Test case generation

A software and data company that revolutionizes advertising by creating a data-driven ecosystem. Our client’s platform unifies audiences across traditional TV, streaming video, and digital media. It allows advertisers to connect media exposures to sales.

Problem Statement

The company’s existing manual approach to test case design was time-consuming, error-prone, and often failed to uncover critical software defects. As the codebase and product complexity grew, the testing team struggled to keep up with the pace of development, leading to delayed product releases and increased technical debt.

Our Solution

We helped our clients develop and integrate an AI-powered test case generation platform with the following key components:
  • Static Code Analysis: The system performed in-depth analysis of the source code, identifying key functionalities, data flows, and potential failure points. This provided the foundation for generating targeted test cases.
  • Machine Learning Models: Advanced machine learning algorithms were trained on the company’s historical test cases and defect data to learn patterns and relationships within the software under test. This enabled the generation of more intelligent, coverage-optimized test cases.
  • Natural Language Processing (NLP): NLP techniques were used to extract relevant information from software requirements, user stories, and design documents. This allowed the test case generation to be aligned with the intended product behavior.
  • Generative Adversarial Networks (GANs): The platform employed GAN models to create synthetic test data that mimicked real-world user inputs and edge cases, expanding the breadth of test coverage.
  • Continuous Testing Integration: The generated test cases were seamlessly integrated into the company’s continuous integration and delivery (CI/CD) pipeline, enabling rapid feedback loops and automated regression testing.

Business Benefits

  • Improved Test Coverage: The AI-driven test case generation resulted in a 35% increase in code coverage, ensuring that a greater proportion of the software’s functionality was thoroughly tested.
  • Reduced Testing Time: By automating the test case design process, the company was able to reduce the time spent on manual test case creation by 42%.
  • Earlier Defect Detection: The AI-powered test cases were more effective at uncovering critical defects earlier in the development lifecycle, leading to a 27% reduction in post-release bugs.
  • Increased Agility: The seamless integration of the test case generation platform into the CI/CD pipeline enabled the testing team to keep pace with the rapid development cycles, supporting the company’s agile transformation.
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