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Automatically classifying benign and harmful data racesallusing replay analysis

Satish Narayanasamy, Zhenghao Wang, Jordan Tigani, Andrew Edwards, Brad Calder
<span title="">2007</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jpubwsjaozha5itdes6pzyz2fm" style="color: black;">Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation - PLDI &#39;07</a> </i> &nbsp;
In this paper, we instead focus on a dynamic analysis technique to automatically classify the data races into two categories -the data races that are potentially benign and the data races that are potentially  ...  To automatically classify if a data race that we find is potentially benign or potentially harmful, we replay the execution twice for a given data race -one for each possible order between the conflicting  ...  This work was funded by grants from Intel and Microsoft.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1250734.1250738">doi:10.1145/1250734.1250738</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/pldi/NarayanasamyWTEC07.html">dblp:conf/pldi/NarayanasamyWTEC07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7mdhiaq7efg7raoso4eh6xz2ee">fatcat:7mdhiaq7efg7raoso4eh6xz2ee</a> </span>
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