Metrics for evaluation of metaprogram complexity

The concept of complexity is used in many areas of computer science and software engineering. Software complexity metrics can be used to evaluate and compare quality of software development and maintenance processes and their products. Complexity management and measurement is especially important in...

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Bibliographic Details
Published inComputer Science and Information Systems Vol. 7; no. 4; pp. 769 - 787
Main Authors Damasevicius, Robertas, Stuikys, Vytautas
Format Journal Article
LanguageEnglish
Published 01.12.2010
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Summary:The concept of complexity is used in many areas of computer science and software engineering. Software complexity metrics can be used to evaluate and compare quality of software development and maintenance processes and their products. Complexity management and measurement is especially important in novel programming technologies and paradigms, such as aspect-oriented programming, generative programming, and metaprogramming, where complex multilanguage and multi-aspect program specifications are developed and used. This paper analyzes complexity management and measurement techniques, and proposes five complexity metrics (Relative Kolmogorov Complexity, Metalanguage Richness, Cyclomatic Complexity, Normalized Difficulty, Cognitive Difficulty) for measuring complexity of metaprograms at information, metalanguage, graph, algorithm, and cognitive dimensions. nema
ISSN:1820-0214
2406-1018
DOI:10.2298/CSIS090315004D