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http://dx.doi.org/10.14372/IEMEK.2018.13.6.305

A Framework for Detecting Data Races in Weapon Software  

Oh, Jin-Woo (Gyeongsang National University, DTaQ)
Choi, Eu-Teum (Gyeongsang National University)
Jun, Yong-Kee (Gyeongsang National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.13, no.6, 2018 , pp. 305-312 More about this Journal
Abstract
Software has been used to develop many functions of the modern weapon systems which has a high mission criticality. Weapon system software must consider multi-threaded processing to satisfy growing performance requirement. However, developing multi-threaded programs are difficult because of concurrency faults, such as unintended data races. Especially, it is important to prepare analysis for debugging the data races, because the weapon system software may cause personal injury. In this paper, we present an efficient framework of analysis, called ConDeWS, which is designed to determine the scope of dynamic analysis through using the result of static analysis and fault analysis. As a result of applying the implemented framework to the target software, we have detected unintended data races that were not detected in the static analysis.
Keywords
Multi-thread; Weapon system software; Concurrency faults; Data races; Dynamic analysis;
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