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.
Quickly identify pitfalls, including minor errors with automated test evaluations and testing tools
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
Graphical dashboards for component interactions without the need for programming techniques effective in defect management.
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.
Real-time dashboard and AI-based predictive analytics performance engineering approach based on ML Analytics and Performance Predictions.
Smart Automation and self-healing test scripts with automated change detectors in your application.
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.
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.
Looking For A Comprehensive AI Based Validation Solution For Your Next Big Project?
How ImpactQA Makes AI/ML Testing More Intelligent?
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%.