Abstract
Measures of software complexity are essential part of software engineering. Complexity metrics can be used to forecast key information regarding the testability, reliability, and manageability of software systems from study of the source code. This paper presents the results of three distinct software complexity metrics that were applied to two searching algorithms (Linear and Binary search algorithm). The goal is to compare the complexity of linear and binary search algorithms implemented in (Python, Java, and C++ languages) and measure the sample algorithms using line of code, McCabe and Halstead metrics. The findings indicate that the program difficulty of Halstead metrics has minimal value for both linear and binary search when implemented in python. Analysis of Variance (ANOVA) was adopted to determine whether there is any statistically significant differences between the search algorithms when implemented in the three programming languages and it was revealed that the three (3) programming languages do not vary considerably for both linear and binary search techniques which implies that any of the (3) programming languages is suitable for coding linear and binary search algorithms.
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