Machine Learning Model to Predict Automated Testing Adoption

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Software Development ◽  
Software Testing ◽  
Model Development ◽  
Learning Model ◽  
Development Project ◽  
Automated Testing ◽  
Machine Learning Model ◽  
Manual Testing ◽  
Automation Testing

Software testing is an activity conducted to test the software under test. It has two approaches: manual testing and automation testing. Automation testing is an approach of software testing in which programming scripts are written to automate the process of testing. There are some software development projects under development phase for which automated testing is suitable to use and other requires manual testing. It depends on factors like project requirements nature, team which is working on the project, technology on which software is developing and intended audience that may influence the suitability of automated testing for certain software development project. In this paper we have developed machine learning model for prediction of automated testing adoption. We have used chi-square test for finding factors’ correlation and PART classifier for model development. Accuracy of our proposed model is 93.1624%.

Brazilian Data Scientists: Revealing their Challenges and Practices on Machine Learning Model Development

2020 ◽  
Author(s):  
João Lucas Correia ◽  
Juliana Alves Pereira ◽  
Rafael Mello ◽  
Alessandro Garcia ◽  
Baldoino Fonseca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Learning Model ◽  
Machine Learning Model

scholarly journals Machine learning model development for predicting aeration efficiency through Parshall flume

2021 ◽  
Vol 15 (1) ◽  
pp. 889-901
Author(s):  
Sangeeta ◽  
Seyed Babak Haji Seyed Asadollah ◽  
Ahmad Sharafati ◽  
Parveen Sihag ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Learning Model ◽  
Machine Learning Model ◽  
Aeration Efficiency

scholarly journals PHOTONAI—A Python API for rapid machine learning model development

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254062
Author(s):  
Ramona Leenings ◽  
Nils Ralf Winter ◽  
Lucas Plagwitz ◽  
Vincent Holstein ◽  
Jan Ernsting ◽  
...  
Keyword(s):  
Machine Learning ◽  
External Validation ◽  
Model Development ◽  
Learning Model ◽  
Complex Data ◽  
Algorithm Selection ◽  
Iterative Model ◽  
Machine Learning Model ◽  
High Level ◽  
Learning Analysis

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.

Sign in / Sign up

Export Citation Format

Share Document