Improve QA efficiencies using Artificial intelligence (AI) algorithms and Machine learning

Overview

Using machine learning and analytics, Infosys helps unlock the power of data (like project documentation, test artifacts, defect logs, test results, production incidents, etc.) and drives automation and innovation, improving QA efficiencies beyond the reach of traditional QA practices.

Infosys AI/ML-led QA offering, based on a scientific approach using both supervised and unsupervised methods, helps unearth defects beforehand, optimize testing and predict failure points, thus reducing the overall cost and achieving high customer satisfaction.

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How Infosys AI/ML led QA helps enterprises:

Our approach to artificial intelligence (AI) / machine learning (ML) based quality assurance is design-based, complying with the following steps - Discover > Learn > Sense > Respond cycle. The knowledge base constantly helps in storing and building pattern, which in turn helps in self-learning and responding to actions.

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Challenges & Solutions

Infosys Test Suite Optimizer helps identify redundancies and similarities to the tune of up to 30%

Infosys prediction tool predicts number of failures and identifies high failure modules with an accuracy of over 85%

Infosys Traceability tool highlights missed requirements ensuring better coverage and early identification of defects

Infosys Test Scenario Mining tool prevents failures by early detection of high risk areas and by acting as a deciding factor for the right test cases to be executed

Infosys Impact analysis tool gives a pictorial view of the inter-relationships between the entities analyzed over 50 for easier impact analysis

Infosys Defect analytics solution identifies high risk areas in the application and can also conduct a pareto analysis showing which modules/ applications are generating 80% of defects

Infosys Customer Sentiment Analytics gives insights from extracts of social media comments of the customer which helps to enhance customer experience