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 Java) in order to check whether there is significant difference between both versions of our algorithm. We conclude that TTR 1.2 is more adequate than TTR 1.1 especially for higher strengths (5 and 6).
However, there is no assessment about the cost of the algorithm to generate MCAs. The three versions (1.0 (Balera and Santiago Júnior 2015), 1.1, and 1.2) of TTR were implemented in Java. To obtain the tools, please send a request to Rick Kuhn – including your name and the name of your organization. No other information is required, but we like to have a list of organizations so that we can show our management where the software is being used.
The TConfig tool can generate test cases based on strengths varying from 2 to 6. However, it is not entirely clear whether the IPOG algorithm (Lei et al. 2007) was implemented in the tool or if another approach was chosen for t-way testing. In our empirical evaluation, TTR 1.2 was superior to IPO-TConfig not only for higher strengths (5, 6) but also for all strengths (from 2 to 6). Moreover, IPO-TConfig was unable to generate test cases in 25% of the instances (strengths 4, 5, 6) we selected. In this section, we present a second controlled experiment where we compare TTR 1.2 with five other significant greedy approaches for unconstrained CIT test case generation. Many characteristics of this second controlled experiment ressemble the first one (Section 4).
In the last version, 1.2, the algorithm no longer generates the matrix of t-tuples (Θ) but rather it works on a t-tuple by t-tuple creation and reallocation into M. The academic community has been making efforts to reduce the cost of the software testing process by decreasing the size of test suites while at the same time aiming at maintaining the effectiveness (ability to detect defects) of such sets of test cases. CIT relates to combinatorial analysis whose objective is to answer whether it is possible to organize elements of a finite set into subsets so that certain balance or symmetry properties are satisfied (Stinson 2004).
Recent Advances in Automatic Black-Box Testing
We also present simple heuristics for isolating the fault causing factors that can lead to such system failures. The test method and input model described in this paper have immediate application to other systems that provide complex full text search. We are investigating a new test development method that aims to maximize the confidence to be achieved by combining Assurance Cases with High Throughput Testing (HTT).
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 (runtime) 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. 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.
It measures the degree to which combinations of input values have been covered in tests, which is a static property of the test set. Measures such as statement or branch coverage are dynamic properties, as they measure the proportion of statements and branches covered when the program is running. An application of a method of test case generation for scientific computational software is presented. NEWTRNX, neutron transport software being developed at Oak Ridge National Laboratory, is treated as a case study.
Assurance Cases, developed for safety-critical systems, are a rigorous argument that the system satisfies a property (e.g., the Mars rover will not tip over during a traverse). They integrate testing, analysis, and environmental and operational assumptions, from which the set of conditions that testing must cover is determined. In our method, information from the Assurance Case is used to determine the test coverage needed, and then input to HTT to generate the minimal test suites needed to provide that coverage. This paper presented a novel CIT algorithm, called TTR, to generate test cases specifically via the MCA technique. TTR produces an MCA M, i.e. a test suite, by creating and reallocating t-tuples into this matrix M, considering a variable called goal (ζ).
- M. Mehta, R. Philip, Applications of Combinatorial Testing methods for Breakthrough Results in Software Testing, 2nd Intl.
- In (Pairwise 2017), 43 algorithms/tools are presented for CIT and many more not shown there exist.
- Moreover, the algorithm performs exhaustive comparisons within each horizontal extension which may cause longer execution.
- In t-way testing, a t-tuple is an interaction of parameter values of size equal to the strength.
JMB worked in the definitions and implementations of all three versions of the TTR algorithm, and carried out the two controlled experiments. VASJ worked in the definitions of the TTR algorithm, and in the planning, definitions, and executions of the two controlled experiments. Ecological threats refer to the degree to which the results may be generalized between different configurations.
Algorithms/tools were subjected to each one of the 80 test instances, one at a time, and the outcome was recorded. Cost is the number of generated test cases, and efficiency was obtained via instrumentation of the source code with the same computer previously mentioned. Regarding the metrics, cost refers to the size of the test suites while https://www.globalcloudteam.com/ efficiency refers to the time to generate the test suites. Although the size of the test suite is used as an indicator of cost, it does not necessarily mean that test execution cost is always less for smaller test suites. However, we assume that this relationship (higher size of test suite means higher execution cost) is generally valid.
Combinatorial testing is being applied successfully in nearly every industry, and is especially valuable for assurance of high-risk software with safety or security concerns. Combinatorial testing is referred to as effectively exhaustive, or pseudo-exhaustive, because it can be as effective as fully exhaustive testing, while reducing test set size by 20X to more than 100X. As before and by making a comparison between pairs of solutions (TTR 1.2 × other), in both assessments (cost-efficiency and cost), we can say that we have a high conclusion, internal, and construct validity. Regarding the external validity, we believe that we selected a significant population for our study.
Thinking about the testing process as a whole, one important metric is the time to execute the test suite which eventually may be even more relevant than other metrics. Hence, we need to run multi-objective controlled experiments where we execute all the test suites (TTR 1.1 × TTR 1.2; TTR 1.2 × other solutions) probably assigning different weights to the metrics. We also need to investigate the parallelization of our algorithm so that it can perform even better when subjected to a more complex set of parameters, values, strengths.
This tool allows us to write the constraints using an If-Then format as shown below. The evaluated results indicate that both techniques succeed in detecting security leaks in web applications with different results, depending on the background logic of the testing approach. Last but not least, we claim that attack pattern-based combinatorial testing with constraints can be an alternative method for web application security testing, especially when we compare our method to other test generation techniques like fuzz testing. The general description of both evaluations (cost-efficiency, cost) of this second study is basically the same as shown in Section 4.
Such approaches have drawn attention of the software testing community to generate sets of smaller (lower cost to run) and effective (greater ability to find faults in the software) test cases where they have been successful in detecting faults due to the interaction of several input parameters (factors). 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. 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. If branch coverage is not close to 100%, then (1) input parameter values can be changed to improve the test set, or (2) a higher strength (higher value of t) covering array can be used.
It further reports on undertaken practical experiments thus strengthening the applicability of combinatorial testing to web application security testing. Threats to population refer to how significant is the selected samples of the population. For our study, the ranges of strengths, parameters, and values are the determining points for this threat.