Launching “AiTest” soon. A Cross-Browser performance testing SaaS platform. Automated but real load testing of your application. Stay tuned!
Transforming Software Quality Assurance for Faster and More Reliable Releases
A leading software development firm partnered with Applied AI Consulting to enhance the quality assurance of their web application. The project involved comprehensive performance testing to evaluate the solution’s ability to handle large data sets and assess the API’s performance under high user loads.
The application integrates with platforms like LinkedIn to provide users with detailed, up-to-date information about companies and individuals.
Key features of the application include:
Data Retrieval: Fetches comprehensive data on employees, branches, and companies from LinkedIn.
Search Functionality: Enables users to search for companies or employees, delivering relevant data quickly.
Credit System: Users spend credits to unlock specific information such as contact details.
Team Management: Managers can oversee credits, control team access, and share data among team members.
Bulk Updates: The application performs regular data updates, typically monthly, to reflect changes such as employee shifts or new company branches.
Admin Portal: An internal tool used by the product owners to manage credits and access control, ensuring smooth operations.
Application’s robust features and Applied AI Consulting’s performance testing helped ensure the platform could efficiently scale, delivering accurate, timely data under heavy user demands.
Manual to Automated Testing: Transitioning from manual to automated testing reduced regression time from 8 hours to 1.5 hours, saving 6.5 hours per run, improving speed and reliability in the QA process.
Testing Environment Issues:
Local testing environments were unreliable and lacked visibility, prompting a shift to centralized, accessible systems for better test management.
Random Test Failures: Random failures occurred due to shared resources during parallel testing, resolved by using separate test environments and accounts.
Performance Testing:
Automated stress tests (25-50 users over 12-24 hours) ensured application stability under load, integrated into the release pipeline for continuous performance assurance.
In today’s digital landscape, customer support is critical for brand loyalty. Integrating Generative AI (GenAI)
Transforming QA Efficiency: Achieving 80% Regression Time Reduction through Automation Streamlining Quality Assurance with Automated
The team automated over 320 test cases using a Selenium Java Cucumber framework, reducing testing time and eliminating human error. The suite runs independently, improving efficiency.
The automation suite was integrated with Jenkins for continuous testing and GitHub for version control, allowing tests to be scheduled and results to be shared, boosting collaboration.
JMeter simulated 50 concurrent users to ensure stability under load, while private accounts and dedicated environments resolved random test failures caused by concurrent testing conflicts.
Shortened Sprints: Faster testing enables quicker completion of sprints, allowing for rapid feature iterations. Quicker Go-to-Market Timelines: Accelerated testing facilitates faster transitions from development to production, helping the organization launch new features ahead of competitors.
Enhancements led to fewer random test failures, creating a more reliable and stable testing environment.
Cost Reduction: The reduced testing time significantly lowered AWS usage costs, achieving around 80% savings in overall expenses. Faster Delivery Cycles: Streamlined processes enabled quicker transitions from staging to production, enhancing responsiveness to market demand
Automated Testing Improvement: The effectiveness of the automated testing framework was enhanced, yielding more accurate and timely results. Faster Decision-Making: The improved testing process provided quicker insights into application readiness, allowing clients to make informed decisions promptly and facilitating faster go-to-market strategies.
Automation and parallel processing significantly reduced manual overhead, leading to a 50% increase in testing throughput. Regression testing time dropped from over 8 hours to just 1 hour and 30 minutes, yielding substantial cost savings for the client.
Continuous performance testing has built confidence in the application’s reliability during real-world usage. This proactive approach has led to a 30% reduction in post-deployment incidents, minimizing critical failures and improving overall application performance for users.
Faster testing cycles deliver accurate results, enhancing client trust and satisfaction. With quicker insights, clients can make informed decisions more rapidly, improving their overall experience and fostering stronger relationships with the testing team.
— Head of Engineer
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