- author = "George Smith Cowan",
- title = "An evolutionary computation approach to the software engineering of evolving programs",
- school = "Computer Science, Wayne State University",
- year = "1999",
- address = "Detroit, Michigan, USA",
- keywords = "genetic algorithms, genetic programming, Cultural Algorithm, Software Engineering Environment for Evolving Programs, SWEEP, Discipulus",
-
URL = "
http://search.proquest.com/docview/304534353",
- size = "196 pages",
-
abstract = "The Software Engineering Environment for Evolving
Programs (SWEEP) is a prototype for an evolving
software development environment consisting of adaptive
agents. SWEEP captures the salient features of
automated assistance for the software systems of the
future: distributed agents specializing in different
life-cycle activities, focusing on software evolution,
with an adaptive capability that will allow the agents
to evolve new development processes. The programming
agents for this system are Genetic Programming (GP)
elements. A common architectural scheme for the agents,
Cultural Algorithms (CA), facilitates the learning and
interaction between the agents. The processes used by
the GP agents create programs that are quite different
from those of traditional processes. Two specific
differences introduced with the use of the GP process
are the phenomenon of bloat and the software quality
issue of solution program generalizability to new
problem instances. Unnecessary complexity compromises
program generalization, but bloat obscures this
relationship by introducing extra structural complexity
into the program without affecting the program's
outputs. Three levels of bloat are characterized: local
bloat, global bloat , and representational
bloat.
Hopefully, any new assessment processes for new quality issues and new code phenomena can build on traditional assessment knowledge. The Metrics Apprentice (MA) is a prototype knowledge-based Software Quality Agent that constructs new software metrics, based on traditional software engineering metrics, for assessing software quality issues. To facilitate the learning of new concepts, the MA uses a CA shell that combines an evolutionary programming population component with a semantic network of schemata, or generalized knowledge, in a belief space. The performance of the system for a symbolic regression problem was compared to that of a traditional linear discriminant analysis (LDA) statistical approach. As more bloat was removed, the knowledge-based MA was able to outperform the LDA approach through the emergence of a hierarchical structure in its belief space. This structure allowed the MA knowledge-based approach to climb the conceptual hierarchy to use traditional software metrics that had a higher knowledge level, such as Intelligence Content, leading to an increased ability to predict the Generalizability of the GP produced code.",
-
notes = "http://genealogy.math.ndsu.nodak.edu/id.php?id=100311
Supervisor: Robert Gene Reynolds
McCabe's Cyclomatic Complexity measurements.
Chapter 7 Relationship of Software Metrics to Bloat. Chapter 8 Defining a New Software Metric To Estimate Generalization Using the Metrics Apprentice UMI Microform 9956975",
