Research on model verification from execution traces to analyze multicore and real-time systems, detect timing issues, and reduce debugging work.
Beamonte, Raphaël, Ezzati-Jivan, Naser, and Dagenais, Michel R. (2022). Execution trace-based model verification to analyze multi-core and real-time systems. Concurrency and Computation: Practice and Experience, 34(17):e6974. doi:10.1002/cpe.6974
@article{beamonte:2022a,
author = {Beamonte, Raphaël and Ezzati-Jivan, Naser and Dagenais, Michel R.},
title = {{Execution trace-based model verification to analyze multi-core and real-time systems}},
journal = {{Concurrency and Computation: Practice and Experience}},
publisher = {Wiley},
year = 2022,
month = may,
day = 4,
volume = 34,
number = 17,
pages = {e6974},
doi = {10.1002/cpe.6974}
}Abstract
As a key part of model-driven development, modeling allows users to represent the application workflow or to automatically generate source code. This is convenient for developers, particularly to create or improve real-time applications embedded in complex systems.
Multicore systems are difficult to debug because the concurrently running processes can interfere with each other. In real-time systems, timing constraints add to the complexity, invalidating results when a deadline is missed. Tracing is usually the most accurate and reliable tool to study the runtime behaviour of those applications.
However, the interpretation of voluminous detailed execution traces requires a deep understanding of the operating system and application behaviour, and time to dig through the millions of trace events.In this paper, we present the use of model-based constraints on top of user-space and kernel traces to provide weighted analysis results.
Our algorithms have been applied to multiple traces showing common problems for multi-core real-time systems. The experimental results show that our algorithms can quickly identify many different types of problems with a low runtime, even for traces with millions of events, thus helping to save time when analyzing thousands of trace events for complex systems.