Combining Satisfiability Solving and Heuristics to Constrained Combinatorial Interaction Testing SpringerLink

Using this approach, CIT is used as a black-box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper introduces a different approach to use the CIT as a gray-box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. The results showed that this approach would help to detect new faults as compared to the equal impact parameter approach.

This paper presents a new patching workflow that allows developers to validate prospective patches and users to select which updates they would like to apply, along with two new technologies that make it possible. We use change analysis in combination with binary rewriting to transform the old executable and buggy execution into a test case including the developer’s https://globalcloudteam.com/ prospective changes that let us generate and run targeted tests for the candidate patch. We also provide analogous support to users, to selectively validate and patch their production environments with only the desired bug-fixes from new version releases. Ecological threats refer to the degree to which the results may be generalized between different configurations.

A penalty-based Tabu search for constrained covering arrays

As IPOG-C is based on IPOG, it accomplishes exhaustive comparisons in the horizontal growth which may lead to a longer execution. Alternative Hypothesis, H 1.1 – There is difference regarding cost-efficiency between TTR 1.1 and TTR 1.2. If this is not done, the final goal will never be matched, since there are no uncovered t-tuples that correspond to this interaction. Is the number of parameters, each v i is the number of values for each parameter p i , and t is the strength. National Institute of Standards and Technology has released the latest version of its free testing tool, complete with a new tutorial on how to use it.

If you add some constraints, saying some feature is not supposed to work with some config, you can even lean it further. Main calls parse_config to get the configurations and, for each configuration, calls important_function with the arguments obtained from the configuration. In this case we are merely checking if important_function throws an exception when it is called, but you might want to perform any relevant check (e.g. assert that an invariant holds). Here we specify the parameter name, its type , and then we list all of its possible values. Gülsüm Uzer received her BS and MS degrees in computer science and engineering from Bilkent University, Ankara, Turkey, in 2017, and from Sabanci University, Istanbul, Turkey, in 2019, respectively.

3 Description of the experiment

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. 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.

Moreover, the algorithm performs exhaustive comparisons within each horizontal extension which may cause longer execution. On the other hand, TTR 1.2 only needs one auxiliary matrix to work and it does not generate, at the beginning, the matrix of t-tuples. These features make our solution better for higher strengths even though we did not find statistical difference when we compared TTR 1.2 with our own implementation of IPOG-F (Section 6.4). We performed two controlled experiments addressing cost-efficiency and only cost. Considering both experiments, we performed 3,200 executions related to 8 solutions. In the first controlled experiment, our goal was to compare versions 1.1 and 1.2 of TTR in order to check whether there is significant difference between both versions of our algorithm.

Performance testing

In the context of CIT, meta-heuristics such as simulated annealing (Garvin et al. 2011), genetic algorithms (Shiba et al. 2004), and Tabu Search Approach (Hernandez et al. 2010) have been used. Recent empirical studies show that meta-heurisitic and greedy algorithms have similar performance (Petke et al. 2015). Hence, early fault detection via a greedy algorithm with constraint handling (implemented in the ACTS tool (Yu et al. 2013)) was no worse than a simulated annealing algorithm (implemented in the CASA tool (Garvin et al. 2011)). Moreover, there was not enough difference between test suites generated by ACTS and CASA in terms of efficiency and t-way coverage. All such previous remarks, some of them based on strong empirical evidences, emphasize that greedy algorithms are still very competitive for CIT.

• It can read the test data inputs, analyze the test data coverage, select a subset of the tests, and generate a new test plan ensuring full range.
• However, for low strengths, other greedy approaches, like IPOG-F, may be better alternatives.
• 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.
• We evaluated and comparecoverage achieves by CIT and RIPPER classification with hierarchical clustering.
• This insertion is done in the same way as the initial solution for M is constructed, as described in the section above.
• This way of reduction is very effective for the detection of defect as there is no necessity to test all the combinations that are possible (Nie & Leung, 2011).
• Although useful, practically, not all the parameters having the same impact on the SUT.

To this end, we have, indeed, developed a number of different strategies , namely fixed coverage, randomized coverage, opportunistic coverage, guaranteed coverage, and optimized coverage. A daily build process is a process where the latest version of a software system under development is downloaded, configured, built, and tested on a daily basis (typically during off-work hours). The ultimate goal of this process is to reveal https://globalcloudteam.com/glossary/combinatorial-testing/ the defects regarding the fundamental functionalities of the system as early as possible, so that the turn-around time for fixes is reduced as much as possible . Consequently, the daily build processes have been extensively used for software testing (Memon et al., 2003, Fowler and Foemmel, 2006). Regarding the variables involved in this experiment, we can highlight the independent and dependent variables (Wohlin et al. 2012).

Paper statistics

When fixes are distributed by including extra context users can incorporate only updates that guarantee compatibility between buggy and fixed versions. Can be applied to deal with different types of goals and is able to reduce the adaptation space and hence the time to make adaptation decisions with over 90%, with negligible effect on the realization of the adaptation goals. Some authors (Kuhn et al. 2013; Cohen et al. 2003) abbreviate a Mixed-Level Covering Array as CA too.

We randomly chose 80 test instances/samples with the strength, t, ranging from 2 to 6. Full data obtained in this experiment are presented in (Balera and Santiago Júnior 2017). A Fixed-value Covering Array denoted by CA is an N×p matrix of entries from the set such that every set of t-columns contains each possible t-tuple of entries at least a certain number of times (e.g. once). Free Combinatorial Testing ToolWednesday, 17 November 2010Testing just six parameter combinations is as good as exhaustive testing – NIST has a tool for that.

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IPOG-F (Forbes et al. 2008) is an adaptation of the IPOG algorithm (Lei et al. 2007). Through two main steps, horizontal and vertical growths, an MCA is built. The algorithm is supported by two auxiliary matrices which may decrease its performance by demanding more computer memory to use.

For this reason, it is important to create test conditions similar to those your users will experience. It doesn’t do much good to test your web application with a direct fiber connection if many of your users are going to be using your software over poor wifi connections. This idea also starts to show the concepts within software testing interact with each other and are interdependent. It is possible to see the following example as more usability testing than performance testing but there certainly is a point where the 2 interact. It matters less what you call it and more that you properly test what the users will experience.

Optimization Driven Constraints Handling in Combinatorial Interaction Testing

A sophisticated code generation mechanism allows for hooking into the generated transformation code at the imperative level to supply behavior that cannot be expressed declaratively. A thorough evaluation demonstrates conciseness, expressiveness, and scalability of our approach. 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. The conclusion validity has to do with how sure we are that the treatment we used in an experiment is really related to the actual observed outcome (Wohlin et al. 2012). One of the threats to the conclusion validity is the reliability of the measures (Campanha et al. 2010).