Bach and Bolton (their blog post, “Testing and Checking Refined”)
describe the idea that the word “check” is a useful label for some types of
activities and procedures commonly called “test.” The issue is that when
measuring product quality, it is important to differentiate between what test
professionals typically do as they exercise the product, and the value of
automated measurements of product behavior with Boolean pass/fail results.
In literature about testing, the word “check” often serves
as synonym for “assert” or “verify.” This book proposes a closely related use
of the word, similar to what Bach and Bolton described, and an important one to
the nature of the first pattern of MetaAutomation, Atomic Check.
Many practitioners believe that automation for software
quality starts with manual test cases. The manual test case is designed to be
executed by a person, and often they are, with useful quality measurement
results.
When the manual test case is automated, the conventional
wisdom goes, the quality measurement value of that manual test is multiplied
many times, because the test can now run faster and more reliably and offline
or after hours. In practice, “faster” is true most often, but not always, and
“more reliably” is only sometimes true, but running the tests offline and
repeatedly is in any case a very significant business value for catching
regressions and managing product risk.
On execution by a person, the original manual test had some
quality value to the product team. Is that value now covered by the automated
version, so the manual test never needs to be run again? No, generally, this is
not the case; people are smart and observant, and test professionals can note
and characterize (write a bug for) quality issues that automation will not
notice, especially for apps with a GUI or web sites. On execution by a test
professional, and according to expectations of people performing that role, the
original manual test measures much more than just the written test procedure
and verifications. People notice stuff, especially good testers. Automation
only notices what is required for the automation to run, plus explicitly coded
verifications or assertions.
If the team automates a manual test, and thereafter no
person ever runs the original test, along with the well-understood gains of
automation comes a significant but poorly understood loss in team capability of
measuring quality. The person who might have taken time to run the manual test
now has more time to add value in other ways, but all of the human-observable
aspects of product quality that are implied by or incidental to the manual test
are now going unmeasured. The automation can add verifications, but speaking
from extensive experience in automating for quality, coded verifications of
product quality that do not break the automation are very limited in number
relative to the range of issues a tester can notice.
Automating manual tests, combined with the above misunderstanding,
creates significant quality risk.
An example
Here is a manual test for a hypothetical team working on a
banking web site. Call these steps “transfer balances:”
1.
Browse to the bank site
2.
Login as test user
3.
Note checking balance
4.
Note savings balance
5.
Go to transfer page
6.
Transfer $5 from savings into checking
7.
Verify correct checking balance
8.
Verify correct savings balance
9.
Logout
When a person runs this test, in addition to the explicit
steps and verifications, she might notice and bug a huge number of potential
issues, for example:
·
The browser shows a security warning about the
site certificate
·
The balance in savings has gone negative
·
The ad on the page messes up page rendering
·
An incorrect name is shown for the greeting
·
The protocol serving the page does not have SSL
·
There is a spurious alert message
·
…
Later, she automates this test, with the verifications as
written. Management is happy and she is happy because, they think, she never
has to run the manual test again.
It is true that the importance of running this manual test
is reduced when it is automated, because what the team decided are the most
important business issues are now verified automatically. However, the need for
a team member to do these or closely related procedures manually does not actually go away.
The common misunderstanding is that after the manual test is
automated, there is no need to run it manually or even test around that
scenario. Call this case “A.” On the other hand, we have case “B,” the
understanding that there must still be some manual testing around this, because
the automation is very limited in ability to measure the details of a quality
experience for the product.
The judgments represented by cases A and B cause different
actions from the team around measuring quality, after the “transfer balances”
steps from the example test have been automated. The actions and inactions in
case A cause a gap in quality measurement, because the bulleted items may no
longer be measured. The longer these issues are broken, the more potential for
downstream issues, and for quality regressions, difficulty and cost in finding
and correcting the root cause of the issue increase rapidly with the time lapse
between the issue occurring in product code and discovery by the team.
That all adds up to create the risk of case A, relative to
case B. Case A can potentially result in such issues as:
·
Basic product issues discovered late in the
product cycle
·
Issues shipped to customers, so the customers
find serious issues before the team knows about them
From a project perspective, an important root cause of the
management error of case A is that “transfer balances” starts as a manual test,
and when it is automated, it becomes an “automated test.” It still looks like
the same test to teams afflicted by the misunderstanding of case A, except that
it now runs repeatedly and with lower personnel cost. The word “test” still
applies, and that trips the team up.
Words are labels, and choice of labels is easily dismissed
as “just semantics,” but words have connotations as well as denotations.
A manual test is a test. If that test is automated, the
result is still a type of test, but to highlight the change that automation
brings, this book uses and recommends the noun “check” for that purpose.
Checks do not have nearly the powers of observation that a
person does. Any verifications that the check does can be implicit, that is,
comes with the procedure code anyway, or explicit, which requires an explicitly
coded verification.
Use the term “check” every time a measurement is made of the
product where
1.
It is an end to end test
2.
The measurement procedure runs without human
presence or intervention
3.
The measurement procedure completes with a
pass/fail result
The word “check” works as a noun, for example: “A check is a
better label to use than automated test.” It also works as a verb, for example:
“Execute this set of automation to check that the end-to-end product behavior is
still correct.”
This terminology clarifies what automated testing does, but
even better, it avoids eclipsing manual testing around the functionality.
Promoting the term “check” instead of “automated test”
emphasizes the limitation of automation, and makes it clear that, especially
when working with a web site or other GUI, some manual testing still needs to
be done. Checks make the manual testing easier and less tedious by removing the
need to check the important business-logic behaviors of the product, but they
do not remove the need to run the
manual tests or to do exploratory testing around the tested feature.
“Check” is still a kind of test, but think of it like cheese
that originated in the Brie region of France; one can call it “cheese” and be
correct, but the much preferred and more efficient term is “Brie” and people
sound more discerning, educated and domain-aware when they use the latter term.
Readers may be wondering at this point: How about unit
tests? Should we call them “unit checks” now?
This book uses “unit test.” Unlike with automated end-to-end
tests, that were originally written as manual tests but then automated, unit
tests are never manual in origin so the risk described above is not an issue.
This book also uses “check” for an API or service test, as
long as all dependencies are in place. This is useful for techniques such as
bottom-up testing, for which the Atomic Check pattern is especially powerful.
Another advantage of “check” is that it makes it easier to
see that the best checks are designed and grouped differently than manual
tests. There is much more on this point in the book “MetaAutomation,” in Chapter
3 on the pattern Atomic Check.
