Improving Software Automation Testing Using Jenkins, and Machine Learning Under Big Data

2018 ◽  
pp. 87-96 ◽  
Author(s):  
Ali Stouky ◽  
Btissam Jaoujane ◽  
Rachid Daoudi ◽  
Habiba Chaoui
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Software Automation Testing ◽  
Software Automation ◽  
Automation Testing

Software Automation Testing using LeanFT Tool

2020 ◽  
Vol 9 (12) ◽  
pp. 134-143
Keyword(s):  
Web Application ◽  
Tool Support ◽  
Functional Testing ◽  
Test Environment ◽  
Short Period ◽  
Testing Tool ◽  
Software Automation Testing ◽  
Software Automation ◽  
Automation Testing

Software industries are giving more importance for to developing, maintaining, servicing and delivering the best quality of software products always to the customer. To achieve this goal software industry’s main objective is having the best quality of software testing. Nowadays the automation testing activity is performing the big role in the software industry. Automation testing tools are makes easier to identify the bugs during the regression testing by running them n number of times and rectified with all effective manners within short period of time with low cost expenses. Here we are focusing on to make use of the latest and having advanced feature in LeanFTv14.52 (Lean functional Testing) software automation testing tool in our automation testing project, This LeanFT tool is been introduced after the UFT Developer (Unified Functional Testing) tool from the Micro Focus microfocus.com.Software test automation user will get more benefited in his automation scripting activity by using this LeanFT software automation testing tool as it is having the new benefited features for to testing the application by consolidated with high level of tool support, having better customization support as per the project requirement and perfect compatibility support as per the test environment requirement, technical help from customer care support to customize the tool as per the testing application needs in all possibilities by comparing to other software automation testing tool.LeanFT software automation testing tool can be used to accelerate test with intelligent solution for more than 200 technologies across web application, standalone application, mobile application, mainframe application, SAP, Salesforce, Java, PDF, Citrix, API(Application Programming Interface), RPA(Robotic Process Automation) and enterprise apps also including financial and highly secured and confidential applications are can be tested.

A Cognitive Approach in Software Automation Testing

2021 ◽  
Author(s):  
Abdus Samad ◽  
Tabrez Nafis ◽  
Shibli Rahmani ◽  
Shahab Saquib Sohail
Keyword(s):  
Cognitive Approach ◽  
Software Automation Testing ◽  
Software Automation ◽  
Automation Testing

The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

2020 ◽  
Author(s):  
Turan G. Bali ◽  
Amit Goyal ◽  
Dashan Huang ◽  
Fuwei Jiang ◽  
Quan Wen
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Corporate Bonds ◽  
Cross Sectional ◽  
The Cross

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

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