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	1. Dynamic Granule Substitution and Granule Tree in GranuleJS
	ZENG Qing-Hua, WU Wen-Bin, ZHAO Yin-Liang, SUN Li-Yu
	Computer Science and Technology 18 December 2017
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:\renewcommand{\raggedright}{\leftskip=0pt \rightskip=0pt plus 0cm}\raggedrightModern software systems are usually running at various external environments, and are required to automatically maintain their behaviors to adapt the changed surrounding environments. To deal with the problem, Granule oriented programming was proposed and GranuleJ built top of Java was designed and implemented by language extensions. However, such static language infrastructure implementation often has an expensive cost and a petty high performance overhead in granule substitution, where granules as compositional code units are to be composed at runtime. In this paper, we propose an extension language called GranuleJS based on dynamic language JavaScript by fully leveraging dynamic nature of JavaScript's behaviors. We design granule tree for granule substation in two optimized ways, lazy chain approach and marking approach respectively. Moreover, since the dynamic feature of granule tree can cause the dissatisfaction of the invariance of context variable string path, we designed a fast similar granule search algorithm. The performance evaluation has shown that GranuleJS have fairly low performance overhead.
	TO cite this article:ZENG Qing-Hua, WU Wen-Bin, ZHAO Yin-Liang, et al. Dynamic Granule Substitution and Granule Tree in GranuleJS[OL].[18 December 2017] http://en.paper.edu.cn/en_releasepaper/content/4742873


	2. A Fitness Test Programming Model for Dynamic Evolution In Context-aware Applications
	ZENG Qing-Hua, ZHAO Yin-Liang, SUN Li-Yu, WU Wen-Bin
	Computer Science and Technology 18 December 2017
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	Abstract:\renewcommand{\raggedright}{\leftskip=0pt \rightskip=0pt plus 0cm}\raggedright Nowadays, modern context aware applications are increasingly expected to adapt their behaviors to the surrounding environmental changes autonomously to meet their desired needs at run-time, especially in the fields of pervasive and ubiquitous computing. However, since unforeseen context variations mostly arise at runtime, which are unknown to the developers, applications have to suffer from dynamic changes to hard-to-predict behaviors which cannot be explicitly specified after initially deployed. Most existing context-based languages can only provide anticipated adaptation which is usually predefined at the initial design time, resulting in the program is running on the wrong context. In this work, we propose a fitness test programming model to address the problem. The model enables detect the unfitness between program behavior and its related context, and then automatically adjust program behavior to adapt the current context, making the program perform dynamic evolution. To validate the feasibility and effectiveness of the model, we have developed a case study and run it in the GranuleJ language framework successfully.%From the perspective of language-level, language extension is an efficient and prompt approach to conduct those adaptable applications. However, the existing context-based languages can only provide anticipated adaptation which is usually predefined at the initial design time, and they also lack appropriate programming language abstractions of dynamic flavor to support context uncertainty at runtime.}%In this paper, we present a novel fitness test programming model, which enables implicit context checks to be aware of the adaptation of the program and carry out program evolution when the program is no longer satisfied with the current context. GranuleJ introduces \emph{context variable} to identify context changes clearly, \emph{fitness tests} to detect the adaptation points where unsuitable behaviors in the program happen relying on context variables and \emph{granules} that modularize behavior variations as reuse building blocks to be freely assembled or disassembled at runtime. We have already implemented the language framework of GranuleJ and validated the feasibility and effectiveness of it through case study and performance evaluation.
	TO cite this article:ZENG Qing-Hua, ZHAO Yin-Liang, SUN Li-Yu, et al. A Fitness Test Programming Model for Dynamic Evolution In Context-aware Applications[OL].[18 December 2017] http://en.paper.edu.cn/en_releasepaper/content/4742869


	3. GranuleJ: A Context Check-based Programming Language for Flexible Runtime Adaptation
	ZENG Qing-Hua, ZHAO Yin-Liang, SUN Li-Yu, WU Wen-Bin
	Computer Science and Technology 18 December 2017
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:\renewcommand{\raggedright}{\leftskip=0pt \rightskip=0pt plus 0cm}\raggedrightModern applications tend to increasingly suffer from unpredicted context changes that may occur at any moment during the program execution, so it is urgently needed to adapt their behaviors to such frequently changing contexts dynamically. From the perspective of language-level, language extension is an efficient and prompt approach to conduct those adaptable applications. However, the existing context-based languages can only provide anticipated adaptation which is usually predefined at the initial design time, and they also lack appropriate programming language abstractions of dynamic flavor to support context uncertainty at runtime. In this paper, we present a novel programming language called \emph{GranuleJ}, which enables implicit context checks to be aware of the adaptation of the program and carry out program evolution when the program is no longer satisfied with the current context. GranuleJ introduces \emph{context variable} to identify context changes clearly, \emph{fitness tests} to detect the adaptation points where unsuitable program behaviors happen relying on context variables and \emph{granules} that modularize behavior variations as reuse building blocks to be freely assembled or disassembled at runtime. We have already implemented the language framework of GranuleJ and validated the feasibility and effectiveness of it through performance evaluation.
	TO cite this article:ZENG Qing-Hua, ZHAO Yin-Liang, SUN Li-Yu, et al. GranuleJ: A Context Check-based Programming Language for Flexible Runtime Adaptation[OL].[18 December 2017] http://en.paper.edu.cn/en_releasepaper/content/4742867

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