mmmkkaaayy
Joined 6 years ago
Joined 6 years ago
From what I gather from the paper, they frame the problem of test generation as a search problem. An evolutionary algorithm randomly mutates a randomly generated test suite. The evolutionary algorithm optimizes for greater branch coverage.
Excerpt from the abstract:
"Our experiments confirm that evolutionary algorithms can outperform random test generation also in the context of Python, and can even alleviate the problem of absent type information to some degree. However, our results demonstrate that dynamic typing nevertheless poses a fundamental issue for test generation, suggesting future work on integrating type inference"
eg:
* Which db is the source of truth?
* How will conflicts from offline editing be handled?
* Will you be able to see edits made in realtime across the devices?
Every variable name is assigned its own color, so you can track the data dependencies in your function using color. Similar to what the author suggests as "Type Highlighting"
Some might consider it distracting/overstimulating, but I find it both useful and it makes the code look a bit more 'artistic'.