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AI coding has made CI a bottleneck, so we reworked ours to keep up

317 points · 408 comments · julian_digital

  1. aliclark · · focus · HN ↗
    In my case it's not the CI that's the bottleneck. It's the human testing side. Does it work, sure. But does it actually do the thing we want (and more importantly) does it do it in a way our customers will understand and actually like?
    1. radarsat1 · · focus · HN ↗
      I was just discussing with my team that this exact thing maybe brings back to relevance the idea of "behaviour driven development" and I was reminded of this Cucumber/Gherkin lib & language that defines a kind of executable prose you can use to specify how the software should behave. It's an interpretable programming language but designed to be close to how a human might just write down their specs of what kind of actions and responses are expected from a software system.

      The idea is to drive actual testing from this, but in this era, I think it's interesting as a way to use AI to generate tests, and to cross-check those tests with the natural language descriptions, in a bit of a cycle that helps refine the highest level definition of the software.

      Once that's nailed down, the implementation is just details.. normal engineering concerns like maintainability etc notwithstanding of course, but you can trust more and more the AI agents to get it right. The design specs being natural enough for humans to deal with but interpretable/specific enough to actually generate tests is pretty interesting for the bottleneck you are talking about, I think.

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