It claims to plug into your CI pipeline, detect what changed, and generate relevant test cases using LLMs.
As someone who’s struggled with stale or missing tests—especially in fast-moving codebases—I find this idea quite compelling. But I’m also curious about how it handles:
Contextual understanding across large codebases (e.g., multiple modules touched in a PR)
Avoiding flaky or non-deterministic tests
Matching team-specific coding styles or conventions
anuragdt · 67d ago
Generating tests is good, but how to handle the updating tests? Also how will you handle the flakiness and side effects of AI models?
siddhant_mohan · 67d ago
We handles flakiness with retries, smart waits, and isolation, while side effects are avoided using clean setups, teardowns, and state-safe mocks. Each tests scenarios are independent of each other and can be configured in a way to have prerequisite to setup the system and the post callback to cleanup the system
About updating test scenarios, we map it with your github commits and when a new commits come, we use the diff to figure out if tests failing are because of a bug or because of a new feature.
As someone who’s struggled with stale or missing tests—especially in fast-moving codebases—I find this idea quite compelling. But I’m also curious about how it handles:
Contextual understanding across large codebases (e.g., multiple modules touched in a PR) Avoiding flaky or non-deterministic tests Matching team-specific coding styles or conventions
About updating test scenarios, we map it with your github commits and when a new commits come, we use the diff to figure out if tests failing are because of a bug or because of a new feature.