Journal of Systems and Software
A UML/SPT Model Analysis Methodology for Concurrent Systems Based on Genetic Algorithms
MoDELS '08 Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems
A systematic review of search-based testing for non-functional system properties
Information and Software Technology
Improving testing of multi-unit computer players for unwanted behavior using coordination macros
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Search-based system testing: high coverage, no false alarms
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Modeling and analysis of CPU usage in safety-critical embedded systems to support stress testing
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
Optimizing threads schedule alignments to expose the interference bug pattern
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
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Reactive real-time systems have to react to external events within time constraints: Triggered tasks must execute within deadlines. It is therefore important for the designers of such systems to analyze the schedulability of tasks during the design process, as well as to test the system's response time to events in an effective manner once it is implemented. This article explores the use of genetic algorithms to provide automated support for both tasks. Our main objective is then to automate, based on the system task architecture, the derivation of test cases that maximize the chances of critical deadline misses within the system; we refer to this testing activity as stress testing. A second objective is to enable an early but realistic analysis of tasks' schedulability at design time. We have developed a specific solution based on genetic algorithms and implemented it in a tool. Case studies were run and results show that the tool (1) is effective at identifying test cases that will likely stress the system to such an extent that some tasks may miss deadlines, (2) can identify situations that were deemed to be schedulable based on standard schedulability analysis but that, nevertheless, exhibit deadline misses.