Test input generation from cause–effect graphs

Ufuktepe, Deniz Kavzak; Ayav, Tolga; Belli, Fevzi

doi:10.1007/s11219-021-09560-3

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…Woo et al [19] present an approach for automatically generating cause-effect relationships from informal requirement specifications in Korean, using a mechanism that simplifies the requirements through the following procedures: (1) the automatic simplification of the informal requirement sentences, (2) the generation of a Condition/Conjunction/Clause Tree (C3Tree) Model, and (3) cause-effect relationship generation. A novel approach called Spectral Testing is proposed by Deniz et al [6] for generating test inputs for Boolean specification models. This method utilizes the spectral analysis of Boolean expressions and applies mutations to the original expressions to generate test inputs.…”

Section: Related Work 21 Cause-effect Graphmentioning

confidence: 99%

Combinatorial Test Case Generation Based on ROBDD and Improved Particle Swarm Optimization Algorithm

Li,

Song,

Zhang

2024

Applied Sciences

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In applications of software testing, the cause–effect graph method is an approach often used to design test cases by analyzing various combinations of inputs with a graphical approach. However, not all inputs have equal impacts on the results, and approaches based on exhaustive testing are generally time-consuming and laborious. As a statute-based software-testing method, combinatorial testing aims to select a small but effective number of test cases from the large space of all possible combinations of the input values for the software to be tested, and to generate a set of test cases with a high degree of coverage and high error detection capability. In this paper, the reduced ordered binary decision diagram is utilized to simplify the cause–effect graph so as to reduce the numbers of both the inputs and test cases, thereby saving the testing cost. In addition, an improved particle swarm optimization algorithm is proposed to significantly reduce the computation time needed to generate test cases. Experiments on several systems show that the proposed method can generate excellent results for test case generation.

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Section: Related Work 21 Cause-effect Graphmentioning

confidence: 99%

Combinatorial Test Case Generation Based on ROBDD and Improved Particle Swarm Optimization Algorithm

Li,

Song,

Zhang

2024

Applied Sciences

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“…An important attempt based on Boolean expressions employed the particle swarm optimization (PSO) algorithm for optimizing the test case selection 18 . The most recent approach for converting cause‐effect graphs to Boolean expressions and performing mutant analysis was proposed by Ufuktepe et al 32 . As part of their research, they developed a software tool 33 for the application of the proposed algorithm in the process of test suite generation.…”

Section: Related Workmentioning

confidence: 99%

“…The most recent approach for converting cause‐effect graphs to Boolean expressions and performing mutant analysis was proposed by Ufuktepe et al 32 . As part of their research, they developed a software tool 33 for the application of the proposed algorithm in the process of test suite generation.…”

Section: Related Workmentioning

confidence: 99%

Usage of cause‐effect graphs for reliability analysis of an embedded IoT alarm software system

Cogo,

Karabegović

et al. 2024

Quality & Reliability Eng

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Software is a part of most complex systems used for different purposes in today's world. The reliability of these systems must be verified before putting them to use, because their malfunction may lead to catastrophic failure, the loss of human lives, or huge costs. Software reliability can be verified by performing black‐box testing, without knowledge of the complex internal structure of these systems. In this work, an embedded IoT alarm system for detection of intruders and potential robbery is proposed. It is composed of three subsystems: embedded IoT, web‐server, and mobile phone subsystems. Cause‐effect graphing technique was applied for generating three types of black‐box test suites, by using the forward‐propagation and two different minimization algorithms. These test suites were used for black‐box testing the system, which was performed after injecting a maximum of 12 faults into the system in 100 iterations. The resulting software quality and reliability metrics showed that the forward‐propagation algorithm always achieves maximum fault detection rate (FDR), and this algorithm should be used when the highest demand for reliability must be met. The optimized minimization should only be used in cases where testing is very expensive, because it did not find faults in all testing iterations. The basic optimization retained high FDR, while reducing the impact of the minimized test suite and achieved a significant increase of test effect coverage values.

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“…random selection of inputs) [25]. • Cause-Effect Graph is a testing method with a heuristic approach [26]. It is the only black-box testing technique that considers combinations of causes for system behaviour [27].…”

Section: ) Black-box Testingmentioning

confidence: 99%

Development of Automatic Source Code Evaluation Tests Using Grey-Box Methods: A Programming Education Case Study

Skalka,

Drlík

2023

IEEE Access

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Increasing the effectiveness of programming education has emerged as an important goal in teaching programming languages in the last decade. Automatic evaluation of the correctness of the student's source code saves teachers time and effort and allows a more comprehensive focus on the preparation of assignments with integrated feedback. The study aims to present an approach that will enable effective testing of students' source codes within object-oriented programming courses while minimising the demands on teachers when preparing the assignment. This approach also supports variability in testing and preventing student cheating. Based on the principles of different types of testing (black-box, white-box, grey-box), an integrated solution for source code verification was designed and verified. The basic idea is to use a reference class, which is assumed to be part of every assignment, as the correct solution. This reference class is compared to the student solution using the grey-box method. Due to their identical interface (created by assignment), comparing instance states and method outputs is a matter of basic programming language mechanisms. A significant advantage is that a random generation of test cases can be used in such a case, while the rules for their generation can be determined using simple formulas. The proposed procedure was implemented and gradually improved during 4 years on groups of bachelor students of applied informatics with a high level of acceptance.

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Test input generation from cause–effect graphs

Cited by 11 publications

References 38 publications

Combinatorial Test Case Generation Based on ROBDD and Improved Particle Swarm Optimization Algorithm

Combinatorial Test Case Generation Based on ROBDD and Improved Particle Swarm Optimization Algorithm

Usage of cause‐effect graphs for reliability analysis of an embedded IoT alarm software system

Development of Automatic Source Code Evaluation Tests Using Grey-Box Methods: A Programming Education Case Study

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