Predictive Analytics and Machine Learning for Quality Assurance

Posted By : Sanjana Singh | 31-May-2020


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. 




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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-


  1. Supervised Learning
  2. Unsupervised Learning
  3. Semi-Supervised Learning
  4. Semi- unsupervised Learning
  5. Reinforcement Machine Learning


Supervised Machine Learning for Quality Assurance


The approach of Testing that must be followed in Supervised Learning:


  1. Defining and Randomizing Datasets


To test a machine learning algorithm, three different datasets have been defined by the QAs:

  1. Training Dataset
  2. Validation Dataset
  3. 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.


4) Cross-Validation:


A technique of splitting datasets into multiple subsets is called Cross-Validation. On the basis of these subsets, the models are evaluated. One such example of these types of techniques is k-fold cross-validation. Cross-validation has been used to predict the performance of the machine learning models.



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5) Predictive Analysis


Predictive analysis is a part of Machine Learning which has been used to make predictions by analyzing present and past facts. Predictive analysis is helpful in identifying risks that may occur in the future. It specifies the probabilities of the future which may occur based on Current events. Abstracting information from the current and past data to predict the behavior of the particular pattern is the main concern of Predictive analysis. Predictive analysis techniques identify the relationship of variables and through that will be able to detect what will occur in the future from the occurrences of the past.


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