相关论文: Cost-effective Simulation-based Test Selection in …
Abstract Simulation platforms facilitate the development of emerging cyber-physical systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational tests. Despite this, thoroughly testing…
Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing…
Testing with simulation environments helps to identify critical failing scenarios for self-driving cars (SDCs). Simulation-based tests are safer than in-field operational tests and allow detecting software defects before deployment.…
Software metrics such as coverage and mutation scores have been extensively explored for the automated quality assessment of test suites. While traditional tools rely on such quantifiable software metrics, the field of self-driving cars…
Software systems for safety-critical systems like self-driving cars (SDCs) need to be tested rigorously. Especially electronic control units (ECUs) of SDCs should be tested with realistic input data. In this context, a communication…
Developing tools in the context of autonomous systems [22, 24 ], such as self-driving cars (SDCs), is time-consuming and costly since researchers and practitioners rely on expensive computing hardware and simulation software. We propose…
This is the first edition of the tool competition on testing self-driving cars (SDCs) at the International Conference on Software Testing, Verification and Validation (ICST). The aim is to provide a platform for software testers to submit…
Simulators are widely used to test Autonomous Driving Systems (ADS), but their potential flakiness can lead to inconsistent test results. We investigate test flakiness in simulation-based testing of ADS by addressing two key questions: (1)…
Future extreme-scale computer systems may expose silent data corruption (SDC) to applications, in order to save energy or increase performance. However, resilience research struggles to come up with useful abstract programming models for…
Scenario-based testing with driving simulators is extensively used to identify failing conditions of automated driving assistance systems (ADAS). However, existing studies have shown that repeated test execution in the same as well as in…
Discrete choice models (DCMs) have been widely utilized in various scientific fields, especially economics, for many years. These models consider a stochastic environment influencing each decision maker's choices. Extensive research has…
Self-driving cars require extensive testing, which can be costly in terms of time. To optimize this process, simple and straightforward tests should be excluded, focusing on challenging tests instead. This study addresses the test selection…
Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field testing. Most testing techniques consider virtualized SDCs within a simulation environment, whereas less effort has been directed towards assessing…
The rise of self-driving cars (SDCs) presents important safety challenges to address in dynamic environments. While field testing is essential, current methods lack diversity in assessing critical SDC scenarios. Prior research introduced…
Reliable collision avoidance under extreme situations remains a critical challenge for autonomous vehicles. While large language models (LLMs) offer promising reasoning capabilities, their application in safety-critical evasive maneuvers is…
There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario. However, characterising such behaviour usually requires…
Nonlinear Model Predictive Control (NMPC) is widely used for controlling high-speed robotic systems such as quadrotors. However, its significant computational demands often hinder real-time feasibility and reliability, particularly in…
Application developers often place executable assertions -- equipped with program-specific predicates -- in their system, targeting programming errors. However, these detectors can detect data errors resulting from transient hardware faults…
In a critical software system, the testers have to spend an enormous amount of time and effort to maintain the software due to the continuous occurrence of defects. Among such defects, some severe defects may adversely affect the software.…
Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testings are being conducted before their future mass deployment. Testing the system on the road is…