Understanding Data-Intensive Systems through the Analysis of SQL Execution Traces

Défense de thèse Nesrine Noughi

Most software systems need to be adapted during their life-cycle. It is estimated that more than 60\% of the cost of  a software system is related to its maintenance and evolution. The evolution of a software system not only involves the modification made to its various components, but it also includes an indispensable preliminary phase, which is the in-depth understanding of each component of the system. This is especially true for a certain type of systems called data-intensive systems. Within such a system, the interactions between the application programs and the database are becoming increasingly difficult to analyze, and therefore to understand.

This is why program understanding in general has become a important topic of interest for software engineering researchers and developers. In particular, understanding today's data-intensive systems clearly calls for automated support.

The goal of this thesis is to facilitate the understanding of large data-intensive systems using dynamic analysis, visualization and process mining techniques. The dynamic analysis techniques seek to analyze and visualize the data-manipulation behavior of data-intensive systems via the analysis of their SQL execution traces. The process mining techniques seek to retrieve recurring patterns and extract the data-manipulation processes followed by the program itself.

Contact : Isabelle Daelman - 4966 - isabelle.daelman@hotmail.com
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