Skip to Main content Skip to Navigation

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, 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.
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download
Contributor : Grégory Mounié Connect in order to contact the contributor
Submitted on : Friday, November 25, 2016 - 6:24:09 PM
Last modification on : Tuesday, December 7, 2021 - 5:50:02 PM
Long-term archiving on: : Tuesday, March 21, 2017 - 4:00:13 AM


Files produced by the author(s)


  • HAL Id : hal-00923547, version 2



Christiane Kamdem Kengne, Noha Ibrahim, Marie-Christine Rousset, Maurice Tchuente. Distance-Based Trace Diagnosis for Multimedia Applications: Help Me TED!. [Research Report] RR-LIG-045, LIG. 2013. ⟨hal-00923547v2⟩



Record views


Files downloads