How to Exceed Testing Limits

The Limits of Testing
and How to Exceed
Them
Craig Stuntz
https://speakerdeck.com/craigstuntz

?
I have a couple of questions for you.

How many of you are QAs? Developers? (I’m a developer)

Consider the product you’re working on right now.

What kind of bugs do
you want in your final,
QA approved product?

“None?”
People want to say “none,” but that’s setting a high bar to clear.

https://www.ﬂickr.com/photos/ﬁlipbossuyt/21409291292/
Not impossible, though! Jumping over a bar 2 meters in the air isn’t easy, but it can be done if you’re prepared to work at it. Most people (product owners?) will be
unwilling to pay the price.

So if you want no defects, I’ll tell you how to do that. Cut most of your features. Then do it again.

80/20
80/20 rule for software: If you cut 80% of the features, maybe 20% of users will notice.

Most software has far too many features.

This is the bottom of the third page of ﬁfth tab of the options dialog for the Java plug-in. If you select the highlighted option, this instructs Java to not install malware on
your machine during security updates. Naturally, it’s de-selected by default.

So there is plenty of room to eliminate features.

https://www.ﬂickr.com/photos/10159247@N04/4335602802/
In ancient times, programmer life was simple. Dinosaurs roamed the Earth, we didn’t write unit tests, and we employed people to slowly and painstakingly ﬁnd bugs for
us.

<BEEP!>
<BEEP!>
And then we decided testing was good.

And then people said we should test all the …. time.

And from then on our software was perfectly reliable and secure. We can all go home.

That’s the end of my presentation, thanks for coming….

Now it turns out that fuzzing software makes security bugs jump out at you in a way that tests never will.

Now it turns out that static analysis makes resource leak bugs jump out at you in a way that tests never will.

Now it turns out that…

Wait. This is getting complicated.

What to do?

Agenda
• Whole project quality
• Goal Driven
• Realistic
I’m interested in building correct software. Sometimes people start by writing this off as impossible.

It’s easier to dismiss something as impossible than to ask if you can bite off a big chunk of it.

Whole project quality - not just individual pieces of testing piled on top of each other

Goal driven - Other techniques complement testing to find errors that unit tests can’t find

Realistic - These methods are useful on real-world software, today

https://www.ﬂickr.com/photos/taylor-mcbride/3732682242/
It turns out the QA landscape is huge and there are some beautiful techniques available that you can combine to implement a realistic plan for achieving a desired level of
quality.

The biggest danger that will stop you from getting there is looking to just one technique to solve all your problems. Focus on the goal, not the mechanism.

Immediate
Digression
Manual Testing
Really useful, but doesn’t ﬁt the theme of the rest of the talk.

Still, really useful, so let’s talk anyway!

How is manual testing fundamentally diﬀerent than unit tests, automated tests, etc.?

Sometimes we think of manual testing as poking weird values into inputs. And hey, it works sometimes: Both Android and iPhone lock screens broken by “boredom
testing.” But computers can do this faster.

The best application for manual testing: What is something that computers can never do by themselves today?

https://medium.com/backchannel/how-technology-led-a-hospital-to-give-a-patient-38-times-
his-dosage-ded7b3688558
This is life or death. This is an alert screen.

Epic EMR. One of 17,000 alerts the UCSF physicians received in that month alone.

Contains the number “160” in 14 diﬀerent places.

Nurse clicked through this and patient received 38 1/2 tablets of an antibiotic. He’s fortunate to have survived.

Use human testing for things only people can do!

https://lobste.rs/s/fdmbn5
For the rest of this presentation I’m going to talk about tests performed by a computer.

For many people, unit tests are both a design methodology and the ﬁrst line of defense against bugs.

Let’s Write a parseInt!
let parseInt (value: string) : int = ???
Because I’m a NIH developer, and because it’s a really simple example to play with, I’ll write my own parseInt. It’s simple, right? Maybe too simple to say anything
worthwhile?

Test First!
[<TestMethod>]
member this.``Should parse 0``() =
let actual = parseInt "0"
actual |> should equal 0
But I believe in test ﬁrst and TDD, so… What sort of tests do I need for parseInt?

This looks like a good start. Of course, this test does not pass yet, because I haven't implemented the method. That failure is an important piece of information! If I can’t
parse 0, my parseInt isn’t very good.

So let's say that I go and implement some parseInt code. At least enough to make the test pass. Now, this test tells me very little about the correctness of the method.
That's interesting! Implementing the method removed information from the system! That seems really weird, but…

Test First!
[<TestMethod>]
[<TestMethod>]
Maybe I should add another test.

Am I missing anything?

Test First!
[<TestMethod>]
member this.``Should parse -1``() =
let actual = parseInt "-1"
actual |> should equal -1
[<TestMethod>]
member this.``Should parse with whitespace``() =
let actual = parseInt " 123 "

Test First!
[<TestMethod>]
member this.``Should parse +1 with whitespace``() =
let actual = parseInt " +1 "
[<TestMethod>]
member this.``Should do ??? with freeform prose``() =
let actual = parseInt "A QA engineer walks into a bar…"
actual |> should equal ???
Anything else? null, MaxInt+1, non-%20 whitespace, MaxInt, MinInt, 1729?

I’m starting to realize I have more questions than answers!

More Questions
• Is this for trusted or non-trusted input?
• Can I trust that my function will be invoked
correctly?
• What is the culture of the input?
1) Trusted = exception; untrusted = fail gracefully.

2) For a private method, maybe. For a library function, no! Need tests per invocation?

3) , $, etc.?

It sounds like we might need a lot of tests. How many? Does it seem weird that we’re talking more about corner cases than “success?” Does this teeny little helper
method really need to be perfect? I just wanna parse 123!

Getting one digit wrong really can get your company into the headlines.

Also, what about security sensitive code. Hashes, RNGs.

Does it seem like test case suggestions focused on error cases? Even if 90% of the time we get expected input, I’m far more interested in the reasons which explain 90%
of the failures.

Bad Error
Handling Kills
“Almost all catastrophic
failures (92%) are
the result of incorrect
handling of non-fatal
errors explicitly
signaled in software.”
https://www.usenix.org/conference/osdi14/technical-sessions/presentation/yuan
Only tested software designed for high reliability. (Cassandra, HDFS, Hadoop…)

“But it does suggest that top-down testing, say, using input and error injection techniques, will be challenged by the large input and state space.”

Simple Testing Can Prevent Most Critical Failures, Yuan et. al.
92% of the time the catastrophe was caused not by the error itself but rather the combination of the error and then handling it incorrectly!

How Can I Be
Completely Confident
in a Simple Function?
(Or at least do the right thing when it fails)
(And also insure it’s always called correctly)
(Every. Single. Time)
Let’s face it, this is the bare minimum ﬁrst step for trusting an application, right?

You might ask, “Why is this idiot rambling on about parseInt? I have 10 million lines of code to test.” I think it’s sometimes informative to start with the simplest thing
which could possibly work.

Unit Tests
• Helping you think
through bottom-up
designs
• Preventing
regressions
• Getting you to the
point where at least
something works.
Are Great
• Showing overall
design consistency
(top-down)
• Finding security holes
• Proving correctness
or totality of
implementation
Not So Helpful
We can use techniques like strong typing, fuzzing, and formal methods to compliment testing to give more control over code correctness. You will still need tests, but
you’ll get much more “coverage” with fewer tests.

Looking at the lists here, a theme emerges. To write a test, you needed a mental model of how your function should work. Having written the tests, however, you have
thrown away the model. All that's left are the examples.

When My Test
Fails
I know I’ve found a bug
(useful!)
Passes
I know my function works for at
least one input out of billions
(maybe useless?)
Does this make sense to everyone? Do you agree that a passing test doesn’t tell you much about the overall quality of the system?

Is there a way to ensure we always get correct output for any input?

Yes, but before we even get there, there’s a bigger problem we haven’t talked about yet.

How Can I Be Completely
Confident When
Composing Two Functions?
(Composing two correct functions should produce
the correct result.)
(Every. Single. Time)
Let’s face it, this is the bare minimum second step for trusting an application, right?

More generally, I would like to be able to build complete, correct programs from a foundation of correct functions. Now verifying my 10 million lines of code is easy; start
with correct functions, then combine them correctly!

parseIntAndReturnAbsoluteValue = abs ∘ parseInt
If I have two good functions, like abs and parseInt, I would like to be able to combine them in order to produce a correct program.

But there’s a problem: parseInt, as written, isn’t total (deﬁne). I can call it with strings which aren’t integers, and it’s really hard to use tests to ensure I call it correctly
100% of the time. How do I know it will always return something useful?

let parseInt (str) =
!" implementation
One thing I need to do is ensure that people call my function passing a string as the argument, and that the thing it returns is actually an integer, in every case.

let parseInt (value: string) : int =
!" implementation
That’s not too hard. I can prove this with the type system.

As long as I don’t do anything which subverts the type system (unsafe casts, unchecked exceptions, null — or use a language which won’t allow it!), I can at least be sure
I’m in the right ballpark.

But how do I ensure I’m only passed a string representing an integer? Or should I? Can I force the caller to “do the right thing” and handle the error if they happen to
pass a bad value.

public static bool TryParse(
string s,
out int result
)
{
!!.
}
Again, you can do it with the type system! I’m showing a C# example here, since the idiomatic F# solution is diﬀerent.

public static bool TryParse(
string s,
out int result
)
{
!!.
}
!" appropriate when input is “trusted”
int betterBeAnInt = ParseInt(input);
!" appropriate for untrusted input
int couldBeAnInt;
if (TryParse(input, out couldBeAnInt))
{
!" !!.
It is now diﬃcult to invoke the function without testing success. You have to go out of your way. This probably eliminates the need to use tests to ensure that every case
in which this function is invoked checks the success of the function.

Consider input validation. Bad input is in the contract. Exceptions inappropriate. Instead of returning an integer, return an integer and a Boolean.

But There’s Still The
Matter of That String
Argument
We can prove that we do the right thing when our parseInt correctly classiﬁes a given input value as a legitimate integer and parses it, or rejects it as invalid, but how can
we show that we do that correctly? Aren’t we back at square one? Types are super neat because you get this conﬁdence essentially for free, and it never fails, but even
the F# type system can’t make sure I return the right integer.

State Space
0
}1 {
A
B
In principle, your app, or your function, is a black box. Same input, same output. Easy to test, right?

This application should have only two possible states!

To be totally conﬁdent in your system you need to test, by some means, the entire state space (LoC discussion).

State Space
“Hello”
}“World” {
A
B
⚅ 🕑
It gets harder quickly. If my inputs are two strings instead of two bits, I now have considerably more possible test cases!

(Click) In the real world, you have additional “inputs” like time and randomness, and whatever is in your database.

Formal Methods
Using formal methods means the design is driven by a mathematical formalism. By deﬁnition, this is not test driven development, although you will probably still write
tests. Formal methods are sometimes considered controversial in the software development community, because they acknowledge the existence and utility of math.

____ + 1234 ____
[ t]*[+-]?[0-9]+[ t]*
It’s easier to use formal methods if there’s an oﬀ-the-shelf formalism you can use. For the problem of parsing, these exist!

One way to reduce the input domain of the parseInt function from an untestably large number of potential states is to use a regular expression. This is not the sort of
regular expression you might encounter in Perl or Ruby; it is a much more restricted syntax typically used on the front end of a compiler. The important point, here, is that
we can reduce the number of potential state of the function to a number that you can count on your ﬁngers.

0
1
2
3 4
[ t]
[+-]
[0-9]
[0-9]
[0-9]
[ t]
[+-]
REs convert to FSMs.

3+4 are accepting states.

4-5 states, 2 of them accepting, well less than “any possible string!”

Totality checking. Breaking my vow to avoid showing implementations.

Lots of code here, but the important word is at the top.

I’ve hesitated about showing implementations until now, but I can’t avoid it here, because…

The proof is built into the implementation

When
My
Test Type Checker
Fails
I know I’ve found a
bug
(useful!)
I might have a bug
(sometimes useful,
sometimes
frustrating)
Passes
I know my function
works for at least one
input out of billions
(maybe useless?)
There is a class of
bugs which cannot
exist
(awesome!)
We can expand this chart now.

Tests and types are not opponents; they complement each other.

Where one succeeds, the other fails, and vice versa.

Property Based
Testing
Still, there are cases where it’s hard to use formal methods.

Not every problem has an oﬀ-the-shelf formalism ready to use.

But we don’t have to just give up and accept unit tests as the best we can do!

let parsedIntEqualsOriginalNumber =
fun (number: int) !→
number = parseInt (number.ToString())
> open FsCheck;;
> Check.Quick parsedIntEqualsOriginalNumber;;
Falsifiable, after 1 test (1 shrink) (StdGen
(1764712907,296066647)):
Original:
-2
Shrunk:
-1
val it : unit = ()
>
Can you state things about your system which will always be true?

What must be true for my system to work?

Looks like I have to do some work on my implementation here!

Important: I didn’t have to specify the failing case, as I would with a unit test. FsCheck found it for me. In unit testing, you start with a mental model of the speciﬁcation,
and write your own tests. With property based testing, you write down the speciﬁcation, and the tests are generated for you.

PBT: Great for helping to find bugs in specific routines.

Fuzzing: Great for finding unhanded errors in entire systems.

http://colin-scott.github.io/blog/2015/10/07/fuzzing-raft-for-fun-and-proﬁt/
It often makes sense to write a custom fuzzer. It’s not hard, and the return is huge. This example more similar to property based testing, since it uses the stated invariants
from the Raft speciﬁcation to test an implementation.

(Policy story)

Runtime Validation
Sometimes the most important value to test is the only one that matters to you at runtime.

Assertions are a little under-used, because we tend to think of them as checking trivial things.

But using the techniques of property-based testing, we can do end to end validation of our system.

let input = " +123 "
let number = parseInt input !" 123
let test = number.ToSting() !" "123"
if test <> input !" true!
then
let testNumber = parseInt test !" 123
if number <> testNumber !" false (yay!)
then failwith "Uh oh!"
!" We’re safe now! Use number…
Similar to property based testing

http://lefthandedgoat.github.io/canopy/
Integration testing should always be automated. Deals with coupling between systems not covered by type safety (DB, DOM, etc.)

Use Canopy

Also: write integration test method.

The Quality Landscape
• Manual testing
• Integration tests
• Unit tests
• Runtime validation
• Property based testing
• Fuzzing
• Formal methods
• Static analysis
• Type systems
• Totality checking
The long and the short of it: Think big! Don’t “test all the ___ing time” because somebody told you to. Keep your eyes on the prize of software correctness.

Ask yourself which things are most important to the overall quality of your system. Pick the tool(s) which give you the biggest return.

Synopsis of each.

Craig Stuntz
@craigstuntz
Craig.Stuntz@Improving.com
http://blogs.teamb.com/craigstuntz
http://www.meetup.com/Papers-We-Love-Columbus/

How to Exceed Testing Limits

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