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AUTHOR(S):

Ekbal Rashid, Nikos Mastorakis

 

TITLE

Applying Artificial Intelligence Analysis in Software Engineering for Considering Popularity as a quality parameter

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ABSTRACT

The utility of artificial intelligence in Software Engineering opens the technique for a period of renovation in which the ability to improve effectiveness of software parameters in software engineering. On the other AI can be utilized for making revolutionary changes in software quality parameters and add new parameters such as popularity as a quality parameter. This paper is a discussion about popularity as a novel parameter in a software project quality. In this research paper. We are considering the popularity of a particular software project to be a parameter of the quality of that software project. This discussion is based on numerous previous studies that have explored this issue in detail. The authors have explored several scholarly writings in great depth and details. At the same time many industries’ white papers have also been studied to understand the trend that is being used in the industry at the present time. Going through all such study, the authors are on the verge of concluding that popularity of a particular software project may outweigh several other quality parameters in one sense. Rather, it would not be a mistake to look at the popularity of software and guess its quality. This popularity of a particular software project can be determined in a number of ways starting from its rating in the software repository site to the number of downloads or the number of forks related to it. The present paper stresses on the popularity issue and also highlights several ways to measure the popularity of a particular software project.

KEYWORDS

Artificial Intelligence, Popularity, quality parameter, security, forks, stars

 

Cite this paper

Ekbal Rashid, Nikos Mastorakis. (2024) Applying Artificial Intelligence Analysis in Software Engineering for Considering Popularity as a quality parameter. International Journal of Education and Learning Systems, 9, 7-12

 

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