Using Hypothesis and Pexpect to Test High School Programming Assignments

I’ve been coming up with some fun projects for a beginner Python high school class. Most of these projects are simple command-line programs that prompt the user for some input, perform some calculation, and print some output. For instance, here’s the password checker project:

This program should prompt the user for their username, student ID, and password, and it should print out the string GOOD or BAD to indicate whether or not the password is “valid”; see the assignment writeup for more details.


When a student finishes their password checker, we need to examine it to see whether or not the student programmed it correctly. We have around thirty students, and the checker program needs to satisfy a variety of constraints, and students often don’t get them all right the first time, so each student will usually submit several versions of the checker.

Testing submitted password checkers a jillion times by hand (“does the latest version of Jane’s checker correctly reject 'abcd'? How about '$!@#5555'?”) sounded pretty awful, so I decided to write a program to do this for us.

These students hadn’t learned about functions yet, so their programs didn’t have an is_password_good(password) function that I could import and unit-test. Instead, I needed to write code that would run the student’s program, send it several lines of input, and read its output.

My first instinct was to use the subprocess library to do this, but I had trouble getting that to work. I needed to send a line to the program, then wait and then send another line, and then wait and send a third line; but the subprocess library’s API isn’t particularly well-suited for situations where you want to send a program more than one line of input. I Googled around and found a bunch of StackOverflow questions written by people in my exact situation, and the answers all said to use pexpect instead.


Pexpect is a library that lets you start a program, feed it as many lines of input as you want, and read as many lines of output as you want.

Here’s how to use pexpect to operate the password-checker program you saw earlier:1

Once I had that working, I wrote some standard unit tests.

I hand-wrote around twenty assertions like those and called it a day. It was very satisfying to run the resulting tests, and I felt great about the amount of time that they would save.

A week later, though, I stumbled across Hypothesis and realized that my tests had a lot of room for improvement.


Hypothesis is a property-based testing library. Its homepage says:

Hypothesis runs your tests against a much wider range of scenarios than a human tester could, finding edge cases in your code that you would otherwise have missed.

Earlier, I showed you a test called test_too_short_rejected(). That test asserts that the password 'X$3' is marked “BAD”, because the password checker is supposed to reject passwords that are shorter than eight characters.

This is an example-based test, which means that I wrote it by hand using an example too-short password that I came up with off the top of my head.

This test is actually pretty flimsy, because it only checks to see if 'X$3' is rejected—but if the student’s checker program incorrectly allows a seven-character-long password like 'X$12345', my test won’t catch that bug, because I didn’t think to include that example in my test. I could add more examples to my test, but that isn’t very fun; and even if I did think really hard and came up with five more examples, my test still wouldn’t be very exhaustive, because students are very good at coming up with bugs that I wouldn’t think to test for.

How To Use Hypothesis

Let’s use Hypothesis to improve this test. We’ll start by adding the @given decorator to our test function.

When Hypothesis sees a test that’s annotated with the @given decorator, it runs that test a bunch of times. This test’s decorator says that the test wants a random password argument; so each time Hypothesis runs this test, it’ll generate a random password, and will supply it to the test via the test function’s newly added password parameter.

We’re halfway there—all we have to do now is tell Hypothesis how to actually generate too-short passwords.

A too-short password is a string with some characters in it. Those characters can be the lowercase letters a-z, the uppercase letters A-Z, the digits 0-9, and some specific symbols given in the assignment writeup. The student’s password-checker program is supposed to reject passwords that are shorter than eight characters, so a too-short password can have at most seven characters.

Here’s how to say that to Hypothesis:

st.text() returns a strategy, which is an object that Hypothesis can use to generate random data. Hypothesis has a ton of these that you can use to generate all sorts of stuff.2

When we give short_password_strategy to the @given decorator, Hypothesis will generate random passwords like these:

That’s all there is to it—now that we know how to generate random too-short passwords, we can convert our example-based test to a property-based test.

We’re done! That wasn’t so hard.

Here’s what our test looks like in action:3

That’s a lot more thorough than assert_bad('X$3', checker).

Before I wrap up, I’d like to tell you about two of my favorite Hypothesis features: shrinking and the example database. Both of these features are also described in Anatomy of a Hypothesis Based Test, which is well worth a read.


If Hypothesis generates a random value that causes your test to fail, it will then attempt to shrink that value, which means that it tries to find a “simpler” value that still causes your test to fail.

For instance, if Hypothesis finds that a student’s password checker incorrectly accepts the too-short password',xcc69', it will usually shrink that password down to 'A1!'. That’s because even when students forget to implement the at-least-eight-characters rule, they still often remember to implement the rule that says that passwords must contain “at least three categories of character”; and so for programs like that, 'A1!' is the simplest possible input that causes the test to fail.

This is a really great quality-of-life feature that makes test failures much easier to decipher. It doesn’t make much of a difference in this example, but it’s a lifesaver when you’re dealing with large/complex inputs.

Example Database

When I first learned about Hypothesis, I was concerned that its randomness would be a liability. If Hypothesis gives my tests random input every time, and the program I’m testing has a failure that’s only triggered by a rare input, then won’t my tests sometimes pass and sometimes fail?

Hypothesis solves this problem by saving previously seen failures in a folder called .hypothesis/examples and trying them again the next time you run your tests. This “example database” feature means that once your Hypothesis test fails, it’ll keep on failing until you fix the bug. Which is an extremely good thing.

What It Feels Like To Use Hypothesis

It feels really good.

Our Hypothesis tests have caught a really amazing amount of bugs in students’ programs, many of which were things I simply would not have caught with example-based tests. One student’s password checker turned out to use the hand-crafted string abcdefghijklmnopqrstuvwyz, which if you’ll look closely you may notice is missing the letter x. Lots of little tiny bugs like this.

Hypothesis tests—at least, the basic ones I’ve written so far—aren’t hard to write. In fact, writing them is pretty fun! When I write Hypothesis tests, my tests find a lot of bugs; I tell our students to fix their bugs; and I feel like a happy calm wizard.

You should try using Hypothesis the next time you’re writing tests in Python. If you use a different programming language, check this page to see if your language has a good property-based testing library. If it does, try it out!

  1. It’s interesting to note that pexpect.popen_spawn.PopenSpawn uses the subprocess library under the hood. The subprocess library can’t easily be used to send/receive multiple lines to/from a program, but it seems to still be a fine primitive to use when building a system that can do that.

  2. If you’d like to generate instances of classes defined in your program, you might find this guide handy.

  3. In this recording, I’ve put Hypothesis into verbose mode using the HYPOTHESIS_VERBOSITY_LEVEL environment variable so that we can see the random passwords that it generates. On the rare occasions when I write a Hypothesis test that passes the first time it’s run, I like to put Hypothesis into verbose mode and run the test again to convince myself that I haven’t made some sort of generation mistake.

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