Open AccessMachine Learning Techniques to Predict Defects by using Testing Parameters
Author(s) -
Prasanth Yalla*,
Venkata Naresh Mandhala,
Valavala Abhishiktha,
Chitturi Saisree,
Kandepi Manogna
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d5396.118419
Subject(s) - computer science , machine learning , process (computing) , artificial intelligence , variety (cybernetics) , software , software development , software engineering , programming language
Since ages, the software development plays a very crucial role in the arena of software engineering. An important part here is to believe that Artificial Intelligence and Machine Learning also started its way. In the process, several metrics were analyzed, composed and some predictions were made. These predictions are very much useful to analyze the defects based on machine learning. This can be done by using various system test parameters. We found certain techniques which are used to estimate the defects based on various aspects. These features are retrieved right from the inception of the software development. In this project, we present an advance view on wide variety of Machine Learning approaches, along with different capable areas of the defects by taking their parameters.