5 Days of Work Compressed into 30 Minutes: High-Accuracy Test Design for India’s leading power trading platform

Testing complex domains like energy and commodity markets requires more than just standard QA; it requires deep context. India’s leading power trading platform faced the difficult task of ensuring quality across data-heavy workflows where a single error could have significant downstream effects. This case study details how they utilized AI to generate high-coverage test cases in minutes rather than days.

The Challenge: Complexity and Data Overload

The commodity market modules The client operates are intricate. A single test case could involve 170+ distinct data points, making manual verification incredibly tedious and prone to human error.

The traditional process was unsustainable:

• Heavy Manual Effort: A manual tester required 5 full days to design and execute coverage for these complex flows.

• Dependency on Experts: The process relied heavily on domain experts and open-source tools, creating bottlenecks when those experts were unavailable.

• Scalability Issues: As the market modules grew, the manual approach could not scale efficiently.

   

The Solution: Context-Aware AI Generation

The client turned to Nogrunt, using its AI-powered capabilities to understand the context of data-heavy workflows without needing deep human intervention.

• Empowering Entry-Level Testers: The tool enabled an entry-level tester—without deep domain knowledge—to generate and execute 190+ test cases.

• Contextual Understanding: Unlike standard script recorders, the AI understood the “why” and “how” of the data, managing the 170+ data points automatically.

• Scriptless Execution: The solution removed the dependency on complex scripting or deep domain expertise.

The Impact: Superior Speed and Scalability

The results highlighted a clear victory for AI over traditional expert-driven methods.

• Time Compression: What previously took 5 days of manual work was completed in just 30 minutes.

• Higher Quality Coverage: The AI delivered higher-quality test coverage than even expert-driven methods by eliminating human fatigue and oversight.

• Strategic Freedom: By automating the grunt work, expert bandwidth was freed up to focus on strategic tasks rather than routine validation.

• >90% Coverage: The automated approach achieved greater than 90% coverage for this complex domain.