Concepts: Test-Ideas List
Topics
Information used in designing tests is gathered from many places: design models,
classifier interfaces, statecharts, and code itself. At some point, this source
document information must be transformed into executable tests:
- specific inputs given to the software under test
- in a particular hardware and software configuration
- initialized to a known state
- with specific results expected
It's possible to go directly from source document information to executable
tests, but it's often useful to add an intermediate step. In this step, test
ideas are written into a Test-Ideas List, which is used to create
executable tests.
A test idea (sometimes referred to as a test
requirement) is a brief statement about a test that could be performed.
As a simple example, let's consider a function that calculates a square root
and come up with some test ideas:
- give a number that's barely less than zero as input
- give zero as the input
- test a number that's a perfect square, like 4 or 16 (is the result exactly
2 or 4?)
Each of these ideas could readily be converted into an executable test with
exact descriptions of inputs and expected results.
There are two advantages to this less-specific intermediate form:
- test ideas are more reviewable and understandable than complete tests-it's
easier to understand the reasoning behind them
- test ideas support more powerful tests, as described later under the heading
Test Design Using the List
The square root examples all describe inputs, but test ideas can describe any
of the elements of an executable test. For example, "print to a LaserJet
IIIp" describes an aspect of the test
environment to be used for a test, as does "test with database
full", however, these latter test ideas are very incomplete in themselves:
Print what to the printer? Do what with that full database? They
do, however, ensure that important ideas aren't forgotten; ideas that will be
described in more detail later in test design.
Test ideas are often based on fault
models; notions of which faults are plausible in software and how those
faults can best be uncovered. For example, consider boundaries. It's safe to
assume the square root function can be implemented something like this:
double sqrt(double x) {
if (x < 0)
// signal error
...
It's also plausible that the < will be incorrectly
typed as <=. People often make that kind
of mistake, so it's worth checking. The fault cannot be detected with X
having the value 2, because both the incorrect
expression (x<=0) and the correct expression
(x<0) will take the same branch of the if
statement. Similarly, giving X the value -5
cannot find the fault. The only way to find it is to give X
the value 0, which justifies the second test
idea.
In this case, the fault model is explicit. In other cases, it's implicit. For
example, whenever a program manipulates a linked structure, it's good to test
it against a circular one. It's possible that many faults could lead to a mishandled
circular structure. For the purposes of testing, they needn't be enumerated-it
suffices to know that some fault is likely enough that the test is worth running.
The following links provide information about getting test ideas from different
kinds of fault models. The first two are explicit fault models; the last uses
implicit ones.
These fault models can be applied to many different artifacts. For example,
the first one describes what to do with Boolean expressions. Such expressions
can be found in code, in guard conditions, in statecharts and sequence diagrams,
and in natural-language descriptions of method behaviors (such as you might
find in a published API).
Occasionally it's also helpful to have guidelines for specific artifacts. See
Guidelines: Test Ideas for Statechart
and Flow Diagrams .
A particular Test-Ideas List might contain test ideas from many fault models,
and those fault models could be derived from more than one artifact.
Let's suppose you're designing tests for a method that searches for a string
in a sequential collection. It can either obey case or ignore case in its search,
and it returns the index of the first match found or -1 if no match is found.
int Collection.find(String string,
Boolean ignoreCase);
Here are some test ideas for this method:
- match found in the first position
- match found in the last position
- no match found
- two or more matches found in the collection
- case is ignored; match found, but it wouldn't match if case was obeyed
- case is obeyed; an exact match is found
- case is obeyed; a string that would have matched if case
were ignored is skipped
It would be simple to implement these seven tests, one for each test idea.
However, different test ideas can be combined into a single test. For example,
the following test satisfies test ideas 2, 6, and 7:
Setup: collection initialized to ["dawn", "Dawn"]
Invocation: collection.find("Dawn", false)
Expected result: return value is 1 (it would be 0 if "dawn"
were not skipped)
Making test ideas nonspecific makes them easier to combine.
It's possible to satisfy all of the test ideas in three tests. Why would three
tests that satisfy seven test ideas be better than seven separate tests?
- When you're creating a large number of simple tests, it's common to create
test N+1 by copying test N and tweaking it just enough to satisfy the new
test idea. The result, especially in more complex software, is that test N+1
probably exercises the program in almost the same way as test N. It takes
almost exactly the same path through the code.
A smaller number of tests, each satisfying several test ideas, doesn't allow
a "copy and tweak" approach. Each test will be somewhat different
from the last, exercising the code in different ways and taking different
paths.
Why would that be better? If the Test-Ideas List were complete, with a test
idea for every fault in the program, it wouldn't matter how you wrote the
tests. But the list is always missing some test ideas that could find bugs.
By having each test do very different things from the last one-by adding
seemingly unneeded variety-you increase the chance that one of the tests
will stumble over a bug by sheer dumb luck. In effect, smaller, more complex
tests increase the chance the test will satisfy a test idea that you didn't
know you needed.
- Sometimes when you're creating more complex tests, new test ideas come to
mind. That happens less often with simple tests, because so much of what you're
doing is exactly like the last test, which dulls your mind.
However, there are reasons for not creating complex tests.
- If each test satisfies a single test idea and the test for idea 2 fails,
you immediately know the most likely cause: the program doesn't handle a match
in the last position. If a test satisfies ideas 2, 6, and 7, then isolating
the failure is harder.
- Complex tests are more difficult to understand and maintain. The intent
of the test is less obvious.
- Complex tests are more difficult to create. Constructing a test that satisfies
five test ideas often takes more time than constructing five tests that each
satisfy one. Moreover, it's easier to make mistakes-to think you're satisfying
all five when you're only satisfying four.
In practice, find a reasonable balance between complexity and simplicity.
For example, the first tests you subject the software to (typically the smoke
tests) should be simple, easy to understand and maintain, and intended
to catch the most obvious problems. Later tests should be more complex, but
not so complex they are not maintainable.
After you've finished a set of tests, it's good to check them against the characteristic
test design mistakes discussed in Concepts:
Developer Testing.
A Test-Ideas List is useful for reviews and inspections of design artifacts.
For example, consider this part of a design
model showing the association between Department and Employee classes.
Figure 1: Association between Department and Employee Classes
The rules for creating test ideas from such a model would ask you to consider
the case where a department has many employees. By walking through a design
and asking "what if, at this point, the department has many employees?",
you might discover design or analysis errors. For example, you might realize
that only one employee at a time can be transferred between departments. That
might be a problem if the corporation is prone to sweeping reorganizations where
many employees need to be transferred.
Such faults, cases where a possibility was overlooked, are called faults
of omission. Just like the faults themselves, you have probably omitted
tests that detect these faults from your testing effort. For example, see [GLA81],
[OST84], [BAS87],
[MAR00], and other studies that show
how often faults of omission escape into deployment.
The role of testing in design activities is discussed further in Concepts:
Test-first Design.
Traceability
is a matter of tradeoffs. Is its value worth the cost of maintaining it? This
question needs to be considered during
Activity:
Define Assessment and Traceability Needs .
When traceability is worthwhile, it's conventional to trace tests back to the
artifacts that inspired them. For example, you might have traceability between
an API and its tests. If the API changes, you know which tests to change. If
the code (that implements the API) changes, you know which tests to run. If
a test puzzles you, you can find the API it's intended to test.
The Test-Ideas List adds another level of traceability. You can trace from
a test to the test ideas it satisfies, and then from the test ideas to the original
artifact.
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