Comparative Study of Two Divide and Conquer Sorting Algorithms: Quicksort and Mergesort

Taiwo, Oladipupo Esau; Abikoye, Oluwakemi Christiana; Oluwatobi, Akande Noah; Aderonke, Kayode Anthonia; Kehinde, Adeniyi Jide

doi:10.1016/j.procs.2020.04.274

Cited by 6 publications

(1 citation statement)

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“…The quick sort and merge sort algorithms have been widely used for sorting, according to (Taiwo et al, 2020). However, determining which is the most efficient has always been a contentious issue because most of the existing literature has compared these algorithms using machine-dependent factors such as computational complexity.…”

Section: Merge Sort Algorithmmentioning

confidence: 99%

Halstead’s Complexity Measure of a Merge sort and Modified Merge sort Algorithms

Balogun,

Abdulraheem,

Sadiku

et al. 2023

JJi

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Complexity measuring tools in computer science are deployed to measure and compare different characteristics of algorithms in order to find the best one in solving a particular problem or that suits a particular situation. In view of this, Halstead’s complexity metrics is deployed to compare the efficiency of two external sorting methods; the Merge sort and the Modified Merge sort algorithms. The methodology used in achieving this, lies in the extraction of operators and operands from the C_sharp (C#) implemented program of the two algorithms. Six Halstead metrics are evaluated using these operators and operands as parameters. The results shows that the Modified Merge sort algorithm is much more efficient than the conventional Merge sort as it has a lower Program Volume, lower Program difficulty and a lower Program Effort even though the advantage of a higher Intelligence content goes to the Merge sort algorithm.

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Section: Merge Sort Algorithmmentioning

confidence: 99%