Topic
System under test
About: System under test is a research topic. Over the lifetime, 2239 publications have been published within this topic receiving 29548 citations. The topic is also known as: SUT.
Papers published on a yearly basis
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Papers
TL;DR: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal as discussed by the authors, such as rate of fault detection, a measure of how quickly faults are detected within the testing process.
Abstract: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection, a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing, the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. We describe several techniques for using test execution information to prioritize test cases for regression testing, including: 1) techniques that order test cases based on their total coverage of code components; 2) techniques that order test cases based on their coverage of code components not previously covered; and 3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites.
1,353 citations
1 Aug 2000
TL;DR: Can prioritization techniques be effective when aimed at specific modified versions; what tradeoffs exist between fine granularity and coarse granularity prioritized techniques; and can the incorporation of measures of fault proneness into prioritization technique improve their effectiveness?
Abstract: Test case prioritization techniques schedule test cases in an order that increases their effectiveness in meeting some performance goal. One performance goal, rate of fault detection, is a measure of how quickly faults are detected within the testing process; an improved rate of fault detection can provide faster feedback on the system under test, and let software engineers begin locating and correcting faults earlier than might otherwise be possible. In previous work, we reported the results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: (1) can prioritization techniques be effective when aimed at specific modified versions; (2) what tradeoffs exist between fine granularity and coarse granularity prioritization techniques; (3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? This paper reports the results of new experiments addressing these questions.
1,214 citations
27 May 2018
TL;DR: This work proposes an automated testing algorithm that builds on learnable evolutionary algorithms that outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm.
Abstract: Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures.
302 citations
1 May 1985
TL;DR: The problem of identifying system parameters deals with the evaluation of coefficients in the known mathematical model from measured input-output data and can be reduced to a parameter estimation that uses the tools of estimation theory.
Abstract: The problem of identifying system parameters deals with the evaluation of coefficients in the known mathematical model from measured input-output data. Moreover, the identification problem can be reduced to a parameter estimation that uses the tools of estimation theory. According to [2], “identification is the determination, on the basis of input and output, of a system within a specified class of systems, to which the system under test is equivalent.”
296 citations
TL;DR: For more than a decade, the Swedish Defence Authorities have, in cooperation with Swedish industry and other countries, studied the effects of high-power microwave radiation on electronic systems as mentioned in this paper.
Abstract: For more than a decade, the Swedish Defence Authorities have, in cooperation with Swedish industry and other countries, studied the effects of high-power microwave (RPM) radiation on electronic systems. Testing at high-field levels has been carried out on military equipment as well as on civil equipment, such as cars, computers, and security systems. From these studies, it is concluded that the distance for RPM sabotage can reach about a kilometer. Experience from system testing has, besides giving information about system susceptibility, also demonstrated the need for a careful pre-analysis of the system under test. This is due to the fact that high-level testing, in most cases, includes only a small fraction of the threat parameter space, such as test frequencies and irradiation angles.
287 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 1 |
| 2024 | 2 |
| 2023 | 8 |
| 2022 | 28 |
| 2021 | 51 |
| 2020 | 92 |