%0 Journal Article %1 Briand:2006:GPEM %A Briand, Lionel C. %A Labiche, Yvan %A Shousha, Marwa %D 2006 %J Genetic Programming and Evolvable Machines %K Schedulability Software algorithms, and genetic theory validation, verification %N 2 %P 145--170 %R doi:10.1007/s10710-006-9003-9 %T Using genetic algorithms for early schedulability analysis and stress testing in real-time systems %V 7 %X 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 analyse 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 maximise 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.

@article{Briand:2006:GPEM, abstract = {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 analyse 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 maximise 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Briand, Lionel C. and Labiche, Yvan and Shousha, Marwa}, biburl = {https://www.bibsonomy.org/bibtex/267c939f37b7a9f06f41d530acd20b5a5/brazovayeye}, doi = {doi:10.1007/s10710-006-9003-9}, interhash = {405e8cc744e1415298c557bb45842bee}, intrahash = {67c939f37b7a9f06f41d530acd20b5a5}, issn = {1389-2576}, journal = {Genetic Programming and Evolvable Machines}, keywords = {Schedulability Software algorithms, and genetic theory validation, verification}, month = {August}, note = {Special Issue: Best of GECCO 2005}, number = 2, pages = {145--170}, timestamp = {2008-06-19T17:37:03.000+0200}, title = {Using genetic algorithms for early schedulability analysis and stress testing in real-time systems}, volume = 7, year = 2006 }