Artificial Intelligence and Machine Learning- Based Application Testing Services

Prepare for an intelligent future and enhance your QA efficiencies by our AI platform testing, ML based validation and RPA services

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

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

Perform Impact

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

RPA-Powered Digital Testers

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

software test automation

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

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

Instruct Artificial Intelligence

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

changes in software

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

Include Artificial Intelligence

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

Prioritize test cases

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

Fix tests

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

Predict and timely notify

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

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<br> Analysis

AI-Based Predictive

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

Self-Healing Test <br>Scripts

Self-Healing Test

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

Separate Cognitive<br> Features testing

Separate Cognitive
Features testing

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

AI-Powered Solutions Testing

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

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

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<br> Testing

Analytical Models

Dataset split and generation, model evaluation and test reporting.

Intelligent AI/ML Testing Techniques

Black Box and White Box testing

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.

Model back testing

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.

Non-Functional Requirements

Looking For A Comprehensive AI Based Validation Solution For Your Next Big Project?

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%.

Our Key Clients


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