Over decades of software development, people have discovered one truth: Untested software rarely works. (Many people would go as far as saying: “Most tested software doesn’t work either.” But we are all optimists here, right?) So, to ensure that your program does what you expect it to do, it is wise to test it.

One easy way to do that is to write a README file that describes what your program should do. And when you feel ready to make a new release, go through the README and ensure that the behavior is still as expected. You can make this a more rigorous exercise by also writing down how your program should react to erroneous inputs.

Here’s another fancy idea: Write that README before you write the code.

Automated testing

Now, this is all fine and dandy, but doing all of this manually? That can take a lot of time. At the same time, many people have come to enjoy telling computers to do things for them. Let’s talk about how to automate these tests.

Rust has a built-in test framework, so let’s start by writing a first test:

fn answer() -> i32 {

fn check_answer_validity() {
    assert_eq!(answer(), 42);

You can put this snippet of code in pretty much any file and cargo test will find and run it. The key here is the #[test] attribute. It allows the build system to discover such functions and run them as tests, verifying that they don’t panic.

Now that we’ve seen how we can write tests, we still need to figure out what to test. As you’ve seen it’s fairly easy to write assertions for functions. But a CLI application is often more than one function! Worse, it often deals with user input, reads files, and writes output.

Making your code testable

There are two complementary approaches to testing functionality: Testing the small units that you build your complete application from, these are called “unit tests”. There is also testing the final application “from the outside” called “black box tests” or “integration tests”. Let’s begin with the first one.

To figure out what we should test, let’s see what our program features are. Mainly, grrs is supposed to print out the lines that match a given pattern. So, let’s write unit tests for exactly this: We want to ensure that our most important piece of logic works, and we want to do it in a way that is not dependent on any of the setup code we have around it (that deals with CLI arguments, for example).

Going back to our first implementation of grrs, we added this block of code to the main function:

// ...
for line in content.lines() {
    if line.contains(&args.pattern) {
        println!("{}", line);

Sadly, this is not very easy to test. First of all, it’s in the main function, so we can’t easily call it. This is easily fixed by moving this piece of code into a function:

fn main() {
fn find_matches(content: &str, pattern: &str) {
    for line in content.lines() {
        if line.contains(pattern) {
            println!("{}", line);

Now we can call this function in our test, and see what its output is:

fn find_a_match() {
    find_matches("lorem ipsum\ndolor sit amet", "lorem");
    assert_eq!( // uhhhh

Or… can we? Right now, find_matches prints directly to stdout, i.e., the terminal. We can’t easily capture this in a test! This is a problem that often comes up when writing tests after the implementation: We have written a function that is firmly integrated in the context it is used in.

Alright, how can we make this testable? We’ll need to capture the output somehow. Rust’s standard library has some neat abstractions for dealing with I/O (input/output) and we’ll make use of one called std::io::Write. This is a trait that abstracts over things we can write to, which includes strings but also stdout.

If this is the first time you’ve heard “trait” in the context of Rust, you are in for a treat. Traits are one of the most powerful features of Rust. You can think of them like interfaces in Java, or type classes in Haskell (whatever you are more familiar with). They allow you to abstract over behavior that can be shared by different types. Code that uses traits can express ideas in very generic and flexible ways. This means it can also get difficult to read, though. Don’t let that intimidate you: Even people who have used Rust for years don’t always get what generic code does immediately. In that case, it helps to think of concrete uses. For example, in our case, the behavior that we abstract over is “write to it”. Examples for the types that implement (“impl”) it include: The terminal’s standard output, files, a buffer in memory, or TCP network connections. (Scroll down in the documentation for std::io::Write to see a list of “Implementors”.)

With that knowledge, let’s change our function to accept a third parameter. It should be of any type that implements Write. This way, we can then supply a simple string in our tests and make assertions on it. Here is how we can write this version of find_matches:

fn find_matches(content: &str, pattern: &str, mut writer: impl std::io::Write) {
    for line in content.lines() {
        if line.contains(pattern) {
            writeln!(writer, "{}", line);

The new parameter is mut writer, i.e., a mutable thing we call “writer”. Its type is impl std::io::Write, which you can read as “a placeholder for any type that implements the Write trait”. Also note how we replaced the println!(…) we used earlier with writeln!(writer, …). println! works the same as writeln! but always uses standard output.

Now we can test for the output:

fn find_a_match() {
    let mut result = Vec::new();
    find_matches("lorem ipsum\ndolor sit amet", "lorem", &mut result);
    assert_eq!(result, b"lorem ipsum\n");

To now use this in our application code, we have to change the call to find_matches in main by adding &mut std::io::stdout() as the third parameter. Here’s an example of a main function that builds on what we’ve seen in the previous chapters and uses our extracted find_matches function:

fn main() -> Result<()> {
    let args = Cli::parse();
    let content = std::fs::read_to_string(&args.path)
        .with_context(|| format!("could not read file `{}`", args.path.display()))?;

    find_matches(&content, &args.pattern, &mut std::io::stdout());


We’ve just seen how to make this piece of code easily testable. We have

  1. identified one of the core pieces of our application,
  2. put it into its own function,
  3. and made it more flexible.

Even though the goal was to make it testable, the result we ended up with is actually a very idiomatic and reusable piece of Rust code. That’s awesome!

Splitting your code into library and binary targets

We can do one more thing here. So far we’ve put everything we wrote into the src/main.rs file. This means our current project produces a single binary. But we can also make our code available as a library, like this:

  1. Put the find_matches function into a new src/lib.rs.
  2. Add a pub in front of the fn (so it’s pub fn find_matches) to make it something that users of our library can access.
  3. Remove find_matches from src/main.rs.
  4. In the fn main, prepend the call to find_matches with grrs::, so it’s now grrs::find_matches(…). This means it uses the function from the library we just wrote!

The way Rust deals with projects is quite flexible and it’s a good idea to think about what to put into the library part of your crate early on. You can for example think about writing a library for your application-specific logic first and then use it in your CLI just like any other library. Or, if your project has multiple binaries, you can put the common functionality into the library part of that crate.

Testing CLI applications by running them

Thus far, we’ve gone out of our way to test the business logic of our application, which turned out to be the find_matches function. This is very valuable and is a great first step towards a well-tested code base. (Usually, these kinds of tests are called “unit tests”.)

There is a lot of code we aren’t testing, though: Everything that we wrote to deal with the outside world! Imagine you wrote the main function, but accidentally left in a hard-coded string instead of using the argument of the user-supplied path. We should write tests for that, too! (This level of testing is often called “integration testing”, or “system testing”.)

At its core, we are still writing functions and annotating them with #[test]. It’s just a matter of what we do inside these functions. For example, we’ll want to use the main binary of our project, and run it like a regular program. We will also put these tests into a new file in a new directory: tests/cli.rs.

To recall, grrs is a small tool that searches for a string in a file. We have previously tested that we can find a match. Let’s think about what other functionality we can test.

Here is what I came up with.

  • What happens when the file doesn’t exist?
  • What is the output when there is no match?
  • Does our program exit with an error when we forget one (or both) arguments?

These are all valid test cases. Additionally, we should also include one test case for the “happy path”, i.e., we found at least one match and we print it.

To make these kinds of tests easier, we’re going to use the assert_cmd crate. It has a bunch of neat helpers that allow us to run our main binary and see how it behaves. Further, we’ll also add the predicates crate which helps us write assertions that assert_cmd can test against (and that have great error messages). We’ll add those dependencies not to the main list, but to a “dev dependencies” section in our Cargo.toml. They are only required when developing the crate, not when using it.

assert_cmd = "2.0.14"
predicates = "3.1.0"

This sounds like a lot of setup. Nevertheless – let’s dive right in and create our tests/cli.rs file:

use assert_cmd::prelude::*; // Add methods on commands
use predicates::prelude::*; // Used for writing assertions
use std::process::Command; // Run programs

fn file_doesnt_exist() -> Result<(), Box<dyn std::error::Error>> {
    let mut cmd = Command::cargo_bin("grrs")?;

        .stderr(predicate::str::contains("could not read file"));


You can run this test with cargo test, just like the tests we wrote above. It might take a little longer the first time, as Command::cargo_bin("grrs") needs to compile your main binary.

Generating test files

The test we’ve just seen only checks that our program writes an error message when the input file doesn’t exist. That’s an important test to have, but maybe not the most important one: Let’s now test that we will actually print the matches we found in a file!

We’ll need to have a file whose content we know, so that we can know what our program should return and check this expectation in our code. One idea might be to add a file to the project with custom content and use that in our tests. Another would be to create temporary files in our tests. For this tutorial, we’ll have a look at the latter approach. Mainly, because it is more flexible and will also work in other cases; for example, when you are testing programs that change the files.

To create these temporary files, we’ll be using the assert_fs crate. Let’s add it to the dev-dependencies in our Cargo.toml:

assert_fs = "1.1.1"

Here is a new test case (that you can write below the other one) that first creates a temp file (a “named” one so we can get its path), fills it with some text, and then runs our program to see if we get the correct output. When the file goes out of scope (at the end of the function), the actual temporary file will automatically get deleted.

use assert_fs::prelude::*;

fn find_content_in_file() -> Result<(), Box<dyn std::error::Error>> {
    let file = assert_fs::NamedTempFile::new("sample.txt")?;
    file.write_str("A test\nActual content\nMore content\nAnother test")?;

    let mut cmd = Command::cargo_bin("grrs")?;
        .stdout(predicate::str::contains("A test\nAnother test"));


What to test?

While it can certainly be fun to write integration tests, it will also take some time to write them, as well as to update them when your application’s behavior changes. To make sure you use your time wisely, you should ask yourself what you should test.

In general it’s a good idea to write integration tests for all types of behavior that a user can observe. That means that you don’t need to cover all edge cases: It usually suffices to have examples for the different types and rely on unit tests to cover the edge cases.

It is also a good idea not to focus your tests on things you can’t actively control. It would be a bad idea to test the exact layout of --help as it is generated for you. Instead, you might just want to check that certain elements are present.

Depending on the nature of your program, you can also try to add more testing techniques. For example, if you have extracted parts of your program and find yourself writing a lot of example cases as unit tests while trying to come up with all the edge cases, you should look into proptest. If you have a program which consumes arbitrary files and parses them, try to write a fuzzer to find bugs in edge cases.