Combinatorial Test Design: Who needs it and whats it good for? IBM Research Blog

153

Combinations of configuration values or parameters, in which the covering arrays are used to select the values of configurable parameters, possibly with the assistance of same tests that run against all configuration combinations. One approach that can be used is to ensure that all relevant combinations of input values have been tested and verified for correct operation. Our combinatorial coverage measures provide an efficient means of achieving this type of verification, and validating it in real-world use. The method has been implemented in a prototype tool called ComXAI, which we are currently applying to machine learning problems. Explainability is key in both using and assuring safety and reliability for autonomous systems and other applications of AI and machine learning.

• It is a test design technique that delivers hundred percent test coverage.
• Though pairwise testing can dramatically reduce combinations, it still remains really effective in terms of fault detections and is indeed a smart test design technique that promises best test efforts and exceptional effectiveness.
• Our combinatorial methods and tools for assured autonomy address both verification and validation.
• Developing different applications specific to a company has become the norm for every successful business in today’s world.
• See articles onhigh assurance software testingorsecurity and reliability.
• Combinatorial or t-way testingis a proven method for more effective testing at lower cost.
• See articles on high assurance software testingor security and reliability.

Firstly, it allows us to create multiple input parameter models instead of just one like in the previous tool. Also, we can save these models as well and export them to a different type of files. Another advantage of this tool is that it can use three different algorithms (FIPOG, FIPOG-F, FIPOG-F2) to generate the combinatorial object array. Combinatorial testing tools are easy-to-use test case generators that allow to provision the input and constraints to the input parameter model and then generate the test configurations using the model.

Learn More About Pairwise Combinatorial Testing in These Related Titles

With society becoming increasingly dependent on these services for business, entertainment, government, and education, it’s vital that the applications running on top of the Future Internet meet strict standards for quality and reliability. Unlike other tools, Pairwiser provides a wide range of functionalities and features that one can explore in combinatorial testing. In this section, we will be discussing some easy-to-use, free, and popular combinatorial testing tools. To balance the cost and risks, we first define a systematic testing plan that outlines all possible tests and then choose a subset of tests that cover all the interactions of variables at some level of interaction (pairs, three-way, etc.). We developed a CTD algorithm that identifies a small test plan that covers 100 percent of a given interaction level. The end result is what we call IBM FOCUS CTD – the IBM Functional Coverage Unified Solution.

There are numerous tools available on the internet to perform combinatorial testing. In this article, we will discuss a few such tools that are available for free on the internet to generate test configurations. Sequence coverage measurement – Sequences of events are a significant factor in system failure. We have developed methods and tools to measure t-way event sequence coverage, to supplement measurement of input combination coverage. These methods can be used for all software, but are particularly important for evaluating correct operations for autonomous systems.

Organizations

CTD can help businesses carry out testing in all kinds of industries, whether banking, health insurance, retail — essentially, any business that involves a web service. CTD is especially effective in domains where the testing definition of combinatorial testing efforts require huge amounts of resources and are expected to cover large areas of complex business processes. To obtain the tools, please send a request to Rick Kuhn – Please provide first and last name, and organization.

In our work with a large European insurance company to test their Internet applications, it would have taken months to do the thousands of tests defined in their testing plan. Using our CTD methods and tool, we managed to reduce the plan to 30 tests – which were carried out in a matter of hours. 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.

The solution was to find a method that maximizes the value of each tested scenario so we could reduce the number of tests, while controlling the risk of leaving out important scenarios. This simple but powerful tool help not only to generate tests using pairwise technique but also has capabilities to add required tests, negative values and complex constraints. It is JAVA-based and completely free tool with GUI which makes it even easier to use for anybody.

This is a case where CTD can help make sure all possible scenarios are covered. Full text search our database of 174,500 titles for n-way Combinatorial Testing to find related research papers. Full text search our database of 174,500 titles for Pairwise Combinatorial Testing to find related research papers.

It can also read existing tests, analyze their functional coverage, select a subset of the tests that maintains the same coverage, and generate new tests to close the coverage gaps. A few years ago, our team decided it was time to tackle the challenges that plague the testing process. There were too many combinations to deal with and it was taking too long to test all of them. We wanted to control the risks we took by leaving out certain tests, yet be sure we covered all the important scenarios.

This problem is recognized and teams are seeking solutions in aviation and other fields. As one notes, “How do we determine that the data gathered to train an AI system is suitably representative of the real world?” This key question is currently being https://globalcloudteam.com/ studied in large-scale efforts planning for certification of these systems . Where a human can test a small set of curated theories on a sparse set of data, a machine can test a huge number of combinatorial possibilities on massive datasets.

See articles onhigh assurance software testingorsecurity and reliability. Combinatorial testing is a method that can reduce cost and improve test effectiveness significantly for many applications. The key insight underlying this form of testing is that not every parameter contributes to every failure, and empirical data suggest that nearly all software failures are caused by interactions between relatively few parameters. This finding has important implications for testing because it suggests that testing combinations of parameters can provide highly effective fault detection. Commonly known as all-pair testing, pairwise testing is a combinatorial method of software testing that for each pair of input parameter to a system, tests all possible discrete combinations of those parameters.

It is a test design technique that delivers hundred percent test coverage. The most common bugs in a program are usually found and triggered either by an input parameter or by an interaction between pair of parameters. Bugs involving interactions between three or more parameters are both progressively less common as well as progressively more expensive to find, such testing has as its limit the testing of all possible inputs.

IBM Research

Read sections 2, 3, and 4 (pp. 4 to 18) of Practical Combinatorial Testing. Two example testing use cases are included, illustrating how to apply the combinatorial approach. We have looked at combinatorial test testing in the form of decision table based testing and cause effect graphing. The feasibility of this combinatorial approach was tested in separate visual and motor studies in healthy individuals. Combinatorial test design enables users to get greater test coverage with fewer tests. New testing techniques have revealed a combinatorial quagmire of infections in individual patients, even in routine practice.

Combinatorial testing is a testing technique in which multiple combinations of the input parameters are used to perform testing of the software product. The aim is to ensure that the product is bug-free and can handle different combinations or cases of the input configuration. One of the most commonly used combinatorial testing methods is the pairwise testing technique which involves testing all pairs of input variable values. Since combinatorial testing follows a complex procedure and it can be a tedious task to manually perform this testing on many input parameters, we, therefore, use combinatorial testing tools. Not only are these tools easy to use with many input parameters, but they can also add constraints in the input parameters and generate test configurations accordingly.

Sample sentences with “combinatorial test”

Nowadays, software systems are diverse as well as complex and have many possible configurations. These qualities and features in the software systems has inaugurated a demand of software and applications that are uniquely designed and have innovative as well as creative features. Apart from these, the clients and users also demand exponential performance, functionality, quality, scalability, effectiveness, and more.

With each topic, a section on costs and practical considerations explains tradeoffs and limitations that may impact resources or funding. The material is accessible to an undergraduate student of computer science or engineering, and includes an extensive set of references to papers that provide more depth on each topic. Combinatorial or t-way testingis a proven method for more effective testing at lower cost.

Combinatorial Testing tools in Software Testing

Combinatorial methods make possible an approach to producing explanations or justifications of decisions in AI/ML systems. This approach is particularly useful in classification problems, where the goal is to determine an object’s membership in a set based on its characteristics. These problems are fundamental in AI because classification decisions are used for determining higher-level goals or actions.Explainability is a necessary but not sufficient condition for assurance in these systems. The Future Internet will be a complex interconnection of services, applications, content, and media, offering a rich user experience beyond current hyperlink-based navigation.

Explainability, Verification, and Validation for Assured Autonomy and AI

This tool is the simplest to use because we just have to write the test factors and constraints and the test configurations are generated. This tool allows us to write the constraints using an If-Then format as shown below. The everyday work of the software development specialists coupled with specialized vocabulary usage.

The user guide contains illustrations and screen shots showing how to use the tool for practical testing. Now let us look at Combinatorial Testing, which is another black-box testing technique. Combinatorial methodologies allow fast preparation and high-speed testing of solid materials aimed to be used for heterogeneous catalysis. ComXAI – a prototype tool implementing these methods has been developed. Explainability – If we cannot explain or justify decisions of an AI application, then it is difficult to trust the system.

This combinatorial process is fundamentally different to the ones that physicists generally consider, and which are governed by the laws of physics. CTD helps testers systematically model the gamut of possible scenarios to be tested, and then works to optimize the number of tests so they cover these situations. The tool comes with a visualizer that shows the coverage of each test case that is added to the test suite. Developing different applications specific to a company has become the norm for every successful business in today’s world.

Moreover, it can also cause bodily harm, which can result in large economic losses or security breaches, as well as affect the quality of day-to-day life. However, software testers often have limited time and budget, which frequently makes it impossible to exhaustively test software. Additionally, testers often intuitively test for defects that they anticipate, while less foreseen defects are overlooked and not tested. The best way to overcome such a situation is to implement Combinatorial Testing in software and applications. Combinatorial Testing is an approach that can systematically examine system setting in a manageable number of tests and by systematically covering t-way interactions. Therefore, here is an expounded discussion on Combinatorial Testing, to define its significance, methods and other major qualities.

Combinatorial testing can help detect problems like this early in the testing life cycle. The key insight underlying t-way combinatorial testing is that not every parameter contributes to every fault and most faults are caused by interactions between a relatively small number of parameters. This publication provides a self-contained tutorial on using combinatorial testing for real-world software, including how to use it effectively for system and software assurance.

A new supercomputing-powered weather model may ready us for Exascale

The new materials have been tested in combinatorial solar cell libraries to investigate their properties under real-world operating conditions. DLD is being employed with a combinatorial synthesis and testing protocol. The combinatorial principle might also extend to situations beyond cell growth and development. These systems use quantum mechanical properties to create combinatorial computing capabilities that exceed what is possible in classical systems.