AI Testing Services

The world is heading towards increased adoption of AI-powered smart applications/systems, which over the next few years will increase exponentially.


The main challenge will be to test Artificial Intelligence (AI)-Machine Learning (ML)-based systems as we see the advances. This is due to the absence of any formal or normative literature on the strategy or technique to be followed during the research.

Today, AI is taking innovation and functionality to another level altogether. These systems’ behaviour and reactions vary over time, based on input data, and are therefore less predictable than conventional IT systems. Traditional techniques of testing are often focused on fixed inputs that generate those fixed outputs.

An AI application or an AI-driven process or framework requires extensive testing to ensure an acceptable level of performance. The main focus would be on testing the accuracy of trained models. Indium’s AI testing is methodical and precise in approach.

Key Elements to AI Testing

There are 4 key elements we consider in AI testing:

Algorithmic test

Data creation and curation

Smart interaction testing

Core AI testing

Algorithmic Test

Natural Language processing testing
This procedure incorporates the tests for accuracy, review, true positives, true negatives, false positives and false negatives.
Image recognition testing
Testing picture acknowledgment through essential structures and highlights by contorting or obscuring the picture to decide the degree of picture acknowledgment. Test design acknowledgment is done as well.
Deep learning testing
Testing to perceive the character or words from printed, composed or cursive contents.
Optical character recognition testing
Testing to perceive the character or words from printed, composed or cursive contents.

Data Creation and Curation

The Data creation and curation part includes

Domain-specific data

Cleansing and identifying sample data sets

Contextual data clusters

Data de-noising

Data labelling

Smart Interaction Testing

At Indium, we provide smart interaction testing as follows

Devices (Siri, Alexa, Google Assistant)
Augmented reality
Virtual reality

Core AI Testing

Core AI testing at Indium includes a plethora of complete testing models which are listed below:

Human unbiased testing

Challenger model

Decision analysis

Deployment and accessibility testing

NFR testing

Location-based User Do testing

Test Triangle (Unit service, UI)

White box and Black box testing

Model back testing

Chatbot Testing

Chatbots are available in the market everywhere now simulating human activities through messaging applications, websites especially Finance Services, mobile apps, phone conversations. Most people now like to talk to their devices rather than typing. This AI Software is the TREND and FUTURE as per the Statistics. (As per Gartner,  47% of organizations will use chatbots for customer care and 40% will deploy virtual assistants.)

Most Predicted Chatbot Use Cases for an Individual & Organization,

Chatbot Testing

Now comes the challenge, testing this software is difficult than writing it. We need to involve different testing scope to achieve items for a Successful & Perfect Solution,

Major Parameters involved in chatbot testing are:


Performance Testing

Speed, Measuring Goal Completion Rate, Self-service rate, AI and ML rates, User retention rate, Fallback rate.

Specialized Testing

Crowd, Error Management, Correcting and Interpreting data accurately and Securely. Avoiding Loops

Collecting Scope – Conversational Flow

whether Voice or Text, Dialogue Accuracy, Intelligence

Security Testing

End to end Encryption, Two Factor Authentication, User Authentication, Intent Authorization, Channel Authentication, Compliance Validation, Authentication Timeout, Self-destructing messages.

API – NLP & Cognitive Services

Performance Tuning, Performance Monitoring


User-friendliness, Navigation of hyperlinks

Personality/ Onboarding


Non-Functional Testing

By Adopting the above key items, most of the common challenges of Chatbot testing can be easily rectified!!

Key Challenges

groom professional

Misunderstanding requests

professional work

Execute inaccurate Commands.


Difficulty in understanding the accents.

Chatbot testing challenges

Chatbot Testing Technique/ Strategy:

The best Techniques for bot testing are:

Usability Testing Ico

General Test: UI/UX Testing


Domain Specific Test

Identify Keywords of that domain, Language and expression used related to the product.
Immersive Testing Ico

Limit Test

Handling Irrelevant User Dialogue’s

Standardised Test

Categories are Expected Scenarios, Possible Scenarios & Impossible Scenarios
End to End Automation Framework for AI-infused Chatbot
Involves API testing, Integration testing, Data Import, Export

Best and Successful Tools for Chatbot Testing:

  • Botium – Selenium Chatbot – Open Source
  • – Open Source
  • Beta Family
  • Reddit
  • User Testing
Chatbot Testing

Virtual Assistant Testing

Stepping to the next digital wave, AI opened another popular avenue for businesses especially Healthcare Industry, Financial Services to connect with their end-users – Voice-enabled Digital assistants like Alexa, Apple’s Siri, Microsoft’s Cortana, Google’s Google Assistant. These Virtual assistants leverage technologies such as the Internet of things (IoT) for Data collection and Cloud Computing for Processing. Though they expedited more advancements, there are a lot of incidents reported by customers to assure the quality of AI before it touches the end-users.

So, Machine Learning techniques such as Natural Language Processing (NLP), Natural Language Understanding (NLU) and Deep Neural networks to understand human speech patterns to algorithms and codes are used to get the right AI.

Three key aspects of an end-to-end Quality Assurance strategy in Virtual Assistance Testing are:

Train AI in the context of the business process to be delivered.

Test AI in the context of user-profiles and enterprise IT architecture.

Leverage real-world crowd testers to validate AI for a better customer experience.

The Three Virtual Assistance Solutions we offer are:

Integrating trusted technologies from the leading companies

Using external open-source tools

Independent AI software Development

How AI-Driven Testing is performed?

Testing is coupled with automation tools to speed up on the state of Quality. Key Features of AI-Driven QA Test Tools are

Integrating trusted technologies from the leading companies

Intelligent Requirement Gathering

Simplified Exploratory Testing

AI-Enabled Error Identification

AI-powered visual UI Testing & Monitoring

Maximum code coverage in limited time

Faster Decisions with AI

Benefits of AI Infused Virtual Assistance Automation:

Executing thousands and hundreds of test cases in a matter of seconds

Help the QA team to save time by pointing weaker areas.

Learning from errors and improving testing mechanisms.

Detecting items, functionality that can be removed.

Best Practices of Virtual Assistance Testing:

Measuring the performance of Virtual assistant’s is challenging buy yet essential to get great customer service. So, we need the right tool to analyze metrics.

Utilize best Analytical tool to collect metrics and to evaluate VA effectiveness.
AI is for hurry customers, so improve customer service response time.
Usability Testing Ico
Automating review and feedback emails with personalised email templates.

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