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Improve QA Efficiencies using Artificial Intelligence (AI) and Machine Learning

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The Role of AI and ML in Testing

AI and ML testing framework can efficiently recognize pitfalls and with constant updates to the algorithms, it is feasible to discover even the negligible error. Essentially, Artificial Intelligence (AI) and Machine Learning (ML) tech are well-trained to process data, identify schemes and patterns, form and evaluate tests without human support. This is made possible with deep learning and artificial neural networks when a machine self-educates based on the given data sets or data extracted from an external source such as the web.

The role of artificial intelligence in software testing

Major Approaches for AI and ML Implementation in Software Testing

  • To train the Artificial Intelligence (AI) and Machine Learning (ML) to build automated tests. Few attempts have been made in this scenario with varied success
  • To instruct Artificial Intelligence (AI) to organize tests, deciding autonomously on what needs to be run, what needs to be fixed, and what to remove
  • AI is shaping the future of software testing. It is projected that in the coming future these new-edge technologies will enhance testing in several ways.
  • Identifying any changes in software and defining whether it is a bug or an additional feature that should be tested
  • Identifying any changes in software and defining whether it is a bug or an additional feature that should be tested
  • The inclusion of Artificial Intelligence and Machine Learning quickly detecting software changes by inspecting history logs and correlating them with the test outcomes
  • Prioritizing test cases. Creating dashboards to unite and share data on tested code, current testing statuses, and test coverage.
  • Fixing tests on the run in case a certain element is not discovered
  • Speeding-up maintenance and test runs
  • Predicting and timely notifying about possible code or test issues
  • Analyzing code to estimate test coverage

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Testing Strategies for AI / ML System and Applications

As the biggest buzzwords of our era, they restore a strong faith in a highly advanced future for almost every realm. While we also predict that AI (XAI) and Auto-ML techniques will significantly improve testing efficiency going forward. Let us discuss about some methods that will need to be used in real-life software testing from a model and data set viewpoint.

AI/ML insights and processes help optimize overall testing on what is right, rather than just testing more. AI-based automated tests improve the quality of the test case and additionally, such Quality Engineering services reduce the cost, time, and scalability deficiencies of old testing approaches.

  • Testing Separate Cognitive Features
  • Natural Language Processing
  • Speech Recognition Inputs
  • Optical Character Recognition
  • Image Recognition
  • Testing AI-Powered Solutions
  • Chatbot Testing Framework
  • RPA Tests Framework
  • Testing AI Plateforms
  • Data Source and Conditioning Tests
  • System/Regression Tests
  • Algorithm Tests
  • API Integration
  • Testing ML Models
  • Dual Coding/Algorithm Ensemble
  • Model Performance Testing
  • Coverage Guided Fuzzing
  • Metamorphic Testing
  • Testing Analytical Models
  • Dataset Split and Generation
  • Model Evaluation
  • Tests Reporting
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How ImpactQA make AI/ML Testing more Intelligent?

Our approach to artificial intelligence and machine learning powered QA is design-based, complying with the following key steps – Discover > Learn > Sense > Respond cycle. The knowledge base continuously helps in storing and building the pattern, which assists in self-learning and responding to actions.

The tests environment developed using Machine Learning will have advanced capabilities in terms of self-healing and intuitive dashboarding, using deep learning and neural networks algorithms, the corrections can be handled automatically
Accelerate software testing cycles and speed up time-to-market using ImpactQA’s proven test automation tools, frameworks and automation suite
Cognitive driven, platform-agnostic test automation frameworks focusing on improving test efficiency & success
Proven track record in year-over-year productivity improvement in testing and the practical implementation of onsite-offshore models for managed testing
Deep domain knowledge across various industries and experience with Artificial Intelligence (AI) and Machine Learning (ML) technology and algorithms addressing test solutions
Reusable functions and objects can be created and grouped using semi-supervised learning. Scenarios are flow-based, and hence the execution is transparent to the user
Multi-vendor and multi-technology test labs powered by open-source and commercial test tools addressing a diversity of customer requirements
The test flows are recorded and can be tested using data

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