Authors :
Mohd. Nazim; Tanveer Hassan; Chaudhary Wali Mohammad; Mohd. Sadiq
Volume/Issue :
Volume 7 - 2022, Issue 9 - September
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3z4ns6u
DOI :
https://doi.org/10.5281/zenodo.7237972
Abstract :
Software requirements selection is one of the
key activities of the software development process. In
this activity the requirements are selected based on their
ranking order. Various methods have been developed for
selecting the requirements using fuzzy logic, rough set
theory, and Metaheuristic algorithms, etc. One of the
limitations of the fuzzy based methods is that the
membership functions of fuzzy numbers are manually
decided by the decision makers. In these methods less
attention is given to the automated generation of fuzzy
membership function. To address this issue, this paper
presents a method for the selection of software
requirements in which genetic algorithm has been used
for automatically generating the fuzzy numbers. In the
proposed method, a random population of size 15 is
initialized then the process of reproduction is started by
using the selection, crossovers, and mutation operators of
genetic algorithm for 23 generations. The best
chromosome with the fitness value of 0.883333333 is
selected from the last generation as an input in fuzzy
TOPSIS method. The applicability of the proposed
method is discussed by the requirements of an institute
examination system.
Keywords :
Functional requirements; Fuzzy TOPSIS; Genetic algorithm; Fitness value; Crossover; Mutation; Population; Institute Examination System.
Software requirements selection is one of the
key activities of the software development process. In
this activity the requirements are selected based on their
ranking order. Various methods have been developed for
selecting the requirements using fuzzy logic, rough set
theory, and Metaheuristic algorithms, etc. One of the
limitations of the fuzzy based methods is that the
membership functions of fuzzy numbers are manually
decided by the decision makers. In these methods less
attention is given to the automated generation of fuzzy
membership function. To address this issue, this paper
presents a method for the selection of software
requirements in which genetic algorithm has been used
for automatically generating the fuzzy numbers. In the
proposed method, a random population of size 15 is
initialized then the process of reproduction is started by
using the selection, crossovers, and mutation operators of
genetic algorithm for 23 generations. The best
chromosome with the fitness value of 0.883333333 is
selected from the last generation as an input in fuzzy
TOPSIS method. The applicability of the proposed
method is discussed by the requirements of an institute
examination system.
Keywords :
Functional requirements; Fuzzy TOPSIS; Genetic algorithm; Fitness value; Crossover; Mutation; Population; Institute Examination System.