Software Testing Tutorials
Software Testing is the process of executing a program or system with the
intent of finding errors.
A D V E R T I S E M E N T
Or, it involves any activity aimed at evaluating an attribute or
capability of a program or system and determining that it meets its required
results. Software is not unlike other physical processes where inputs are
received and outputs are produced. Where software differs is in the manner in
which it fails. Most physical systems fail in a fixed (and reasonably small) set
of ways. By contrast, software can fail in many bizarre ways. Detecting all of
the different failure modes for software is generally infeasible.
Unlike most physical systems, most of the defects in software are design
errors, not manufacturing defects. Software does not suffer from corrosion,
wear-and-tear -- generally it will not change until upgrades, or until
obsolescence. So once the software is shipped, the design defects -- or bugs --
will be buried in and remain latent until activation.
Software bugs will almost always exist in any software module with moderate
size: not because programmers are careless or irresponsible, but because the
complexity of software is generally intractable -- and humans have only limited
ability to manage complexity. It is also true that for any complex systems,
design defects can never be completely ruled out.
Discovering the design defects in software, is equally difficult, for the
same reason of complexity. Because software and any digital systems are not
continuous, testing boundary values are not sufficient to guarantee correctness.
All the possible values need to be tested and verified, but complete testing is
infeasible. Exhaustively testing a simple program to add only two integer inputs
of 32-bits (yielding 2^64 distinct test cases) would take hundreds of years,
even if tests were performed at a rate of thousands per second. Obviously, for a
realistic software module, the complexity can be far beyond the example
mentioned here. If inputs from the real world are involved, the problem will get
worse, because timing and unpredictable environmental effects and human
interactions are all possible input parameters under consideration.
A further complication has to do with the dynamic nature of programs. If a
failure occurs during preliminary testing and the code is changed, the software
may now work for a test case that it didn't work for previously. But its
behavior on pre-error test cases that it passed before can no longer be
guaranteed. To account for this possibility, testing should be restarted. The
expense of doing this is often prohibitive.
An interesting analogy parallels the difficulty in software testing with the
pesticide, known as the Pesticide Paradox
: Every method you use to prevent or find bugs leaves a residue
of subtler bugs against which those methods are ineffectual. But this alone
will not guarantee to make the software better, because the Complexity Barrier
principle states: Software complexity(and therefore that of
bugs) grows to the limits of our ability to manage that complexity. By
eliminating the (previous) easy bugs you allowed another escalation of features
and complexity, but his time you have subtler bugs to face, just to retain the
reliability you had before. Society seems to be unwilling to limit complexity
because we all want that extra bell, whistle, and feature interaction. Thus, our
users always push us to the complexity barrier and how close we can approach
that barrier is largely determined by the strength of the techniques we can
wield against ever more complex and subtle bugs.
Regardless of the limitations, testing is an integral part in software
development. It is broadly deployed in every phase in the software development
cycle. Typically, more than 50% percent of the development time is spent in
testing. Testing is usually performed for the following purposes:
As computers and software are used in critical applications, the outcome of a
bug can be severe. Bugs can cause huge losses. Bugs in critical systems have
caused airplane crashes, allowed space shuttle missions to go awry, halted
trading on the stock market, and worse. Bugs can kill. Bugs can cause disasters.
The so-called year 2000 (Y2K) bug has given birth to a cottage industry of
consultants and programming tools dedicated to making sure the modern world
doesn't come to a screeching halt on the first day of the next century.
In a computerized embedded world, the quality and reliability of software is a
matter of life and death.
Quality means the conformance to the specified design requirement. Being
correct, the minimum requirement of quality, means performing as required under
specified circumstances. Debugging, a narrow view of software testing, is
performed heavily to find out design defects by the programmer. The imperfection
of human nature makes it almost impossible to make a moderately complex program
correct the first time. Finding the problems and get them fixed
, is the purpose of debugging in programming phase.
- For Verification & Validation (V&V)
Just as topic
Verification and Validation indicated, another important purpose of testing
is verification and validation (V&V). Testing can serve as metrics. It is
heavily used as a tool in the V&V process. Testers can make claims based on
interpretations of the testing results, which either the product works under
certain situations, or it does not work. We can also compare the quality among
different products under the same specification, based on results from the same
We can not test quality directly, but we can test related factors to make
quality visible. Quality has three sets of factors -- functionality,
engineering, and adaptability. These three sets of factors can be thought of as
dimensions in the software quality space. Each dimension may be broken down into
its component factors and considerations at successively lower levels of detail.
Table 1 illustrates some of the most frequently cited quality considerations.
|Functionality (exterior quality)
||Engineering (interior quality)
||Adaptability (future quality)
Typical Software Quality Factors
Good testing provides measures for all relevant factors. The importance of
any particular factor varies from application to application. Any system where
human lives are at stake must place extreme emphasis on reliability and
integrity. In the typical business system usability and maintainability are the
key factors, while for a one-time scientific program neither may be significant.
Our testing, to be fully effective, must be geared to measuring each relevant
factor and thus forcing quality to become tangible and visible.
Tests with the purpose of validating the product works are named clean tests,
or positive tests. The drawbacks are that it can only validate that the software
works for the specified test cases. A finite number of tests can not validate
that the software works for all situations. On the contrary, only one failed
test is sufficient enough to show that the software does not work. Dirty tests,
or negative tests, refers to the tests aiming at breaking the software, or
showing that it does not work. A piece of software must have sufficient
exception handling capabilities to survive a significant level of dirty tests.
A testable design is a design that can be easily validated, falsified and
maintained. Because testing is a rigorous effort and requires significant time
and cost, design for testability is also an important design rule for software
- For reliability estimation
Software reliability has important relations with many aspects of software,
including the structure, and the amount of testing it has been subjected to.
Based on an operational profile (an estimate of the relative frequency of use of
various inputs to the program
testing can serve as a statistical sampling method to gain failure data for
Software testing is not mature. It still remains an art, because we still
cannot make it a science. We are still using the same testing techniques
invented 20-30 years ago, some of which are crafted methods or heuristics rather
than good engineering methods. Software testing can be costly, but not testing
software is even more expensive, especially in places that human lives are at
stake. Solving the software-testing problem is no easier than solving the Turing
halting problem. We can never be sure that a piece of software is correct. We
can never be sure that the specifications are correct. No verification system
can verify every correct program. We can never be certain that a verification
system is correct either.
There is a plethora of testing methods and testing techniques, serving
multiple purposes in different life cycle phases. Classified by purpose,
software testing can be divided into: correctness testing, performance testing,
reliability testing and security testing. Classified by life-cycle phase,
software testing can be classified into the following categories: requirements
phase testing, design phase testing, program phase testing, evaluating test
results, installation phase testing, acceptance testing and maintenance testing.
By scope, software testing can be categorized as follows: unit testing,
component testing, integration testing, and system testing.
Correctness is the minimum requirement of software, the essential purpose of
testing. Correctness testing will need some type of oracle, to tell the right
behavior from the wrong one. The tester may or may not know the inside details
of the software module under test, e.g. control flow, data flow, etc. Therefore,
either a white-box point of view or black-box point of view can be taken in
testing software. We must note that the black-box and white-box ideas are not
limited in correctness testing only.
The black-box approach is a testing method in which test data are derived
from the specified functional requirements without regard to the final program
It is also termed data-driven, input/output driven
, or requirements-based
testing. Because only the functionality of the software module is
of concern, black-box testing also mainly refers to functional testing -- a
testing method emphasized on executing the functions and examination of their
input and output data.
The tester treats the software under test as a black box -- only
the inputs, outputs and specification are visible, and the functionality is
determined by observing the outputs to corresponding inputs. In testing, various
inputs are exercised and the outputs are compared against specification to
validate the correctness. All test cases are derived from the specification. No
implementation details of the code are considered.
It is obvious that the more we have covered in the input space, the more
problems we will find and therefore we will be more confident about the quality
of the software. Ideally we would be tempted to exhaustively test the input
space. But as stated above, exhaustively testing the combinations of valid
inputs will be impossible for most of the programs, let alone considering
invalid inputs, timing, sequence, and resource variables. Combinatorial
explosion is the major roadblock in functional testing. To make things worse, we
can never be sure whether the specification is either correct or complete. Due
to limitations of the language used in the specifications (usually natural
language), ambiguity is often inevitable. Even if we use some type of formal or
restricted language, we may still fail to write down all the possible cases in
the specification. Sometimes, the specification itself becomes an intractable
problem: it is not possible to specify precisely every situation that can be
encountered using limited words. And people can seldom specify clearly what they
want -- they usually can tell whether a prototype is, or is not, what they want
after they have been finished. Specification problems contributes approximately
30 percent of all bugs in software.
The research in black-box testing mainly focuses on how to maximize the
effectiveness of testing with minimum cost, usually the number of test cases. It
is not possible to exhaust the input space, but it is possible to exhaustively
test a subset of the input space. Partitioning is one of the common techniques.
If we have partitioned the input space and assume all the input values in a
partition is equivalent, then we only need to test one representative value in
each partition to sufficiently cover the whole input space. Domain testing
partitions the input domain into regions, and consider the input
values in each domain an equivalent class. Domains can be exhaustively tested
and covered by selecting a representative value(s) in each domain. Boundary
values are of special interest. Experience shows that test cases that explore
boundary conditions have a higher payoff than test cases that do not. Boundary
requires one or more boundary values selected as representative
test cases. The difficulties with domain testing are that incorrect domain
definitions in the specification can not be efficiently discovered.
Good partitioning requires knowledge of the software structure. A good
testing plan will not only contain black-box testing, but also white-box
approaches, and combinations of the two.
Contrary to black-box testing, software is viewed as a white-box, or
glass-box in white-box testing, as the structure and flow of the software under
test are visible to the tester. Testing plans are made according to the details
of the software implementation, such as programming language, logic, and styles.
Test cases are derived from the program structure. White-box testing is also
called glass-box testing, logic-driven testing
or design-based testing
There are many techniques available in white-box testing, because the problem
of intractability is eased by specific knowledge and attention on the structure
of the software under test. The intention of exhausting some aspect of the
software is still strong in white-box testing, and some degree of exhaustion can
be achieved, such as executing each line of code at least once (statement
coverage), traverse every branch statements (branch coverage), or cover all the
possible combinations of true and false condition predicates (Multiple condition
Control-flow testing, loop testing, and data-flow testing, all maps the
corresponding flow structure of the software into a directed graph. Test cases
are carefully selected based on the criterion that all the nodes or paths are
covered or traversed at least once. By doing so we may discover unnecessary
"dead" code -- code that is of no use, or never get executed at all, which can
not be discovered by functional testing.
In mutation testing, the original program code is perturbed and many mutated
programs are created, each contains one fault. Each faulty version of the
program is called a mutant. Test data are selected based on the effectiveness of
failing the mutants. The more mutants a test case can kill, the better the test
case is considered. The problem with mutation testing is that it is too
computationally expensive to use. The boundary between black-box approach and
white-box approach is not clear-cut. Many testing strategies mentioned above,
may not be safely classified into black-box testing or white-box testing. It is
also true for transaction-flow testing, syntax testing, finite-state testing,
and many other testing strategies not discussed in this text. One reason is that
all the above techniques will need some knowledge of the specification of the
software under test. Another reason is that the idea of specification itself is
broad -- it may contain any requirement including the structure, programming
language, and programming style as part of the specification content.
We may be reluctant to consider random testing as a testing technique. The
test case selection is simple and straightforward: they are randomly chosen.
Study in indicates that random testing is more cost effective for many
programs. Some very subtle errors can be discovered with low cost. And it is
also not inferior in coverage than other carefully designed testing techniques.
One can also obtain reliability estimate using random testing results based on
operational profiles. Effectively combining random testing with other testing
techniques may yield more powerful and cost-effective testing strategies.
Not all software systems have specifications on performance explicitly. But
every system will have implicit performance requirements. The software should
not take infinite time or infinite resource to execute. "Performance bugs"
sometimes are used to refer to those design problems in software that cause the
system performance to degrade.
Performance has always been a great concern and a driving force of computer
evolution. Performance evaluation of a software system usually includes:
resource usage, throughput, stimulus-response time and queue lengths detailing
the average or maximum number of tasks waiting to be serviced by selected
resources. Typical resources that need to be considered include network
bandwidth requirements, CPU cycles, disk space, disk access operations, and
. The goal of performance testing can be performance bottleneck
identification, performance comparison and evaluation, etc. The typical method
of doing performance testing is using a benchmark -- a program, workload or
trace designed to be representative of the typical system usage.
Software reliability refers to the probability of failure-free operation of a
system. It is related to many aspects of software, including the testing
process. Directly estimating software reliability by quantifying its related
factors can be difficult. Testing is an effective sampling method to measure
software reliability. Guided by the operational profile, software testing
(usually black-box testing) can be used to obtain failure data, and an
estimation model can be further used to analyze the data to estimate the present
reliability and predict future reliability. Therefore, based on the estimation,
the developers can decide whether to release the software, and the users can
decide whether to adopt and use the software. Risk of using software can also be
assessed based on reliability information.
advocates that the primary goal of testing should be to measure
the dependability of tested software.
There is agreement on the intuitive meaning of dependable software: it does
not fail in unexpected or catastrophic ways.
Robustness testing and stress testing are variances of
reliability testing based on this simple criterion.
The robustness of a software component is the degree to which it can function
correctly in the presence of exceptional inputs or stressful environmental
Robustness testing differs with correctness testing in the sense that the
functional correctness of the software is not of concern. It only watches for
robustness problems such as machine crashes, process hangs or abnormal
termination. The oracle is relatively simple, therefore robustness testing can
be made more portable and scalable than correctness testing. This research has
drawn more and more interests recently, most of which uses commercial operating
systems as their target, such as the work in
Stress testing, or load testing, is often used to test the whole system
rather than the software alone. In such tests the software or system are
exercised with or beyond the specified limits. Typical stress includes resource
exhaustion, bursts of activities, and sustained high loads.
Software quality, reliability and security are tightly coupled. Flaws in
software can be exploited by intruders to open security holes. With the
development of the Internet, software security problems are becoming even more
Many critical software applications and services have integrated security
measures against malicious attacks. The purpose of security testing of these
systems include identifying and removing software flaws that may potentially
lead to security violations, and validating the effectiveness of security
measures. Simulated security attacks can be performed to find vulnerabilities.
Software testing can be very costly. Automation is a good way to cut down
time and cost. Software testing tools and techniques usually suffer from a lack
of generic applicability and scalability. The reason is straight-forward. In
order to automate the process, we have to have some ways to generate oracles
from the specification, and generate test cases to test the target software
against the oracles to decide their correctness. Today we still don't have a
full-scale system that has achieved this goal. In general, significant amount of
human intervention is still needed in testing. The degree of automation remains
at the automated test script level.
The problem is lessened in reliability testing and performance testing. In
robustness testing, the simple specification and oracle: doesn't crash, doesn't
hang suffices. Similar simple metrics can also be used in stress testing.
When to stop testing?
Testing is potentially endless. We can not test till all the defects are
unearthed and removed -- it is simply impossible. At some point, we have to stop
testing and ship the software. The question is when.
Realistically, testing is a trade-off between budget, time and quality. It is
driven by profit models. The pessimistic, and unfortunately most often used
approach is to stop testing whenever some, or any of the allocated resources --
time, budget, or test cases -- are exhausted. The optimistic stopping rule is to
stop testing when either reliability meets the requirement, or the benefit from
continuing testing cannot justify the testing cost.
This will usually require the use of reliability models to evaluate and predict
reliability of the software under test. Each evaluation requires repeated
running of the following cycle: failure data gathering -- modeling --
prediction. This method does not fit well for ultra-dependable systems, however,
because the real field failure data will take too long to accumulate.
Alternatives to testing
Software testing is more and more considered a problematic method toward
better quality. Using testing to locate and correct software defects can be an
endless process. Bugs cannot be completely ruled out. Just as the complexity
barrier indicates: chances are testing and fixing problems may not necessarily
improve the quality and reliability of the software. Sometimes fixing a problem
may introduce much more severe problems into the system, happened after bug
fixes, such as the telephone outage in California and eastern seaboard in 1991.
The disaster happened after changing 3 lines of code in the signaling system.
In a narrower view, many testing techniques may have flaws. Coverage testing,
for example. Is code coverage, branch coverage in testing really related to
software quality? There is no definite proof. As early as in
, the so-called "human testing" -- including inspections,
walkthroughs, reviews -- are suggested as possible alternatives to traditional
advocates inspection as a cost-effect alternative to unit
testing. The experimental results in
suggests that code reading by stepwise abstraction is at least as
effective as on-line functional and structural testing in terms of number and
cost of faults observed.
Using formal methods to "prove" the correctness of software is also an
attracting research direction. But this method can not surmount the complexity
barrier either. For relatively simple software, this method works well. It does
not scale well to those complex, full-fledged large software systems, which are
In a broader view, we may start to question the utmost purpose of testing.
Why do we need more effective testing methods anyway, since finding defects and
removing them does not necessarily lead to better quality. An analogy of the
problem is like the car manufacturing process. In the craftsmanship epoch, we
make cars and hack away the problems and defects. But such methods were washed
away by the tide of pipelined manufacturing and good quality engineering
process, which makes the car defect-free in the manufacturing phase. This
indicates that engineering the design process (such as clean-room software
engineering) to make the product have less defects may be more effective than
engineering the testing process. Testing is used solely for quality monitoring
and management, or, "design for testability". This is the leap for software from
craftsmanship to engineering.
Available tools, techniques, and metrics
There are an abundance of software testing tools exist. The correctness
testing tools are often specialized to certain systems and have limited ability
and generality. Robustness and stress testing tools are more likely to be made
is an automated mutation testing tool-set developed at Purdue
University. Using Mothora, the tester can create and execute test cases, measure
test case adequacy, determine input-output correctness, locate and remove faults
or bugs, and control and document the test.
. They are run-time checking and debugging aids. They can both
check and protect against memory leaks and pointer problems.
Ballista COTS Software Robustness Testing Harness
. The Ballista testing harness is an full-scale automated
robustness testing tool. The first version supports testing up to 233 POSIX
function calls in UNIX operating systems. The second version also supports
testing of user functions provided that the data types are recognized by the
testing server. The Ballista testing harness gives quantitative measures of
robustness comparisons across operating systems. The goal is to automatically
test and harden Commercial Off-The-Shelf (COTS) software against robustness
Relationship to other topics
Software testing is an integrated part in software development. It is
directly related to software quality. It has many subtle relations to the topics
that software, software quality, software reliability and system reliability are
- Software testing is an art. Most of the testing methods and practices
are not very different from 20 years ago. It is nowhere near maturity,
although there are many tools and techniques available to use. Good testing
also requires a tester's creativity, experience and intuition, together with
- Testing is more than just debugging. Testing is not only used to locate
defects and correct them. It is also used in validation, verification
process, and reliability measurement.
- Testing is expensive. Automation is a good way to cut down cost and
time. Testing efficiency and effectiveness is the criteria for
coverage-based testing techniques.
- Complete testing is infeasible. Complexity is the root of the problem.
At some point, software testing has to be stopped and product has to be
shipped. The stopping time can be decided by the trade-off of time and
budget. Or if the reliability estimate of the software product meets
- Testing may not be the most effective method to improve software
quality. Alternative methods, such as inspection, and clean-room
engineering, may be even better.