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Automated Code Generation Framework for Industrial Automation Applications based on Timed Automata Model
Kyunghyun Lee, Ikhwan Kim, Taehyoun Kim
http://doi.org/10.5626/JOK.2017.44.12.1307
Due to their convergence with state-of-the-art ICT technologies, the complexity and reliability demands of industrial automation systems have been rapidly increasing. In recent years, to cope with these demands, several research works have been carried out to adopt formal methods to the application development cycle at the early design stage. In this paper, we propose an automated code generation framework for industrial automation applications, based on a timed automata model. As a case study, we developed a formal model for a traffic light control system and verified the timing properties described in the specification. We finally demonstrated that the operation of a test-bed based on the auto-generated native code was identical to that of the model specification.
Static Analysis of Large Scale Software Repositories Using WALA and Boa
http://doi.org/10.5626/JOK.2017.44.10.1081
A program analysis of a large-scale open-source software repository has a significant meaning in that it allows us to examine the changes and improvements of the software in repositories, and this brings more reliable results based on a large amount of programs. In this paper, we introduce a new static analysis framework WALABOA, which enables a scalable static analysis of large-scale software repositories. In addition, we show new findings from applying WALABOA, together with a module comparing the analysis results from a static analysis and a dynamic analysis, in evaluation of the field-based analysis, one of JavaScript static analysis techniques used in WALA.
Development of Reconfigurable Tactical Operation Display Framework by Battery and Battalion
Sangtae Lee, Seungyoung Lee, SoungHyouk Wi, Kyutae Cho
The tactical operation centers of future anti-aircraft missile systems provide the environment for the research on future air threats, tactical information, integrated battlefield environment creation and management, engagement control and command and control algorithms. To develop the key functional elements of integrated battlefield situation creation and processing and tactical operation automation processing operations, battery/battalion tactical operation control and reconfiguration design software are required. Therefore, the algorithm software of each function and the tactical operation display software and link software for interworking between equipment were developed as reconfigurable through a data-centric design. In this paper, a tactical operation display framework that can be reconfigured on the operation display of the tactical operations according to the battery/battalion is introduced. This tactical operation display framework was used to develop a common data model design for the reconfigurable structure of multi-role tactical operations with battery / battalion and mission views, and a display configuration tool that provides a tactical operation display framework for view development was also developed using the MVC pattern. If the tactical operation display framework is used, it will be possible to reuse the view design through the common base structure, and a view that can be reconfigured easily and quickly will also be developed.
Techniques to Guarantee Real-Time Fault Recovery in Spark Streaming Based Cloud System
Jungho Kim, Daedong Park, Sangwook Kim, Yongshik Moon, Seongsoo Hong
In a real-time cloud environment, the data analysis framework plays a pivotal role. Spark Streaming meets most real-time requirements among existing frameworks. However, the framework does not meet the second scale real-time fault recovery requirement. Spark Streaming fault recovery time increases in proportion to the transformation history length called lineage. This is because it recovers the last state data based on the cumulative lineage recorded during normal operation. Therefore, fault recovery time is not bounded within a limited time. In addition, it is impossible to achieve a second-scale fault recovery time because it costs tens of seconds to read initial state data from fault-tolerant storage. In this paper, we propose two techniques to solve the problems mentioned above. We apply the proposed techniques to Spark Streaming 1.6.2. Experimental results show that the fault recovery time is bounded and the average fault recovery time is reduced by up to 41.57%.
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