Code‐aware combinatorial interaction testing

Ahmed, Bestoun S.; Gargantini, Angelo; Zamli, Kamal Z.; Yılmaz, Cemal; Bureš, Miroslav; Szeles, Marek

doi:10.1049/iet-sen.2018.5315

Cited by 8 publications

(7 citation statements)

References 25 publications

(34 reference statements)

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“…Similar concepts proven to be perspective in the field of Combinatorial [165] and Constrained [166] Interaction Testing (CIT). Studies suggest good capability to generate CIT-based test cases from the actual state of code [167]- [169], and this concept is mostly applicable in automated generation of unit tests, where also alternatives have been suggested [170], [171]. Not only is CIT employed in automated generation of tests from source code, as another example, the mutation testing approach can also be used [172].…”

Section: A Future Challenges and Opportunitiesmentioning

confidence: 99%

On Code Analysis Opportunities and Challenges for Enterprise Systems and Microservices

et al. 2020

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Code analysis brings excellent benefits to software development, maintenance, and quality assurance. Various tools can uncover code defects or even software bugs in a range of seconds. For many projects and developers, the code analysis tools became essential in their daily routines. However, how can code analysis help in an enterprise environment? Enterprise software solutions grow in scale and complexity. These solutions no longer involve only plain objects and basic language constructs but operate with various components and mechanisms simplifying the development of such systems. Enterprise software vendors have adopted various development and design standards; however, there is a gap between what constructs the enterprise frameworks use and what current code analysis tools recognize. This manuscript aims to challenge the mainstream research directions of code analysis and motivate for a transition towards code analysis of enterprise systems with interesting problems and opportunities. In particular, this manuscript addresses selected enterprise problems apparent for monolithic and distributed enterprise solutions. It also considers challenges related to the recent architectural push towards a microservice architecture. Along with open-source proof-of-concept prototypes to some of the challenges, this manuscript elaborates on code analysis directions and their categorization. Furthermore, it suggests one possible perspective of the problem area using aspect-oriented programming.

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Section: A Future Challenges and Opportunitiesmentioning

confidence: 99%

On Code Analysis Opportunities and Challenges for Enterprise Systems and Microservices

et al. 2020

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“…(a)) for interaction consisting of the input parameters and constraints among them. The parameters are represented as the P[n] array, where each index P [1], P [2],... P[n] represents one of the test model parameters. Each parameter has a value.…”

Section: Sut Test Modelmentioning

confidence: 99%

“…The approach is also used with the test suite generation strategies to maximizing the code-base covered by generating more test cases [8]. We have recently used more advanced techniques function as gray-box methods for test case analysis and test generation [2]. We considered the code's internal structure while augmenting a generated test suite for Combinatorial Interaction Testing (CIT).…”

Section: Introductionmentioning

confidence: 99%

Code Coverage Aware Test Generation Using Constraint Solver

Sykora¹,

Ahmed²,

Bureš³

2020

Preprint

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Code coverage has been used in the software testing context mostly as a metric to assess a generated test suite's quality. Recently, code coverage analysis is used as a white-box testing technique for test optimization. Most of the research activities focus on using code coverage for test prioritization and selection within automated testing strategies. Less effort has been paid in the literature to use code coverage for test generation. This paper introduces a new Code Coverage-based Test Case Generation (CCTG) concept that changes the current practices by utilizing the code coverage analysis in the test generation process. CCTG uses the code coverage data to calculate the input parameters' impact for a constraint solver to automate the generation of effective test suites. We applied this approach to a few real-world case studies. The results showed that the new test generation approach could generate effective test cases and detect new faults.

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“…However, software testers will be driven to the limit upon exhaustive testing being carried out. When a large number of test cases need to be applied, software testing may be unable to execute the process [4,5]. In fact, there is a risk of an increasing number of test cases that helps boost time and cost.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, software failures are discovered, resulting in the interaction of several input parameters called t-way tests; t refers to the strength of the interaction [6,7]. CA can be formulated as CA (N, v P , t), where N * P defines the input matrix [0, 1,2 ..., up to (v -1)], so that each set of t-columns include at least once of each possible t-tuple, where v is a value, P the degree and t the strength [4,8]. Several strategies, as well as improved t-way combinatorial testing techniques, have also been developed to optimize any algorithm and to generate smaller test sequences or an almost optimal number of tests.…”

Section: Introductionmentioning

confidence: 99%

Review of Combinatorial Testing Strategy

Altmemi¹

2019

IJATCSE

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Combinatorial testing (CT) is an essential technique that usually aims for the generation of test cases. The generation of an optimally sized test case is a Non-deterministic Polynomial (NP) problem. The Combinatorial Interaction has been discussed by several researchers in developing different strategies depending on support interactions. Combinatorial testing taxonomy is thus Suggested to facilitate the analysis. In this paper, 12 algorithms were evaluated based on the strategy approach, supported interactions, randomness, and publication year. The year 2011 was noted as the most successful year in which researchers generated t-way algorithms. For them, the one-test-per-time OTAT strategy was the most promising area for Combinatorial t-way analysis. A small variation was seen in the type of interaction support. Nonetheless, uniform strength was the most common form of interaction from the year 2010 to the first half of the year 2018. This article was therefore implemented to review existing t-way strategies to identify their capabilities and limitations, thus providing a valuable guideline for generating the future.

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Code‐aware combinatorial interaction testing

Cited by 8 publications

References 25 publications

On Code Analysis Opportunities and Challenges for Enterprise Systems and Microservices

On Code Analysis Opportunities and Challenges for Enterprise Systems and Microservices

Code Coverage Aware Test Generation Using Constraint Solver

Review of Combinatorial Testing Strategy

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