reverse engineering – recent advances and applications - OpenLibra

Preface
Introduction
In the recent decades, the amount of data produced by scientific, engineering, and life
science applications has increased with several orders of magnitude. In parallel with
this development, the applications themselves have become increasingly complex in
terms of functionality, structure, and behaviour. In the same time, development and
production cycles of such applications exhibit a tendency of becoming increasingly
shorter, due to factors such as market pressure and rapid evolution of supporting and
enabling technologies.
As a consequence, an increasing fraction of the cost of creating new applications and
manufacturing processes shifts from the creation of new artifacts to the adaption of
existing ones. A key component of this activity is the understanding of the design,
operation, and behavior of existing manufactured artifacts, such as software code
bases, hardware systems, and mechanical assemblies. For instance, in the software
industry, it is estimated that maintenance costs exceed 80% of the total costs of a
software product’s lifecycle, and software understanding accounts for as much as half
of these maintenance costs.
Reverse engineering encompasses the set of activities aiming at (re)discovering the
functional, structural, and behavioral semantics of a given artifact, with the aim of
leveraging this information for the efficient usage or adaption of that artifact, or the
creation of related artifacts. Rediscovery of information is important in those cases
when the original information is lost, unavailable, or cannot be efficiently processed
within a given application context. Discovery of new information, on the other hand, is
important when new application contexts aim at reusing information which is
inherently present in the original artifact, but which was not made explicitly available
for reuse at the time of creating that artifact.
Reverse engineering has shown increasing potential in various application fields
during the last decade, due to a number of technological factors. First, advances in
data analysis and data mining algorithms, coupled with an increase of cheap
computing power, has made it possible to extract increasingly complex information
from raw data, and to structure this information in ways that make it effective for