses, and to decrease visual complexity. For improved understandability of the statistical and related graphical concepts, a small set of design aspects from a real-world example is introduced. The techniques illustrated are applied to the same set of data and compared.