Information entropy applied to software based control flow graphs

Aditya Akundi (), Eric Smith and Tzu-Liang Tseng
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Aditya Akundi: The University of Texas at El Paso
Eric Smith: The University of Texas at El Paso
Tzu-Liang Tseng: The University of Texas at El Paso

International Journal of System Assurance Engineering and Management, 2018, vol. 9, issue 5, No 9, 1080-1091

Abstract: Abstract Information theory, introduced by Shannon in the context of information transfer in communication channels, is used as a foundation for research in many diverse fields. The concept Entropy in terms of information theory is seen as the average amount of information or the rate of information produced when forming a message, element by element. Entropy has found broad application in many research fields and can also be applied in software engineering for quantifying the uncertainty associated with a software code. In this paper, information entropy and its application towards measuring software complexity are explored, along with the formulation of an information entropy based complexity measure that considers logical decision-making, processes, and software statement interaction patterns in control flow graphs mapped from actual software code. To broaden the application of the proposed metric, the execution times of nodes in the control flow graphs are also incorporated. Further, the metric is evaluated against eight different axioms that a software complexity measure should satisfy.

Keywords: Complexity; Control flow graph; Entropy; Information theory; Software complexity (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s13198-018-0740-y

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