Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
A safe, efficient regression test selection technique
ACM Transactions on Software Engineering and Methodology (TOSEM)
Tracking down software bugs using automatic anomaly detection
Proceedings of the 24th International Conference on Software Engineering
Accuracy of Profile Maintenance in Optimizing Compilers
INTERACT '02 Proceedings of the Sixth Annual Workshop on Interaction between Compilers and Computer Architectures
Caroline: An autonomously driving vehicle for urban environments
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
Automating Software Testing Using Program Analysis
IEEE Software
A systematic review on regression test selection techniques
Information and Software Technology
Finding concurrency bugs with context-aware communication graphs
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
Design of intelligent agents for collaborative testing of service-based systems
Proceedings of the 6th International Workshop on Automation of Software Test
From autonomous vehicles to safer cars: selected challenges for the software engineering
SAFECOMP'12 Proceedings of the 2012 international conference on Computer Safety, Reliability, and Security
Model-based, composable simulation for the development of autonomous miniature vehicles
Proceedings of the Symposium on Theory of Modeling & Simulation - DEVS Integrative M&S Symposium
Proceedings of International Workshop on Engineering Simulations for Cyber-Physical Systems
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Cyber-physical systems like active safety systems in recent vehicles are significantly driven by software and rely predominantly on data that is perceived by cameras, laser scanners, and the like from the system's environment. For example, these sensor-based systems realize pedestrian protection function-alities, which cannot be tested under simplified conditions on proving grounds only or by arbitrary test-runs on public roads anymore. Instead, simulative environments are used nowadays, which provide the virtual surroundings for such a system where its real input sources are replaced with simplified sensor models. Thus, interactive and hazard-free system tests and automated system evaluations can be carried out easily. However, the simple strategy to run all available modeled traffic scenarios in the simulation on any change of the implementation would consume too much computation time to provide effective and fast feedback for developers. In this article, an improved strategy for selecting scenarios that shall be run in a simulation based on run-time control-flow analysis is proposed, which resulted from the in-depth analysis of the revision history of the source code and their accompanying simulations for two self-driving vehicles. The outlined strategy is evaluated on a self-driving miniature vehicle.