اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدType Annotation for Adaptive Systems
74
0
0.0
(
0
)
تأليف
Paolo Bottoni
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We introduce type annotations as a flexible typing mechanism for graph systems and discuss their advantages with respect to classical typing based on graph morphisms. In this approach the type system is incorporated with the graph and elements can adapt to changes in context by changing their type annotations. We discuss some case studies in which this mechanism is relevant.
قيم البحث
اقرأ أيضاً
Space and movement through space play an important role in many collective adaptive systems (CAS). CAS consist of multiple components interacting to achieve some goal in a system or environment that can change over time. When these components operate
in space, then their behaviour can be affected by where they are located in that space. Examples include the possibility of communication between two components located at different points, and rates of movement of a component that may be affected by location. The CARMA language and its associated software tools can be used to model such systems. In particular, a graphical editor for CARMA allows for the specification of spatial structure and generation of templates that can be used in a CARMA model with space. We demonstrate the use of this tool to experiment with a model of pedestrian movement over a network of paths.
Type inference for dynamic programming languages is an important yet challenging task. By leveraging the natural language information of existing human annotations, deep neural networks outperform other traditional techniques and become the state-of-
the-art (SOTA) in this task. However, they are facing some new challenges, such as fixed type set, type drift, type correctness, and composite type prediction. To mitigate the challenges, in this paper, we propose a hybrid type inference framework named HiTyper, which integrates static inference into deep learning (DL) models for more accurate type prediction. Specifically, HiTyper creates a new syntax graph for each program, called type graph, illustrating the type flow among all variables in the program. Based on the type graph, HiTyper statically infers the types of the variables with appropriate static constraints. HiTyper then adopts a SOTA DL model to predict the types of other variables that cannot be inferred statically, during which process a type correction algorithm is employed to validate and correct the types recommended by the DL model. Extensive experiments show that HiTyper outperforms the SOTA DL approach by 12.7% in terms of top-1 F1-score. Moreover, HiTyper filters out 50.6% of incorrect candidate types recommended by the SOTA DL model, indicating that HiTyper could improve the correctness of predicted types. Case studies also demonstrate the capability of HiTyper in alleviating the fixed type set issue, and in handling type drift and complicated types such as composite data types.
A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and it
s environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. [...]
Many Internet of Things and embedded projects are event-driven, and therefore require asynchronous and concurrent programming. Current proposals for C++20 suggest that coroutines will have native language support. It is timely to survey the current u
se of coroutines in embedded systems development. This paper investigates existing research which uses or describes coroutines on resource-constrained platforms. The existing research is analysed with regard to: software platform, hardware platform and capacity; use cases and intended benefits; and the application programming interface design used for coroutines. A systematic mapping study was performed, to select studies published between 2007 and 2018 which contained original research into the application of coroutines on resource-constrained platforms. An initial set of 566 candidate papers were reduced to only 35 after filters were applied, revealing the following taxonomy. The C & C++ programming languages were used by 22 studies out of 35. As regards hardware, 16 studies used 8- or 16-bit processors while 13 used 32-bit processors. The four most common use cases were concurrency (17 papers), network communication (15), sensor readings (9) and data flow (7). The leading intended benefits were code style and simplicity (12 papers), scheduling (9) and efficiency (8). A wide variety of techniques have been used to implement coroutines, including native macros, additional tool chain steps, new language features and non-portable assembly language. We conclude that there is widespread demand for coroutines on resource-constrained devices. Our findings suggest that there is significant demand for a formalised, stable, well-supported implementation of coroutines in C++, designed with consideration of the special needs of resource-constrained devices, and further that such an implementation would bring benefits specific to such devices.
This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are re
sponsible for proactive and reactive monitoring and mitigation based on specified layers of competence. The agents are self-protecting, detecting cascading failures using a distributed approach. Adaptive, reconfigurable, and mobile objects for reliablility are designed to be self-configuring to adapt automatically to dynamically changing environments. These objects provide a self-healing layer with the ability to discover, diagnose, and react to discontinuities in real-time processing. A generic modeling environment was developed to facilitate design and implementation of hardware resource specifications, application data flow, and failure mitigation strategies. Level 1 of the planned BTeV trigger system alone will consist of 2500 DSPs, so the number of components and intractable fault scenarios involved make it impossible to design an `expert system that applies traditional centralized mitigative strategies based on rules capturing every possible system state. Instead, a distributed reactive approach is implemented using the tools and methodologies developed by the Real-Time Embedded Systems group.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
