BDD testing frameworks produce useful documentation, which is why it’s critical to carefully review any AI generated “spec” to ensure the behavior is going to be what you want and that all of the scenarios are covered. Even if all the code is AI generated, humans should be deeply involved in making sure the “spec” is right, manually blocking out describe / it blocks or Cucumber rule / scenario / GWT as necessary. In the case of Cucumber with Acceptance Test-Driven development, it’s still useful to have humans write most of the feature file with AI assisting with example mapping and step definition implementation.
You’re right that there are also non-functional concerns like performance, security, maintainability, etc. that AI will often get wrong, which is why reviewing and refactoring are mandatory. AI code reviews in CI can sometimes catch some of these issues, and other checks in the pipeline like performance tests, CodeQL for security issues, Sonarqube for code smells / security, etc. can help as well.
AI can be a modest productivity boost when the proper guardrails are in place, but definitely not a free lunch. You can quickly end up with AI slop without the proper discipline, and there are definitely times when it’s appropriate to stop using AI and do it yourself, of not use AI in the first place for certain work that requires creative problem solving, that is too complex for AI to get right, etc. Usually anything I wouldn’t delegate to a junior engineer, I just do it myself and don’t bother with AI—that’s a good rule of thumb.
BDD testing frameworks produce useful documentation, which is why it’s critical to carefully review any AI generated “spec” to ensure the behavior is going to be what you want and that all of the scenarios are covered. Even if all the code is AI generated, humans should be deeply involved in making sure the “spec” is right, manually blocking out describe / it blocks or Cucumber rule / scenario / GWT as necessary. In the case of Cucumber with Acceptance Test-Driven development, it’s still useful to have humans write most of the feature file with AI assisting with example mapping and step definition implementation.
You’re right that there are also non-functional concerns like performance, security, maintainability, etc. that AI will often get wrong, which is why reviewing and refactoring are mandatory. AI code reviews in CI can sometimes catch some of these issues, and other checks in the pipeline like performance tests, CodeQL for security issues, Sonarqube for code smells / security, etc. can help as well.
AI can be a modest productivity boost when the proper guardrails are in place, but definitely not a free lunch. You can quickly end up with AI slop without the proper discipline, and there are definitely times when it’s appropriate to stop using AI and do it yourself, of not use AI in the first place for certain work that requires creative problem solving, that is too complex for AI to get right, etc. Usually anything I wouldn’t delegate to a junior engineer, I just do it myself and don’t bother with AI—that’s a good rule of thumb.