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

The latest AI and ML technologies in the digital era require a new approach to software testing while handling complex systems and functionalities. Testing AI platforms enables companies to ensure adequate security measures for their complex applications. At ImpactQA, we help companies overcome such complexities in testing AI, machine learning and natural language processing implementations. Additionally, we have built-in capabilities of using AI to enhance the efficiency of your software testing in all stages of the QA lifecycle. We offer software testing services to improve the overall experience while reducing risks and achieving high customer satisfaction.

Rapid
Deployments

Quickly identify pitfalls, including minor errors with automated test evaluations and testing tools

Predict
Failures

Execute test cases based on high-risk segments and check accuracy to assist better decision making

Eliminate Test Cases Redundancy

Save up to one third of your time by identifying and eliminating test case redundancies by efficiently utilizing AI and ML techniques and improving productivity

Perform Impact
Analysis

Graphical dashboards for component interactions without the need for programming techniques effective in defect management.

RPA-Powered
Digital Testers

Latest RPA powered digital testing tools increasingly used for repetitive services that help achieve 100% test automation

The Role of AI and ML Testing

Al and Ml testing framework can efficiently recognize pitfalls and with constant updates to the algorithms, it feasible to discover even the negligible error. Artificial Intelligence (AI) and Machine Learning (ML) tech are well-trained to process data, identify schemes and patterns, from 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 and external source such as the web.

Major Approaches for AI and ML Implementation in Software Testing

Train Artificial Intelligence (AI) and Machine Learning (ML) based systems for building automated test cases

Instruct Artificial Intelligence (AI) to organize test filtering data autonomously

Identify any changes in software and define whether it is a bug or an additional feature that should be tested

Include Artificial Intelligence and Machine Learning to easily detect software changes by inspecting history logs and correlating them with the outcomes

Prioritize test cases. Create dashboards to integrate and share data on tested code, current testing statuses, and test coverage

Fix tests on the run in case of any loopholes to speed up maintenance

Predict and timely notify about possible code or testing bugs and analyse to estimate test coverage

Latest Artificial Intelligence and Machine Learning Technologies We Use at ImpactQA

Data Wrangling and Pre-Processing
Data Visualisation
Feature Selection and Reduction
Deep Learning
Machine Learning
Natural Language Processing
Programming Languages
Data Verification

AI & ML Services Offered By ImpactQA

AI-Based Sentiment Analytics

Get insights from extracts of comments on customer’s social media accounts which help to enhance the customer experience by effective sentiment analysis.

AI-Based Predictive
Analysis

Real-time dashboard and AI-based predictive analytics performance engineering approach based on ML Analytics and Performance Predictions.

Self-Healing Test
Scripts

Smart Automation and self-healing test scripts with automated change detectors in your application.

Separate Cognitive
Features testing

This includes natural language processing, speech recognition, inputs optical character recognition and image recognition.

AI-Powered Solutions Testing

Testing services powered by Artificial intelligence and machine learning by using Chatbot Testing Framework and RPA Tests Framework.

AI Platforms Testing

Testing AI-enabled platforms by data source and conditioning tests, system or regression tests, algorithm tests and API integration tools.

ML Models Testing

Ensure holistic performance of your machine learning models by dual coding or algorithm ensemble, model performance testing, coverage guided fuzzing and metamorphic testing.

Analytical Models
Testing

Dataset split and generation, model evaluation and test reporting.

Intelligent AI/ML Testing Techniques

Black Box and White Box testing

Like any other traditional testing methodology, Black box and white box test methodology is used in AI and ML based platform testing to help understand complex information systems, minimize risks, and get actionable intelligence insights.

Model Back Testing

Historical data predictive model testing to check the functionality and accuracy of the system with quantitative metrics which measure the performance of a model’s forecast, the accuracy of its estimates, or its ability to rank-order risk.

NFR (Non-Functional Requirements) Testing

Check for representative sample view of things, deployment approach, product performance, usability, reliability, etc. for effective ML based model testing.

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How ImpactQA Makes AI/ML Testing More Intelligent?

Our approach to artificial intelligence and machine learning powered QA is design-based. The knowledge base continuously helps in storing and building the pattern, which assists in self-learning and responding to actions.

Customized and Dynamic Test Approach

Multi-vendor and multi-technology test labs powered by open-source and commercial test tools addressing a diversity of customer requirements.

Certified Team of Experts

Team of data scientists and machine learning experts to help follow a future roadmap for AI/ML platform.

Fast Quality Product Delivery

Reusable test frameworks and automation scripts help deliver quality solutions with speed and reduce human efforts by up to 50%.

Test Suite Optimizer

Identify redundancies and similarities to the tune of up to 30%.

Robotic Test Automation Frameworks

Cognitive driven, platform-agnostic test automation frameworks focusing on improving test efficiency and increasing automation coverage across heterogeneous technologies to 100%.

Model Evaluation Tools

Assess models built by data scientists with various datasets and improve model accuracy by 40%.

Defect Analysis Solutions

Identify high-risk areas in the application and conduct a Pareto analysis to show which modules/applications are generating 80% of defects.

Document Validation Solutions

Analyse handwritten documents converted into digital format and suggest corrections where necessary. Helps reduce errors by 50%.

Frequently Asked Questions (FAQs)

What is AI and ML testing?

AI and ML testing verifies that AI-powered applications and machine learning models provide accurate, reliable, consistent and expected outcomes. It evaluates the behavior of models, examines the quality of training data, assesses algorithms and integrations, explores the performance limits of the application and verifies the non-functional requirements of the application.

What does testing of machine learning models consist of?

Testing of machine learning models consist of verifying the accuracy of the models and assessing the performance of models. It also includes verification of datasets, algorithms, back-testing, coverage-guided fuzzing and metamorphic testing. Representative datasets, performance metrics and edge cases are used to validate the model. Testing also covers other unanticipated scenarios that may occur in the real world.

How can companies ensure AI models are accurate and reliable?

Companies can ensure accuracy and reliability of AI models by adopting a number of testing strategies that use edge cases, performance metrics, datasets and independent methods of validation. Testing models can also help in assessing the accuracy and consistency of outcomes, as well as the performance of models under various conditions.

Does AI/ML testing cover NLP, computer vision and other intelligent applications?

Yes, AI/ML testing covers applications employing natural language processing, speech recognition, optical character recognition, image recognition and other similar intelligence and predictive analytics. Testing can assess the behavior of models, as well as how the intelligence is integrated within the application.

What are the benefits of AI-powered testing?

With AI-powered testing, teams can detect high-risk areas, prioritize test cases, find changes in applications, remove redundant tests, analyze their testing history, and even determine what failures may occur in the future. With this, teams can improve their testing processes and spend their time on more important quality and risk areas.

What makes ImpactQA a great option for AI and ML testing services?

With its knowledge of AI and ML testing, ImpactQA can provide its services to test a wide variety of solutions, including AI platforms, ML models, analytical models, predictive analytics, and more. The company combines automation with risk-based testing and the evaluation of ML models to help businesses in testing intelligent applications.

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