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Let’s compare the output of an automated test with our old manual debugging methods.
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Benefits of automated testing
- Results displayed in an informative way
- Test files are separate from real code
- Output is easy to read and understand
- Guarantees code works as expected
- Instantly see if anything has broken when making changes
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In this course we'll be using a tool
called Mocha to write automated tests for
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individual units of code
in our application.
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We'll build a collection of
test functions called a suite.
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Mocha, like all testing frameworks,
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lets us run all of our
tests whenever we want to.
0:13
So we can make sure our functions
run as we expect them to.
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This will increase our confidence
that our code is working, and
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it will make understanding
problems much easier.
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So let me show you how easy it can be.
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Developers use the term test suite
in different ways sometimes.
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Some use it to refer to the whole
collection of all our test.
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Sometimes it means a particular
selection of tests that are related.
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For example of the test for logging into
our website might compose a test suite.
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Now the team that
designed Mocha uses suite
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to describe a specific group of related
tests so that's how all use it too.
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Just be aware that some people
use it in different ways and
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you might have to ask someone what exactly
they mean depending on the context.
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So I've already written a test suite for
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our tiny gatherNamesOf function
we used in the previous video.
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And don't worry about what
any of this means yet.
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I'll talk a lot more about how to write
a test suite and what it looks like soon.
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For now, I just want to show
you how Mocha runs tests.
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With mocha already installed and my test
set up, I can go over to the console and
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simply type mocha followed by test.
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And mocha automatically runs the test
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to check that the function
behaves the way we expect.
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So we can see here an output.
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That gatherNamesOf looks
good in our first tests.
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It returns an array, and the array is
just as long as the array we passed in.
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But then it fails when we expect it to
give us the name back from the object.
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We can see that gatherNamesOf
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returns undefined instead of
the person's actual name.
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And when I'm ready to go fix this function
Mocha tells me that I can check line 23
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column 25 of my utility spec.js
file to start debugging.
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So the error mocha is reporting is
just helpful enough in this case.
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So it says expected,
undefined to equal Guncer.
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So that means our function is
returning undefined at some point.
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Instead of Gunther.
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So if I go look at the gather names of
a function in my utilities.js file.
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I see one return statement from irate.
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But the function being used by
map doesn't return anything.
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I'm getting undefined in my new array.
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Because gather names of checks for
a name, but never returns it, so
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I'll fix this function by adding
return before person.name.
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I'll save this file and
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when I run the test again by typing Mocha
test, we can see that they all passed.
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So great,
now I know that the function is working.
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Don't worry if this doesn't make
a whole lot of sense just yet.
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It will all make more sense later.
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The main takeaway here is
understanding how automated testing
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solves a lot of the problems with manual
testing We noted in the last video.
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First our test suite could cover
every important function in our code.
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And when we run the tests we can see
all of the results nicely displayed
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in an informative way.
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So we don't have the problem of
our results being too narrow and
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second the test files
are completely separate.
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From our real code.
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So we don't have to make any changes
there or clean up our live code later.
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Third, the output is easy to read and
understand.
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So it's much easier to interpret
what we're seeing here
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than than in our consul
dot log statements.
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And finally, good tests guarantee that
our code works the way we expect.
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We can run the tests over and over any
time we make a change anywhere and
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instantly see whether
anything new has broken.
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We'll even see what the specific
nature of the problem is.
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As you learned,
automated tests handle a ton of the hard
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tedious work we were
previously doing by hand.
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It makes our code more reliable.
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It makes programming less frustrating and
the benefits scale more and
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more as our applications grow.
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