There are some issues that frequently experienced by some software industries at the testing stage, that is the coverage test case. This is the extent to which the completeness of the test case in the tested unit prog...
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ISBN:
(纸本)9781728150338
There are some issues that frequently experienced by some software industries at the testing stage, that is the coverage test case. This is the extent to which the completeness of the test case in the tested unit program meets the codecoverage criteria. Testers usually design test cases just by looking at the source code for which it is very likely that the test case is incomplete or missing. There are currently many tools to check the coverage test cases. These tools only provide information on the percentage of line code that has been executed. On the other hand, the information from the testing results is presented in the form of XML, HTML or other formats that contain the line code status. It causes the information is still difficult to understand, especially for the beginner testers. This paper explains the results of a study of code coverage visualization using a control flow graph (CFG) to assist the tester as an effort to fulfill the complete test case. The experiment was conducted by involving 32 respondents who were the testers from various industries and a number of students.
There are some issues that frequently experienced by some software industries at the testing stage, that is the coverage test case. This is the extent to which the completeness of the test case in the tested unit prog...
详细信息
ISBN:
(数字)9781728150338
ISBN:
(纸本)9781728150345
There are some issues that frequently experienced by some software industries at the testing stage, that is the coverage test case. This is the extent to which the completeness of the test case in the tested unit program meets the codecoverage criteria. Testers usually design test cases just by looking at the source code for which it is very likely that the test case is incomplete or missing. There are currently many tools to check the coverage test cases. These tools only provide information on the percentage of line code that has been executed. On the other hand, the information from the testing results is presented in the form of XML, HTML or other formats that contain the line code status. It causes the information is still difficult to understand, especially for the beginner testers. This paper explains the results of a study of code coverage visualization using a control flow graph (CFG) to assist the tester as an effort to fulfill the complete test case. The experiment was conducted by involving 32 respondents who were the testers from various industries and a number of students.
As the deployment of autonomous vehicles (AV) grows, ensuring their reliability and safety becomes paramount. The need for rigorous testing and validation methods is surging. This paper focuses on investigating the re...
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ISBN:
(纸本)9798350311617;9798350311624
As the deployment of autonomous vehicles (AV) grows, ensuring their reliability and safety becomes paramount. The need for rigorous testing and validation methods is surging. This paper focuses on investigating the resilience of OpenPilot, an open-source driving agent for assisted driving systems, against faults and environmental conditions affecting sensor data, targeting faults that directly impact machine learning (ML) and perception systems, known to be a significant cause of disengagement incidents in AV. To assess the effectiveness and coverage of Open-Pilot's functionalities, we employ standard model-driven test engineering methodology graph coverage testing. A systematic and comprehensive modeling of OpenPilot using graph coverage methodology is proposed, covering three programming scripts and yielding 16 major test case scenarios. This approach enables graph coverage testing on OpenPilot, facilitating evaluation of its performance, robustness, and safety measures. By systematically exploring the system's functionalities and scenarios, we ensure OpenPilot performs as expected and effectively mitigates safety hazards, contributing to the enhancement of autonomous vehicle safety and building confidence in autonomous driving technology.
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