• Journal of KIISE:Computer Systems and Theory
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• v.32 no.7
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• pp.341-348
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• 2005

Detecting unaffected race conditions is important for debugging message-passing programs effectively, because such races can influence other races to occur or not. The previous technique used in detecting unaffected races detects a race by halting the execution of a process at the receive event of the race that errors first in the process. However this technique does not guarantee that all of the detected races are unaffected, because halting the execution of processes does disconnect some chains of affects-relations among those races. Tn this paper. we improved the second pass algorithm of the previous technique by producing information about affects-relations of the races that occur first in each Process. Then we effectively visualize affect-relations among the races detected in each process. This visualization is effective in detecting visually unaffected races by simplifying affects-relations among the races which occur first In each Process.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.8
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• pp.495-504
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• 2006

Detecting unaffected race conditions is important to debugging message-passing programs effectively, because such a message race can affect other races to occur or not. The previous technique to detect efficiently unaffected races detects racing messages by halting at the receive event of the first race to occur in each process. However this technique does not guarantee that all of the detected races are unaffected, because halting such processes does disconnect some chain of affects-relations among those races. In this paper, we present a novel technique that manages the state of the detected race by examining if every received message is affected until the execution terminates. Our technique therefore guarantees to detect efficiently the unaffected races, because it maintains affects-relations of the races all along the execution of program.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.17-24
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• 2014

Detecting data races is important for debugging shared-memory programs with nested parallelism, because races result in unintended non-deterministic executions of the program. It is especially important to detect the first occurred data races for effective debugging, because the removal of such races may make other affected races disappear or appear. Previous dynamic detection tools for first race detecting can not guarantee that detected races are unaffected races. Also, the tools does not consider the nesting levels or need support of other techniques. This paper suggests a post-mortem tool which collects candidate accesses during program execution and then detects the first races to occur on the program after execution. This technique is efficient, because it guarantees that first races reported by analyzing a nesting level are the races that occur first at the level, and does not require more analyses to the higher nesting levels than the current level.