Can this fault be detected: A study on fault detection via automated test generation

Ping, Ma; Cheng, Hangyuan; Zhang, Jingxuan; Xuan, Jifeng

doi:10.1016/j.jss.2020.110769

Cited by 17 publications

(5 citation statements)

References 22 publications

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“…The adapter circuit is an important part of this automatic test system. The actual interface of the tested object is various, and most test instruments are 3 difficult to connect directly with the signal port of the tested object [8][9]. At this time, it is necessary to make the adapter circuit, through the interface circuit, adapt to the port of the tested object, so that the instrument can correctly connect the signal to the tested circuit and read the response signal of the circuit.…”

Section: Hardware Overall Connectionmentioning

confidence: 99%

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Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Mao,

Pang,

Jin

et al. 2024

J. Phys.: Conf. Ser.

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Traditional manual testing methods are no longer able to meet the maintenance needs of modern shipboard fire control system board-level circuits. Therefore, the development of intelligent and universal automatic test systems has become an important issue for shipboard fire control system fault detection. This article analyzes the performance and functional requirements of the automatic test system, develops the automatic test system based on PXI bus instruments, and designs an overall solution with signal time-frequency analysis capabilities that can carry out intelligent fault diagnosis for a variety of board-level circuits. On this basis, the design of hardware circuit and software solution is completed. The automatic test system was analyzed on hardware indicators and software functions. On the hardware side, the hardware resource selection was completed based on the hardware indicator requirements. On the software side, the overall software architecture design and the design of each functional module were completed based on the system software functional requirements. Finally, the composition and functions of each module of the system software and the overall operation process are introduced.

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Section: Hardware Overall Connectionmentioning

confidence: 99%

“…According to the functional requirements of system use, the software is divided into several corresponding functional modules. Different modules are interrelated and mutually contained to form the overall automatic test system software architecture [9].…”

Section: System Software Module Compositionmentioning

confidence: 99%

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Mao,

Pang,

Jin

et al. 2024

J. Phys.: Conf. Ser.

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“…evosuite.org (accessed on 20 February 2020)) [24] to generate test cases for Java code. EvoSuite is widely used in software testing [3,39,40]. It utilizes search-based methods, including genetic algorithms, to generate test cases using Java bytecode.…”

Section: Test Generation Toolmentioning

confidence: 99%

Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

et al. 2021

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Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

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“…The quality of a software product is affected by the quality of software testing [1]- [3]. Several of the techniques used is the approach of making the test case [4] and automatic assessment [5], [6]. One of them is mutation testing.…”

Section: Introductionmentioning

confidence: 99%

MT-Forward Algorithm for Prediction of Project Categories Based on Selection of Mutants

Arnomo¹,

Ibrahim²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Mutation testing is an effective technique for errors. Although effective, mutation testing has the main limitation that it is awfully expensive because it requires a series of tests on each mutant. Therefore, many researchers have focused on presenting various techniques to reduce the cost of mutation testing. In this study, the MT-Forward algorithm was developed. The concept of MT-Forward is mutation testing that connects the selection and prediction methods. As a result of the selection, the mutant operator can make a more efficient selection of the number of mutants obtained. The shrinkage of the selected mutants was from 1,019 live mutants to 749 live mutants as a priority. There are 15 features used in this study. The required features are obtained from the Locmetrix Test, Coverage Test, and Mutation Testing. The accuracy value of each method is almost the same, which distinguishes this study is the selection of important operators who prioritize improvement programs without reducing the level of accuracy. Where the PMS Method uses 43 Mutant Operators, PMT Method 22 Mutant Operators, while MT-Forward uses 7 mutant operators as priority mutant operators. The method used to evaluate the performance of the algorithm is 5-fold cross-validation. The accuracy of each method is PMS 57.14%, PMT Method 57.14%, and MT-Forward is 95.00%. MT-Forward states that combining mutant selection techniques and project category prediction is important for fixing faults.

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Can this fault be detected: A study on fault detection via automated test generation

Cited by 17 publications

References 22 publications

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Overall Scheme Design of Fault Diagnosis for Complex Electronic Systems

Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

MT-Forward Algorithm for Prediction of Project Categories Based on Selection of Mutants

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