However, as soon as metamodels become large, navigating through large metamodels becomes a tedious task that hinders their understanding. To support a comprehensive comparison, for each we review its concepts, principles, procedures, examples, strengths, weaknesses, and related research areas in Sections 3.1 Combinatorial Testing , 3.2 Random Testing , 3.3 Adaptive Random Testing . In Section 3.4 we detail the stopping conditions used for CT, RT and ART. Can be a multiset, because there can be multiple parameters having same number of choices.

As reducing the switching cost can make the whole test suite run faster and thus achieve full combination coverage earlier, single-objective algorithms were used to minimize the total switching cost. Besides, when determining the next test case to run, there is a trade-off between high combination coverage and low switching cost. Hence, hybrid and multi-objective algorithms were used to achieve a better balance. In order to evaluate different algorithms, we conducted a series of experiments covering 400 different testing scenarios. We also conducted an empirical study with six real world applications.

So instead a subset of combinations is generated to satisfy all factors. Our approach assumes that there is no constraint defined across LHS and RHS. However, it is usual not to have such an assumption in practical situations, especially when we construct a VSCA for a system with multiple components.

A real-world software product has numerous parameters, which causes a combinatorial explosion when conducting a fully exhaustive testing. A CIT technique provides a way to handle this situation while guaranteeing reasonable coverage over all combinations of possible parameter values. However, generating a test suite employing the CIT technique is an expensive process, particularly when complicated constraints over the parameters are present. One approach to solve this issue is to generate test suites for components in the system separately and then combine them into one.

The Practical Testing Book

As shown in this discussion, there are various tools each of which has its distinct pros and cons and it is beneficial to employ the combinatorial join technique to combine covering arrays built by different tools. In strength 2, our approach increases the size of output covering array by 35%–90%, and the size penalty becomes 41%–141% in strength 3, to generate a covering array from scratch or incrementally. For VSCA generation, with strengths 2 and 3, the size penalty is −46%–15%. The size penalty becomes smaller when more factors and more complex constraints are given. In order to generate covering arrays in our experiments, we need an external tool that executes the process and we chose ACTS for it. The reason why we chose ACTS is because it is not only widely used but also the fastest one among the tools available for us.

• Model-Driven Development and Service-Oriented Architecture are two challenging research areas in software engineering.
• Another intuitive tool for performing combinatorial testing is testcover.com where factors, values, and constraints are simply written in the editor, and test configurations are generated.
• Specifically, “resource conflict” refers to a type of bug that is triggered by conflicting usage of resources shared among multiple components.
• After the template is filled with data, we create specific test cases using equivalence partitioning and boundary values techniques.
• Scenario, on the other hand, is a sequence of actions that lead to accomplishing some specific goal.
• Where tuples is a function that returns a set of all the t-way tuples in an array A.
• However, the implementation was impractically expensive in terms of time and memory usage when there are more than 100 parameters or strength t exceeds 2.

As mentioned already, we measure the generation time and size of output covering arrays , for various set of settings along with different number of parameters. One suite of settings is characterized by G eneration Scenario and D esired Covering Array Model, which usually consists of D egree, R ank, S trength, and C onstraint Set. We describe each of there independent variables in our evaluation in the next sections. A variable strength covering array is a covering array where the strength t can be different depending a set of parameters among all of them (Cohen et al., 2003).

Especially, we expect our approach accelerates the generation of a covering array with a large number of parameters in higher strength or under complex constraints. Because in such situations, the generation time grows more rapid than linear and the approach makes it possible to apply “divide-and-conquer” to build the final output covering array. To maximize the improvement, we use the same model for generating both LHS and RHS covering arrays, because in this case the input arrays for the join operation are generated in the same amount of time.

Combinatorial Testing

For example, when ACTS generates a covering array of CA with no constraint, it takes less than 1.0 and the size of the generated covering array is 14. Another popular tool, PICT can generate a covering array of CA in less than 1.0 with 15 rows, but it shows quite unpractical performance when a complex constraint set is present . As discussed in ‘Flexibility of weaken product combinatorial join’, testing parameters sometimes have quite different value changing costs. An OS-level parameter such as file system type might take hours to change, while an application level parameter value such as a text font type takes less than a second.

Project is a function that returns an array created from an input array A and a set of factors f. When output of the weaken is constructed, depending on the order of selecting rows from A, the size of the output can be different. Our implementation chooses to select a row that contains the most key-value pairs that are not covered in the output so far. For constraint handling, PICT provides quite readable notation as shown below. ACTS supports four data types, which are bool, number, enum, and range. The following code block contains examples to define factors of those types.

Specification-Based Testing Techniques

Combinatorial join assumes that input arrays are already covering arrays and a new row in the output is created by connecting rows in the input arrays so that the entire output becomes a new covering array which has all the parameters to test. Ukai et al. presented an implementation of the combintorial join operation based on a covering array generation algorithm called IPOG (Lei et al., 2008). However, the implementation was impractically expensive in terms of time and memory usage when there are more than 100 parameters or strength t exceeds 2. In a practical software system each parameter cannot be assigned independently. Instead, parameter values must be selected so that a certain set of conditions are satisfied.

In this work, we propose a mathematical operation, called “weaken-product based combinatorial join”, which constructs a new covering array from two existing covering arrays. The operation reuses existing covering arrays to save computational resource by increasing parallelism during generation without losing combinatorial coverage of the original arrays. Our proposed method significantly reduce the covering array generation time by 13–96% depending on use case scenarios.

Numerous open source and free tools exist to generate pairwise value sets. The tester must inform the tool about how the application functions definition of combinatorial testing for these value sets to be effective. Grad school, Software Engineering, Automated Combinatorial Testing for Software project.

CAGEN: A fast combinatorial test generation tool with support for constraints and higher-index arrays

This technique seems fairly easy, but sometimes you can overlook boundaries because they are not so obvious. Don’t miss partitions (e.g. out of valid range), don’t miss sequences and open boundaries, choose the right “step” and this simple technique will save you plenty of time. Hexawise’s Auto-Scripts feature creates detailed tester instructions from sets of optimized test conditions. The project combined constructive research, which involved a broad range of quality experts from academia and industry in workshops, sprint work and reviews, with empirical studies. All deliverables within the project were peer-reviewed by two project members from a different area. Most deliverables were developed in two or three iterations and underwent an evaluation.

In summary, the contributions of this work are as follows, which altogether enhance the applicability of CIT toward the larger and more complex software products in the real world. For attribution, the original author, title, publication source and either DOI or URL of the article must be cited. The least formal technique of all, and for many people it might be just a matter of course, like “be a QA and use your experience”.

ITF and quality metrics for test suites

These classes are “Data Corruption caused by Resource conflict”, “Incorrect abstraction”, and “Unintended dependencies between components”. At the same time, by reusing test oracles, functionality testing cost can be reduced in system level testing. As shown in the figures, as the degree increases, our approach reduces the generation more remarkably. When the strength is 2 and degree is 980, the time is reduced by 21% to 25% or more, with or without constraint sets.

Orthogonal Array Testing – Orthogonal Array testing and pairwise testing are very similar in many important respects. They are both well established methods of generating small sets of unusually powerful tests that will find a disproportionately high number of defects in relatively few tests. The main difference between the two approaches is that pairwise testing coverage only requires that every pair of parameter values appear together in at least one test in a generated set. Orthogonal Array-based test designs, in contrast, have an added requirement that there be a uniform distribution throughout the domain. This added requirement tends to result in significantly more tests for Orthogonal Array Testing solutions than are required for pairwise test sets. All-pairs testing, also known as pairwise testing, is a software quality assurance technique that involves a combination of expected input and output values.

A Survey of Constrained Combinatorial Testing

Many popular tools (Kuhn, Kacker & Lei, 2008; Cohen et al., 1997a; Czerwonka, 2006) have been implemented in this category. These tools can handle new parameters that are https://globalcloudteam.com/ not present in the initial covering array and generate an output that covers all combinations. This feature is usually called ‘seeding’ or ‘incremental generation’.

Just imagine that test cases are generated, executed and evaluated automatically in a continuous cycle, allowing you to run thousands and millions of cases within a reasonable time. Identification of test relevant aspects (aspects that affect functionality — so called classifications) and their corresponding values . If separate rights are desired for different test plans put those test plans in a separate project and define the access rights for that project as desired.

One is to split the mode into LHS and RHS and the other is to generate covering arrays for them respectively. One is to divide the input into two groups each of which has the same number of factors. We also expect it to accelerate the overall generation even with the overhead of combining smaller input covering arrays and enhance the applicability of CIT technique toward the larger and more complicated software products. In this section, we first illustrate the procedure of our proposed technique “weaken-product based combinatorial join” with a running example, which implements the “combinatorial join” operation.

Known CIT tools have different characteristics in performance (i.e., generation time), size efficiencies and capabilities, as described in Table 7. As we can see from the table, each tool has its own strengths and weaknesses. The first one refers to a scenario, in which a covering array is generated from a couple of given models from scratch. In this scenario, we expect that our approach can improve the overall generation time by executing a CIT tool concurrently and then combining the arrays generated in parallel.

Mistakes can be avoided while doing Mobile App Testing.

Research has reported that ART can improve RT greatly for many applications , but we have not seen any work in using ART to hit MFS. When a test case of RT is executed but fails to find an ITF, no value combinations of this test case can reveal ITFs. With this in mind, when selecting the next test case for RT, we should ensure that the next test case covers more new combinations, so that we may have a higher chance to detect an ITF. The fundamental idea behind ART is to reward diversity among sampled test cases.