Machine Learning Based Approach for Evaluating Agile Based Methods to Enhance Software Quality

Neha Saini, Prof. Indu Chhabra, Dr. Ajay Guleria
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引用次数: 0

Abstract

Developing a quality software product is an essential need of the software industry. Software quality comprises of various factors. Therefore, it cannot be measured on the basis of a single variable. Several agile software development methods have evolved all around the world with the passage of time that contribute towards the development of new and improved software methods. The agile processes have started invading the software development industry to provide good quality software in minimal time. As the changes have occurred in the modern day evaluation metrics, the changes have been observed in the agile oriented quality evaluation methods as well. This paper presents a machine learning based approach for evaluating agile based methods for enhancing software quality. This advanced mechanism of processing the data attributes is inspired by SWARA and FDD. The validation and evaluation has been done using statistical and the quantitative parameters.
基于机器学习的评估敏捷方法以提高软件质量
开发高质量的软件产品是软件行业的基本需求。软件质量由多种因素组成。因此,它不能在单一变量的基础上进行测量。随着时间的推移,一些敏捷软件开发方法在世界范围内得到了发展,这些方法有助于开发新的和改进的软件方法。敏捷过程已经开始侵入软件开发行业,以便在最短的时间内提供高质量的软件。随着现代评估指标的变化,面向敏捷的质量评估方法也发生了变化。本文提出了一种基于机器学习的方法来评估基于敏捷的软件质量提升方法。这种处理数据属性的高级机制受到了SWARA和FDD的启发。利用统计参数和定量参数进行了验证和评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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