How do syntax highlighting tools implement automated testing?

Question

How do syntax highlighting tools such as pygments and textmate bundle do automated testing?

Answer 1

Tools like this often simply resort to a large collection of snippets of text representing a chosen input and the expected output. For instance if you look at the Pygments Github, you can see they have giant lists of text files divided into an input section and a tokens section like so:

---input---
f'{"quoted string"}'

---tokens---
'f'           Literal.String.Affix
"'"           Literal.String.Single
'{'           Literal.String.Interpol
'"'           Literal.String.Double
'quoted string' Literal.String.Double
'"'           Literal.String.Double
'}'           Literal.String.Interpol
"'"           Literal.String.Single
'\n'          Text

Since a highlighting tool reads a piece of code and then has to identify which bits of text are parts of which bits of code (is this the start of a function? is this a comment? is it a variable name?), they usually perform various processing steps that will result in a list of tokens as above, which they can then feed into the next step (insert highlights from the first Literal.String.Interpol to the next, bold any Literal.String.Single, etc. by generating the appropriate HTML or CSS or other markup relevant to the system). Checking that these tokens are generated properly from the input text is key.

Then, depending on the language the tool is built in you might use an existing testing suite or build your own (pygments seems to use a Python-based tool called pyTest), which essentially consists of running each of the inputs through your tool in a loop, reading the output, and comparing it to the expected values. If the output doesn't match, you can display a message showing what test failed, what the input/output/expected/error values were. If an output passes, you could simply signal with a happy green checkmark. Then when the test finishes, the developer can hopefully reason out what they broke by looking over the results.

It is often a good idea to randomize the order that these inputs so that you can be sure that each step in the test doesn't have side effects that are getting passed along to the next test and cause it to pass or fail incorrectly. It might also be a good idea to time the length of the complete test. If the whole thing was taking 12 seconds yesterday, but now it takes two minutes, we may have broken something even if all the test technically "pass".

In tools like a code highlighter, you often have a good idea of what many of the inputs and outputs will look like before you can code everything up, for instance if some spec document already exists. In that case, it may be a good idea to include tests that you know won't pass right away, but mark them with some tag (perhaps some text marker within the file that says "NOT PASSING", or naming the file in a certain way), and telling your testing suite to expect those tests to fail. Then, as you fix bugs and add features, say you fixed Bug X in your attempt to make test #144 pass. Now when you run the text, it also alerts you that 10 other tests that should be failing are now passing. Congrats! You just saved yourself a lot of work trying to fix several separate problems that were actually caused by the same root issue.

As the codebase is updated, a developer would run and rerun the test to ensure that any changes he makes doesn't break tests that were working before, and then would add new tests to the collection to verify that his new feature, fixed edge case, etc., now has a known expected output that you can be sure someone won't accidentally break in the future.