Distance-based Trace Diagnosis for Multimedia Applications: Help me TED!

Abstract : Execution traces have become essential resources that many developers analyze to debug their applications. Ideally, a developer wants to quickly detect whether there are anomalies on his application or not. However, in practice, the size of multimedia applications trace can reach gigabytes, which makes their exploitation very complex. Usually, developers use visualization tools before stating a hypothesis. In this paper, we argue that this solution is not satisfactory and propose to automatically provide a diagnosis by comparing execution traces. We use distance-based models and conduct a user case to show how TED, our automatic trace diagnosis tool, provides semantic added-value information to the developer. Performance evaluation over real world data shows that our approach is scalable.
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Communication dans un congrès
David A. Evans, Mihaela van der Schaar, Phillip Sheu, Jeffrey Abbott. ICSC 2013 - International Conference on Semantic Computing, Sep 2013, Irvine, CA, United States. IEEE, pp.306 - 309, 2013, 〈10.1109/ICSC.2013.59〉
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https://hal.archives-ouvertes.fr/hal-00923537
Contributeur : Noha Ibrahim <>
Soumis le : vendredi 3 janvier 2014 - 17:37:18
Dernière modification le : mardi 28 octobre 2014 - 18:34:49

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Christiane Kamdem Kengne, Noha Ibrahim, Marie-Christine Rousset, Maurice Tchuenté. Distance-based Trace Diagnosis for Multimedia Applications: Help me TED!. David A. Evans, Mihaela van der Schaar, Phillip Sheu, Jeffrey Abbott. ICSC 2013 - International Conference on Semantic Computing, Sep 2013, Irvine, CA, United States. IEEE, pp.306 - 309, 2013, 〈10.1109/ICSC.2013.59〉. 〈hal-00923537〉

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