| Reference : Identifying Optimal Trade-Offs between CPU Time Usage and Temporal Constraints Using ... |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/16336 | |||
| Identifying Optimal Trade-Offs between CPU Time Usage and Temporal Constraints Using Search | |
| English | |
Nejati, Shiva  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)] | |
| Jul-2014 | |
| International Symposium on Software Testing and Analysis (ISSTA 2014) | |
| 11 | |
| Yes | |
| International Symposium on Software Testing and Analysis (ISSTA 2014) | |
| July 21-25, 2014 | |
| [en] Integration of software from different sources is a critical activity in
many embedded systems across most industry sectors. Software integrators are responsible for producing reliable systems that fulfill various functional and performance requirements. In many situations, these requirements inversely impact one another. In particular, embedded system integrators often need to make compromises regarding some of the functional system properties to optimize the use of various resources, such as CPU time. In this paper, motivated by challenges faced by industry, we introduce a multi-objective decision support approach to help balance the minimization of CPU time usage and the satisfaction of temporal constraints in automotive systems. We develop a multi-objective, search-based optimization algorithm, specifically designed to work for large search spaces, to identify optimal trade-off solutions fulfilling these two objectives. We evaluated our algorithm by applying it to a large automotive system. Our results show that our algorithm can find solutions that are very close to the estimated ideal optimal values, and further, it finds significantly better solutions than a random strategy while being faster. Finally, our approach efficiently identifies a large number of diverse solutions, helping domain experts and other stakeholders negotiate the solutions to reach an agreement. | |
| Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab) | |
| http://hdl.handle.net/10993/16336 |
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