11:26 M. Harman et al.
and most easily applied, which may be why this area has already been considered in
the literature.
Though all of these may not occur in the same systems, they are all the subject
of change and, should a suitable fitness function be found, can therefore be evolved.
Where two such populations are evolved in isolation, but participate in the same overall
software system, it would seem a logical “next step”, to seek to evolve these populations
together; the fitness of one is likely to have an impact on the fitness of another, so
evolution in isolation may not be capable of locating the best solutions. Like the move
from single to multiple objectives, the migration from evolution to coevolution offers
the chance to bring together theory and real-world reality.
12. FUTURE BENEFITS TO BE EXPECTED FROM OPTIMIZATION IN SE
This section briefly reviews some of the benefits that can be expected to accrue from
further development of the field of search-based SE. These benefits are pervading,
though often implicit, themes in SBSE research. To borrow the nomenclature of aspect-
oriented software development, these are the “cross cutting concerns” of the SBSE
world, advantages that can be derived from almost all applications at various points
in their use.
12.1. Generality and Applicability
One of the striking features of the SBSE research program that emerges from this
survey is the wide variety of different SE problems to which SBSE has been applied.
Clearly, testing remains a predominant application, with 54% of all SBSE papers
targeting various aspects of testing. However, as the survey reveals, there are few
areas of SE activity to which SBO remains unapplied.
This generality and applicability arises from the very nature of SE. The two primary
tasks that have to be undertaken before a search-based approach can be applied to
an SE problem are the definition of a representation of the problem and the fitness
function that captures the objective or objectives to be optimized. Once these two tasks
are accomplished, it is possible to begin to get results from the application of many
SBO techniques.
In other engineering disciplines, it may not be easy to represent a problem; the
physical properties of the engineering artifact may mean that simulation is the only
economical option. This puts the optimization algorithm at one stage removed from
the engineering problem at hand. Furthermore, for other engineering disciplines, it
may not be obvious how to measure the properties of the engineering artifact to be
optimized. Even where the measurements required may be obvious, it may not be easy
to collect the readings; once again the physical properties of the engineering materials
may be a barrier to the application of optimization techniques.
However, software has no physical manifestation. Therefore, there are fewer prob-
lems with the representation of a software artifact, since almost all software artifacts
are, by their very nature, based on intangible “materials” such as information, pro-
cesses, and logic. This intangibility has made many problems for SE. However, by
contrast, within the realm of SBSE, it is a significant advantage. There are few SE
problems for which there will be no representation, and the readily available represen-
tations are often ready to use “out of the box” for SBSE.
Furthermore, measurement is highly prevalent in Software Engineering, with a
whole field of research in software metrics that has spawned many conferences and
journals. Therefore, it is also unlikely that the would-be search-based software engineer
will find him or herself bereft of any putative fitness function.
đang được dịch, vui lòng đợi..
