AI Testing

New-edge Customized AI-Powered QA Services

Testing is a crucial procedure that guarantees client satisfaction and quality within an application and helps in safeguarding against potential failures that may prove to be detrimental down the line. It is a planned procedure where the application is tested for stress, security, functional failures, UI, UX and analyzed under definite conditions to understand the overall threshold and risks involved in its execution.

With SDLC (software development life-cycles) becoming more complex by the day & delivery time spans reducing, software testers need to report feedback and evaluations immediately to the development teams. Given the breakneck pace of new software and product launches, there is no other option than to test smarter and not harder in this day and age.

AI Credibility in QA and Software Testing

Before we answer that let’s understand what Artificial Intelligence is, so any system which is designed to respond intelligently like a human being or say another living being, this can be in terms of any parameters like faster response time, ethical decisions, complex decisions, etc.

As there are so many branches of AI as a system and its applications, we at ImpactQA provide a custom-tailored AI solution to target your diverse testing needs, like :-

  • Black Box Testing

Testing the AI system without touching its core, from outside of the black box, testing input, and response only

  • Grey Box Testing

Testing a slightly deeper layer of the AI system to check its integration with subsystems like sensors, mechanical or electronic components, UI interface, Functionality & validations

Data Processing Layer or sub Algorithms

Testing the internal dynamics of the AI system as a whole as per above diagram, we at ImpactQA target each of those sub-systems to weed out the internal sub-layer anomalies, Like Neural Networks, Cognitive sciences, Swarm Intelligence, Fuzzy Logic, Expert system, logic programming and Re-enforced learning of the AI system as whole.

Weighted Parameters layer

Testing and validating the weighted parameter which is the core of any sub-system like Fuzzy, Neural or Swarm while learning or decision making.

Training Data Testing

Testing the data that makes an AI freeze its decisions over time, outlier testing, decision validation, etc.

Integration Testing

Anything communicating with the AI system like a UI application, website or mobile app, Databases, servers of 3rd party application, etc, simulating the traffic in between these systems either through, UI, API, or any other protocol and testing it out.

AI Engine Performance and Stress Behavior

Simulating traffic to your AI system and taking it to the desired stress levels and weeding out weird anomalies that occur when the application or AI system is under stress because of increased traffic.

Security of the AI system

Most of the AI system is directly or indirectly can be exposed to public which can lead to hackers tweaking the decision making or predicting capabilities of an AI, considering there is a lot of data involved which may be sensitive or personal, especially when such AI systems are interacting with other systems through the internet or any other communication.

Testing with AI

As Artificial Intelligence has so many applications, we thought of using these applications and intelligence into our testing stack/methodology to reap more benefits by testing Smart.

Intelligent AI-based Services by ImpactQA

AI in Automation Testing

AI-powered for test automation, organizations can now focus more on delivering superior customer experiences.

AI in Performance Testing

Help the performance testing in far more ways to analyze and identify performance bottlenecks and issues.

AI in Security Testing

Artificial intelligence could well help make Penetration testing much easier to do consistently and at scale.

AI in Reporting and Analytics

Rapid technological advances like AI-powered testing have been reshaping the business scenario and enabling the emergence of new business innovations.

Using AI in Automation Testing

  • Using AI in Automation Testing: Predictive execution of analyzed areas over subsequent builds to target functionally weak or defect prone functionality while still having complete coverage
  • Smart Defect Analysis: Analysis like Defect ageing, Defect logging Priority and severity decisions, minimal error in reporting, Functional Mapping, and real time execution tree changes to decrease time to market
  • Application stability analytics: Sign Off decisions, reporting to senior management, Resource analytics and time, effort and estimate prediction
  • Recovery and Error Handling Intelligence: The Smart Framework which is AI enabled is an intelligent self learning auto-updating testing engine which learns the changes going in the application through releases and automatically updates itself as per the latest functionality to maintain high coverage even with the new introduced errors and failure while gracefully reporting and exit from such situations

Using AI in Performance Testing

  • Analyze test data better and faster: Faster identification of bottlenecks and performance issues, heartbeat URLs, different protocols, automatic co-relations, etc
  • Identify performance trends more gradually: Pattern recognitions discovery and learning will help the performance testing in far more ways to analyze archived benchmarks and trends
  • Fine/Diamond Cut SLAs: As AI works on learning and decision making, it will automatically benchmark the service level agreements over time
  • Smart Monitoring and putting it all together: Different applications works with different monitoring types, tools and protocols, which needed to analyzed for analysis of system/server/DB query and response time behaviors etc and putting it all together in reports
  • Smart Analytics: Drilled down reporting, analysis and comparison for all roles

Using AI in Security Testing

  • Make the AI learn ethical hacking techniques: All the techniques in the quiver makes testing the security of the system easy and ready to penetrate
  • Reliable and error proof methodology: As compared to a manual security testing technique
  • New ways to penetrate through cognitive testing: Simulating speech, text recognition, etc are important to secure as well before go-Live

Using AI in Reporting and Analytics

  • Data Collection Capability- As in all different types of automated, functional, performance and security testing, the data collection is the key to data analytics
  • Data Analytics – Building custom based rules to utilize the data and present it in a useful and understandable manner
  • Data Projection- Different roles require different analysis, drilled down, high level, mid-level reporting, and analyses
  • Data feedback- Feedback mechanism to the Automatic controller of any automated, functional, performance or security testing system to help it make more intelligent execution decisions
  • Bringing it all under one roof- We at ImpactQA, tie all the techniques that we have learned and developed so far to cater to any custom tailored need of a client interested in having more returns out of its testing investments
Explore how you can embrace the AI-powered software testing innovations to gain a competitive advantage in Business.