Figure 1(a) provides a histogram charting SBSE publication growth over time, while
Figure 1(b) shows the proportion of papers that fall into each of the different SE
application area subject categories.
Harman and Jones [Harman 2007b; Harman and Jones 2001a] identified two key
ingredients for the application of SBO to SE problems:
(1) the choice of the representation of the problem; and
(2) the definition of the fitness function.
This simplicity and ready applicability makes SBSE a very attractive option. Typi-
cally, a software engineer will have a suitable representation for her problem, because
one cannot do much engineering without a way to represent the problem in hand.
Furthermore, many problems in SE have a rich and varied set of software metrics
associated with them that naturally form good initial candidates for fitness functions
[Harman and Clark 2004]. With these two ingredients it becomes possible to implement
SBO algorithms.
Naturally, there is a lot more to the application of these techniques, but these two sim-
ple ingredients are sufficient to get started with experimentation. Poulding et al. [2007]
presented a framework for experimental investigation of the different algorithms. An
overview of search techniques is available in other surveys [Harman 2007b], while a
more detailed treatment of search methodologies can be found in the book edited by
Burke and Kendall [2005].
3. CLASSIFICATION SCHEME
Our classification of SE activities is taken from the Association for Computing Machin-
ery (ACM) Computing Classification System, projected onto those SE areas to which
SBSE has been applied (see Table I). A list of query keywords was constructed for
each of the activities and each of the search techniques (see Table II). For example,
the search term used to locate papers on Search-Based Requirements/Specifications
(D.2.1) was:
((requirements OR specifications OR next release OR release planning OR require-
ments selection OR requirements analysis OR COTS OR requirements prioritization
OR requirements triage) AND (search based OR optimization OR multiobjective opti-
mization OR search techniques))
We used the following sources from which to search: Google Scholar, IEEE Xplore
Digital Library, ACM Digital Library, SpringerLink, ScienceDirect, and Wiley
ACM Computing Surveys, Vol. 45, No. 1, Article 11, Publication date: November 2012
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