Test design concerns making the decisions on (1) what to and what not to test, (2) how to stimulate the system and with what data values, and (3) how the system should react and respond to the stimuli. It is a separate task from test execution and is done before executing the tests against the system. Test design techniques, on the other hand, are used to identify the test scenarios through which the test cases are created.
There are numerous effective and efficient test design techniques for identifying those test scenarios which are applied in the industry. The purpose of this short blog post is to give an overview of some of the most well-known techniques and how you can apply those using tools like Conformiq Designer and Conformiq Creator. These techniques can be divided into static and dynamic techniques where, in this post, we really focus on dynamic techniques only. The dynamic techniques can be broadly divided into two categories called black-box and white-box testing techniques where the “color of the box” refers to the visibility that the test has to the internals of the SUT. Also check out a post about Black-box vs. White-box Coverage at https://www.conformiq.com/2013/11/black-box-vs-white-box-coverage/.
The techniques listed below all have their strengths and weaknesses; they are good in finding certain types of defects while they easily miss other types of defects. It is really up to the end user to decide what is important from the testing point of view and then to design test assets by applying a combination of techniques. For any real system, carrying this out manually or even with limited automation easily becomes prohibitively difficult.
In addition, while techniques listed below have shown to be an effective way of finding software faults, it is crucial to keep in mind that these techniques are only a part of the whole picture and a technique by itself has only limited value and use. Even if we design an “optimized” set of test inputs for stimulating the system under test by applying a certain test design technique, we also need to be able to understand how the system should react to each input. This is part of the “test oracle problem” which when implemented manually is a huge endeavor, takes considerable time, and is a very error prone process.
The beauty of the Conformiq test generation platform is that it can apply those techniques automatically but also fully automate the test oracle generation, saving a lot of time and increasing the quality of testing at the same time. This means that the Conformiq test generation platform – the engine core of both Conformiq Designer and Creator – will automatically design and create test cases with input combinations used as stimuli to the system under test plus an exact expected response from the SUT. There is no need to do additional (manual) test design.
Black-box testing techniques
Black-box approach assumes no internal details of the system and is based on judging the quality and correctness of the system using an external reference such as system specification. The following is a list of major types of test design techniques.
Equivalence class partitioning
The idea of equivalence class partitioning is to divide the all possible inputs to the system into “equivalence classes”, i.e. sets of inputs that should produce “analogous” results and “work the same”. By applying the equivalence class partitioning technique we test only a few conditions (maybe even one) from each partition as it is assumed that all the conditions in a given partition will trigger the same behavior in the SUT. If that condition is working properly, then all the other conditions within the partition are assumed to work properly, meaning that we can dramatically narrow down the number of test scenarios that we need to execute. Similarly, if that one condition happens to trigger an error in the SUT then it is also assumed that all the other conditions will have the same effect. For more information about equivalence class partitioning, check out this post https://www.conformiq.com/2013/12/equivalence-class-partitioning-and-boundary-value-analysis-as-black-box-test-design-heuristics/.
Boundary value analysis
Boundary value analysis or BVA is a refinement of the equivalence class partitioning method which is an applicable method when the equivalence classes involve numbers and there are decision boundaries between them. These decision boundaries are places where the behavior of the system changes. What makes boundary value analysis an interesting method is that it is widely recognized that values on the boundaries cause more errors in the system than at other locations. Therefore we should be always checking the boundaries since if the system fails, it is likely to fail on these decision boundaries. For more information about BVA, check out this post https://www.conformiq.com/2013/12/equivalence-class-partitioning-and-boundary-value-analysis-as-black-box-test-design-heuristics/.
Decision or cause-effect tables
Decision tables are used represent how combinations of inputs result in different actions taken and thus it is a testing technique focused more on business logic and rules. While the above mentioned equivalence class partitioning and boundary value methods are extremely useful methods for narrowing down the number of inputs and apply to a specific condition or input, decision tables are used define the system action given a combination of these inputs. It is a systematic way of describing complex business rules and they help the tester to understand and see the effect of combinations of different inputs. This way they help the tester to identify a subset of the combinations to test, but decision tables themselves do not provide an efficient way of selecting the actual combinations. Therefore, applying decision tables in testing may well result in an inefficient and poor test result.
Use case testing
Use case testing is a method that utilizes specification assets called Use Cases which are descriptions of a particular use of the system by the end user. Use Cases can be used to design and capture test cases that exercise the whole end-to-end system. As Use Cases are described in terms of the end user and they describe what the user does with system. They do not describe the inputs that should be used to stimulate the system nor the (exact) expected response. However Use Cases may be very valuable in the test design process for establishing an understanding about the type of test scenarios that should be included in to the testing work.
Combinatorial testing (all-pairs, n-wise, etc)
The basis for combinatorial testing is the interaction principle which states that most software failures are induced by single “factor” faults or by a combinatorial effect–that is interaction–of two factors, with progressively fewer failures induced by interactions between more factors. Therefore, if a significant part of the faults in the system under test can be induced by a combination of N or fewer factors, then testing all N-way combinations of values provides a high rate of fault detection. This is also what has been empirically observed over the years. Combinatorial testing is applied to input parameters and tools for combinatorial testing then attempts to optimize the number of tests you need to run. For more information about combinatorial testing, check out this post https://www.conformiq.com/2014/10/combinatorial-test-data-generation-with-conformiq/.
Classification tree method
The classification-tree method is an approach to partition testing which uses a descriptive tree-like notation. The basic idea is to separate the inputs of the SUT into different classes that directly reflect the relevant test scenarios or classifications which is then followed by test case selection by combining classes of the different classifications. The selection of classes typically follows techniques such as equivalence class partitioning and boundary value analysis. All the classifications form the classification tree. In the next step the test cases are composed by selecting one class from every classification of the classification tree. The selection of test cases can be done manually but there are tools existing that automate and optimize this process.
White-box testing techniques
White-box approach assumes that the tester has a full visibility to the box being tested and how the system internally operates. White-box methods are based on the structure of the implementation and therefore the applied techniques are also referred as structure-based techniques.
Statement or line coverage
Statement coverage identifies statements or code lines executed by a test suite and then it calculates the percentage of executed statements versus those that are not executed.
Decision or branch coverage
Decision or branch coverage answers the question has each branch of a control flow construct been executed or to put in in different terms, has each possible outcome of a decision been executed. For example, with an “if” statement this means do we have a test that exercises the “then” branch and a test that exercises the “else” branch.
Condition or predicate coverage
Condition or predicate coverage answers the question has each Boolean sub-expression been evaluated for both true and false where a condition is a Boolean sub-expression that cannot be broken down into a simpler Boolean expression.
More advanced condition / decision coverage
There are in addition numerous more advanced and exhaustive ways to test and asses the completeness of testing in terms of more advanced condition and decision coverage heuristics such as MC/DC (Modified Condition / Decision Coverage) that requires that each condition should affect the decision outcome separately and MCC (Multiple Condition Coverage) that requires that all the combinations of conditions inside decisions are tested.
For more information about various condition and decision coverage options, check out this post https://www.conformiq.com/2014/11/conditional-coverage-heuristics/.
With path testing we aim to cover every control path at least once which also includes all the iterations / loops that the code might have for zero, one and multiple times. If the code includes unbounded iteration the number of tests needed for testing the code is also unbounded which naturally is not practical, therefore for path testing in practice we fix some upper bound for these constructs to limit the number of test cases to a practical size.
Putting it all together
The basic idea is that a test design technique helps us to identify a “good” subset of test cases from infinitely many more. Each of the techniques listed above focuses on certain specific characteristics of the system and has its own strengths and weaknesses. Focusing only on one particular test design technique is therefore not really enough, as each technique is good at identifying only certain types of issues with the implementation and is hopeless in finding others. It is really up to you to select the best set of techniques for identifying the test cases that meet your testing requirements and goals.
When it comes to the classification of test design techniques discussed in this post, it is also worth noting that black-box and white-box test design are not mutually exclusive and even if I have categorized structural coverage aspects under white-box test design techniques, it does not mean that you could not apply those techniques also when doing black-box design. What we need to remember is that black-box techniques assume no visibility in to the tested system meaning that we cannot apply those design techniques on white-box aspects. What we can do however when we conduct model-based black-box test design, is to apply those techniques classified under white-box techniques to the model and not to the actual implementation. By applying structure based test design techniques for black-box test design from models we can improve the quality of the testing because we increase the coverage of the testing and therefore we increase the chances of finding defects in the implementation that singular test design techniques might overlook.
In addition, it is important to note that a test suite designed using a black-box approach (even when also applying white-box test design techniques on the model) with demonstrably good coverage of the functional specification (whether implicit or explicit) does not necessary mean a high white-box coverage measurement. This is because there can be disproportionate amounts of code in the system that are either unused or dead code or that are related to unspecified functionality. It is known that good coverage of functional requirements does not always lead to high white-box coverage due to the aforementioned issues. On the other hand, good white-box coverage can be of only limited value if the tests are derived from the code itself. The key point of black-box testing is that the system is judged against an independent reference. For more information, refer to this article https://www.conformiq.com/2013/11/black-box-vs-white-box-coverage/.
Testing techniques with Conformiq test generation platform
As I mentioned early in this writing, an additional and a very big problem with the test design techniques mentioned in this blog post is that they are typically explained with simple examples, while in the real world these methods need to be applied—for example in the case of equivalence class partitioning—in relation to every equivalence class of the reachable system states of the application that we are testing. For real world systems, carrying this out manually easily becomes prohibitively difficult or too time consuming to be practical.
A major benefit of the Conformiq test generation platform is the it implements the selected set of test design techniques automatically across the whole system specification (as encoded in a model). The tool is able to generate test suites that cover the aspects of all the user selected set of techniques in combination (and if not produce a report of what failed to be covered during the test generation) that at the same time are of reasonable size with full coverage and traceability information. By using an automated test design tool, you are relieved from doing test design by hand; a time-consuming and error-prone process for comprehensive coverage that is also very complicated, if not nearly impossible, for larger systems. The combination of coverage methods is a unique value proposition of the Conformiq test generation platform that separates this platform from other model-based testing approaches and tools available in the market.