Metrics for evaluation of metaprogram complexity

Abstract

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.