Static Analysis of Data-Intensive Applications

Abstract

Data-intensive systems are designed to handle data at massive scale, and during the years they might evolve to very large, complex systems. In order to support maintenance tasks of these systems several techniques have been developed to analyze the source code of applications or to analyze the underlying databases for the purpose of reverse engineering, e.g. quality assurance or program comprehension. However, only a few techniques take into account the specialties of data-intensive systems (e.g. dependencies arising via database accesses).In this thesis we conducted research to analyze and to improve data-intensive applications via different methods based on static analysis: methods for recovering architecture of data-intensive systems and a quality assurance methodology for applications developed in Magic 4GL. We targeted SQL as the most widespread databases are relational databases using certain dialect of SQL for their queries. With the proposed techniques we were able to analyze large scale industrial projects, such as banking systems with more than 3 million lines of code, and we successfully recovered architecture maps and quality issues of these systems.

On Testing Data-Intensive Software Systems

“…A certain number of papers concern testing the correctness and the integrity of the data stored in databases by data-intensive systems. For instance, static analysis has been used to test SQL statements embedded in the code of data-intensive systems to ensure database integrity [22].…”

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

“…Query sequences can be obtained from database query logs as in [17,22,30,33], for instance, or by code analysis as in [19,26], including the frequency and the time of arrival. More formally, a query sequence is an ordered set of n individual queries Q 0 ; :::; Q n−1 .…”