Supporting Efficient and Advanced Omniscient Debugging for xDSMLs
Omniscient debugging is a promising technique that relies on execution traces to enable free traversal of the states reached by a system during an execution. While some General-Purpose Languages (GPLs) already have support for omniscient debugging, developing such a complex tool for any executable Domain-Specific Modeling Language (xDSML) remains a challenging and error prone task. A solution to this problem is to define a generic omniscient debugger for all xDSMLs. However, generically supporting any xDSML both compromises the efficiency and the usability of such an approach. Our contribution relies on a partly generic omniscient debugger supported by generated domain-specific trace management facilities. Being domain-specific, these facilities are tuned to the considered xDSML for better efficiency. Usability is strengthened by providing multidimensional omniscient debugging. Results show that our approach is on average 3.0 times more efficient in memory and 5.03 more efficient in time when compared to a generic solution that copies the model at each step.
Slides (2015-10-SLE-OmniscientDebugging.pdf) | 4.87MiB |
Tue 27 Oct Times are displayed in time zone: Eastern Time (US & Canada) change
13:30 - 15:00: Model Execution and VerificationSLE at Grand Station 2 Chair(s): Davide Di RuscioUniversity of L'Aquila | |||
13:30 - 14:00 Talk | Weaving Concurrency in eXecutable Domain-Specific Modeling Languages SLE Florent LatombeUniversity of Toulouse, France, Xavier CrégutUniversity of Toulouse, France, Benoit CombemaleINRIA, France, Julien DeAntoni, Marc PantelUniversity of Toulouse, France DOI Pre-print Media Attached File Attached | ||
14:00 - 14:30 Talk | Supporting Efficient and Advanced Omniscient Debugging for xDSMLs SLE Erwan BousseIRISA, France, Jonathan CorleyUniversity of Alabama, USA, Benoit CombemaleINRIA, France, Jeff GrayUniversity of Alabama, USA, Benoit BaudryINRIA, France Link to publication DOI File Attached | ||
14:30 - 15:00 Talk | Using Decision Rules for Solving Conflicts in Extended Feature Models SLE Lina OchoaUniversity of Los Andes, Colombia, Oscar González-RojasUniversity of Los Andes, Colombia, Thomas ThümTU Braunschweig, Germany DOI |