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.