Markov approach for quantifying the software code coverage using genetic algorithm in software testing. (19th July 2019)

• Record Type: Journal Article
• Authors: Boopathi, M.; Sujatha, R.; Kumar, C. Senthil; Narasimman, S.; Rajan, A.
• Is Part Of: International journal of bio-inspired computation. Volume 14:Number 1(2019)
• Page Start: 27
• Page End: 45
• Publication Date: 2019-07-19
• Subjects: software testing -- test adequacy -- cyclomatic complexity -- Markov chain -- dd-graph -- genetic algorithm; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

Markov chain approach to quantify the coverage of dd-graph representing the software code using genetic algorithm (GA) is presented in this paper. Initially the dd-graph is captured from the control flow graph. In this technique, test software code coverage is carried out by applying GA through sufficient number of feasible linearly independent paths. These paths have been decided in a software code depending on computational uses and predicate uses. Automatic test cases have been produced for the three mixed data type variables namely, integer, float, Boolean and GA is applied. Transition probability of the Markov chain is attained from gcov coverage analysis tool of the initial test suite. Fitness function of GA is measured using path coverage metric; as the product of node coverage and TPM values. Highest fitness value represent the most critical paths among these independent paths with aim to increase testing efficiency of the software code.