Model-level, platform-independent debugging in the context of the model-driven development of real-time systems

Bagherzadeh, Mojtaba; Hili, Nicolas; Dingel, Juergen

doi:10.1145/3106237.3106278

Cited by 28 publications

(25 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our approach is based on a platform-independent model instrumentation process. As in [5], our approach uses M2M transformation techniques for creating an instrumented version of the userdefined model supporting introspection, checking and adapting activities at runtime. This support is added by adding reflection and adaptation capability to the model in the (2) runtime package (runtime infrastructure).…”

Section: Nd Step: Automatic Generation Of the Resco Modelmentioning

confidence: 99%

Runtime observable and adaptable UML state machines

Illarramendi

Etxeberria

Elkorobarrutia

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

View full text Add to dashboard Cite

An embedded system is a self-contained system that incorporates elements of control logic and real-world interaction. UML State Machines constitute a powerful formalism to model the behaviour of these types of systems. In current industrial environments, the software of these embedded systems have to cope with the increasing complexity and robustness requirements at runtime. One way to manage these requirements is having the software component's behaviour model available at runtime (models@run.time). Thus, it is possible to enhance the safety of the software component by enabling verification and adaptation at runtime. In this paper, we present a model-driven approach to generate software components (namely, RESCO framework), which are able both to provide their internal information in model terms at runtime and adapt their behaviour automatically when an error or an unexpected situation is detected. The aforementioned runtime introspection and adaptation abilities are added automatically to the software component and it does not require the developer make any extra effort. The solution has been tested in the design and implementation of an industrial Burner controller. Results indicate that the software components generated by the presented solution provides introspection at runtime. Thanks to this introspection ability at runtime, the software components are able to adapt automatically from their normal-mode behaviour to a safe-mode behaviour which was defined to be used in erroneous or unexpected situations at runtime. Therefore, it is possible to enhance the safety of the systems consisting of these software components. CCS CONCEPTS• Computer systems organization → Dependable and faulttolerant systems and networks; Embedded software; Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

show abstract

Section: Nd Step: Automatic Generation Of the Resco Modelmentioning

confidence: 99%

Runtime observable and adaptable UML state machines

Illarramendi

Etxeberria

Elkorobarrutia

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

View full text Add to dashboard Cite

show abstract

“…Moreover, Model Driven Development tools that are known for their hierarchical organization and asynchronous transitions and communications have been extensively analyzed in terms of their semantics and syntax characteristics as an attempt to enhance their behavioral modeling capabilities (via enhancing state machine diagrams or statecharts), dimensions, and their final results quality [ 13], [ 14]. One drawback of UML-RT is that there is no significant support for models' debugging at model-level which is an important limitation for MDSD systems [ 15] This barrier leads model-driven software developers towards platformindependent solutions adding an extra dimension of complexity.…”

Section: Related Workmentioning

confidence: 99%

Model-Driven Software Development Platforms Reviews

Kedwan¹,

Sharma²

2019

IJCA

View full text Add to dashboard Cite

The Model-Driven Software Development Systems (MDSDS) were initially developed as an attempt to increase software development productivity and quality. This is because focusing on the logical solution abstract is more important than focusing on the pure infrastructure technicalities. Developers discovered the abstracted modelling technique that includes both programming and platform tools in the same time, which is now referred to as MDSDS.Nowadays, there are plenty of modeling software applications that almost achieve the same work, yet, the user might not be aware of the detailed nuances between them. This paper aims to discover the distinguishing features between four of the most commonly used MDSDS including; YAKINDU, Papyrus-RT, Rhapsody, and The State Machine Compiler (SMC). Analysis of the suitability of those platforms for modeling structural and behavioral domain specific software will be investigated.The same model will be built using the four MDSDSs. Then, main differences, obstacles, observations, and overall experience quality using those four environments will be discussed. Some of the common distinguishing features to be explored is GUI intuitivism, user friendliness, clarity of commands and tools, tool learning time needed and learning curve, model building time consumed, etc. General TermsModel-Driven Software Development Systems, YAKINDU, Papyrus-RT, Rhapsody, The State Machine Compiler-SMC, Model-Driven Engineering

show abstract

“…There is a growing body of research on techniques to enable model debugging, most particularly in the model-driven engineering community [9]. Some of these techniques focus on providing tools and features to help with manual debugging of software models (e.g.…”

Section: Model Debuggingmentioning

confidence: 99%

“…Some of these techniques focus on providing tools and features to help with manual debugging of software models (e.g. [9,56]). Automated approaches to fault localization and debugging at the level of software models can be broadly divided into the following two categories [9]: First, techniques that enable model debugging at the level of simulation models and rely on simulators or model interpreters.…”

Section: Model Debuggingmentioning

confidence: 99%

“…[9,56]). Automated approaches to fault localization and debugging at the level of software models can be broadly divided into the following two categories [9]: First, techniques that enable model debugging at the level of simulation models and rely on simulators or model interpreters. Such techniques complement design-time simulation by helping identify and localize faults in early and executable design models.…”

Section: Model Debuggingmentioning

confidence: 99%

See 1 more Smart Citation

Effective fault localization of automotive Simulink models: achieving the trade-off between test oracle effort and fault localization accuracy

et al. 2018

View full text Add to dashboard Cite

One promising way to improve the accuracy of fault localization based on statistical debugging is to increase diversity among test cases in the underlying test suite. In many practical situations, adding test cases is not a cost-free option because test oracles are developed manually or running test cases is expensive. Hence, we require to have test suites that are both diverse and small to improve debugging. In this paper, we focus on improving fault localization of Simulink models by generating test cases. We identify four test objectives that aim to increase test suite diversity. We use four objectives in a search-based algorithm to generate diversified but small test suites. To further minimize test suite sizes, we develop a prediction model to stop test generation when adding test cases is unlikely to improve fault localization. We evaluate our approach using three industrial subjects. Our results show (1) expanding test suites used for fault localization using any of our four test objectives, even when the expansion is small, can significantly improve the accuracy of fault localization, (2) varying test objectives used to generate the initial test suites for fault localization does not have a significant impact on the fault localization results obtained based on those test suites, and (3) we identify an optimal configuration for prediction models to help stop test generation when it is unlikely to be beneficial. We further show that our optimal prediction model is able to maintain almost the same fault localization accuracy while reducing the average number of newly generated test cases by more than half.

show abstract

Model-level, platform-independent debugging in the context of the model-driven development of real-time systems

Cited by 28 publications

References 24 publications

Runtime observable and adaptable UML state machines

Runtime observable and adaptable UML state machines

Model-Driven Software Development Platforms Reviews

Effective fault localization of automotive Simulink models: achieving the trade-off between test oracle effort and fault localization accuracy

Contact Info

Product

Resources

About