Smart test case editor for OTA testing
Uusitalo, Santeri (2025)
Uusitalo, Santeri
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025052817774
https://urn.fi/URN:NBN:fi:amk-2025052817774
Tiivistelmä
The purpose of this thesis was to design and implement smart code editor features for the test case editor used in Nokia’s OTA test runner. The existing editor, built on the Monaco code editor, was extended to support intelligent features specifically tailored to the application’s YAML-based, keyword-driven syntax.
The smart features implemented include context-aware autocompletion, error and type validation, hover-based documentation, and document formatting. These were integrated using a custom web worker to ensure that computation occurred off the main thread. This approach ensured that the UI responsiveness remained unaffected during real-time processing.
Different architectural options were explored during the design phase. A client-side solution using web workers was ultimately adopted to minimize dependencies and simplify deployment across the OTA test runner’s continuously updated environments.
The final implementation enabled smoother and more efficient test case designing and editing experience. Furthermore, the work establishes a foundation for further development, such as AI-assisted test generation.
The smart features implemented include context-aware autocompletion, error and type validation, hover-based documentation, and document formatting. These were integrated using a custom web worker to ensure that computation occurred off the main thread. This approach ensured that the UI responsiveness remained unaffected during real-time processing.
Different architectural options were explored during the design phase. A client-side solution using web workers was ultimately adopted to minimize dependencies and simplify deployment across the OTA test runner’s continuously updated environments.
The final implementation enabled smoother and more efficient test case designing and editing experience. Furthermore, the work establishes a foundation for further development, such as AI-assisted test generation.