Machine Learning techniques and algorithms enable the system to work without human involvement. Machine learning for quality assurance processes involves models with the ability to collect and analyze data about inputs to generate outputs that are compared with the benchmarked results. The automated testing process is what makes machine learning development a cost and time-efficient solution for software development, manufacturing, pharmaceutical, and other industries.
Types of Algorithms in Machine Learning for Quality Assurance
Under the vast umbrella of artificial intelligence services , machine learning algorithms for quality testing approaches can be broadly classified into the following-
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Semi- unsupervised Learning
Reinforcement Machine Learning
Supervised Machine Learning for Quality Assurance
The approach of Testing that must be followed in Supervised Learning:
Defining and Randomizing Datasets
To test a machine learning algorithm, three different datasets have been defined by the QAs:
Training Dataset
Validation Dataset
Test Dataset
Test Data is a subset of the training dataset. Firstly these three datasets have been randomized and then split into the parts based on priority.
2) Working of the Datasets
Training Dataset has been used by the testers to train the models after defining and randomizing the data sets. Validation datasets will get in work after the training model process is done. The evaluation of these trained models has been performed by the validation datasets. This process will work in multiple iterations that are helpful in dealing with model-based changes. At the last Test, datasets have been used to test the already evaluated model.
3) Re-Evaluation of the models with Test Dataset:
After evaluating all the models with Training and Validation Datasets, the model which is having the minimum rate of errors and for which approximation prediction is high has been selected by the testers to perform testing on it through Test Dataset. The re-evaluation of the selected model has been performed to verify the stability of the model and to make sure that the model works as expected and matches the results of the Validation dataset.
Learn more: Predictive Analytics and Machine Learning for Quality Assurance