Close this search box.

Ph.D. Student Paper Receives Best Paper Award at the SEAMS 2013

Ph.D. Student Paper Receives Best Paper Award at the SEAMS 2013

/uploadedImages/people/students/casanova-paulo_100x100.jpg “Diagnosing Architectural Run-time Failures” is the title of the paper that granted a Best Paper Award to Paulo Casanova, a dual degree doctoral student in Software Engineering/Computer Science (SE/CS), at Faculdade de CIências e Tecnologia of the Universidade de Coimbra and Carnegie Mellon University. The paper focuses on systems behavior and was presented at the “8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems” (SEAMS 2013), in May 20-21, 2013.

The paper was co-written by Paulo Casanova, David Garlan, and Bradley Schmerl, from Carnegie Mellon University, and Rui Abreu, from Faculdade de Engenharia of the Universidade do Porto. “While significant research has been done over the past years on self-healing systems, identifying which specific parts of the system have failed is still a significant challenge,” explains Paulo Casanova adding that “this research proposes a new technique that leverages design-time diagnosis techniques to perform autonomic diagnosis at run time to identify which parts of the system are failing”. The SEAMS Symposium brings together many of the world’s experts on self-adaptive systems, and “the best paper award signals both the relevance of this area of research and the paper’s contributions to the field of autonomic computing,” stresses Paulo Casanova.

The abstract of the paper explains that it addresses three major shortcomings of the researchers’ previous work. First of all, it uses “an expressive, hierarchical language to describe system behavior that can be used to diagnose when a system is behaving different to expectation; the hierarchical language facilitates mapping low level system events to architecture level events,” Paulo Casanova explains. Moreover, in this work, the researchers provide “an automatic way to determine how much data to collect before an accurate diagnosis can produce”, and “a technique that allows the detection of correlated faults between components”. The results have already been tested and validated by injecting several failures in a system and accurately diagnosing them using the algorithm proposed by the researchers.

Paulo Casanova is a CMU Portugal dual degree Ph.D. student since 2010/2011. He is involved in several research projects, and is co-advised by Mário Zenha-Rela, at the Faculdade de Ciências of the Universidade de Coimbra, and by David Garlan at CMU.

July 2013