- author = "Taghi M. Khoshgoftaar and Yi Liu and Naeem Seliya",
- title = "A Multiobjective Module-Order Model for Software Quality Enhancement",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2004",
- volume = "8",
- number = "6",
- pages = "593--608",
- month = dec,
- keywords = "genetic algorithms, genetic programming, module-order model (MOM), multiobjective optimization (MOO), software metrics, software quality estimation, SBSE",
- bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
- ISSN = "1089-778X",
-
DOI = "
doi:10.1109/TEVC.2004.837108",
- size = "16 pages",
-
abstract = "The knowledge, prior to system operations, of which
program modules are problematic is valuable to a
software quality assurance team, especially when there
is a constraint on software quality enhancement
resources. A cost-effective approach for allocating
such resources is to obtain a prediction in the form of
a quality-based ranking of program modules.
Subsequently, a module-order model (MOM) is used to
gauge the performance of the predicted rankings. From a
practical software engineering point of view, multiple
software quality objectives may be desired by a MOM for
the system under consideration: e.g., the desired
rankings may be such that 100percent of the faults
should be detected if the top 50percent of modules with
highest number of faults are subjected to quality
improvements. Moreover, the management team for the
same system may also desire that 80percent of the
faults should be accounted if the top 20percent of the
modules are targeted for improvement.
Existing work related to MOM(s) use a quantitative prediction model to obtain the predicted rankings of program modules, implying that only the fault prediction error measures such as the average, relative, or mean square errors are minimized. Such an approach does not provide a direct insight into the performance behavior of a MOM. For a given percentage of modules enhanced, the performance of a MOM is gauged by how many faults are accounted for by the predicted ranking as compared with the perfect ranking. We propose an approach for calibrating a multi-objective MOM using genetic programming. Other estimation techniques, e.g., multiple linear regression and neural networks cannot achieve multi objective optimization for MOM(s). The proposed methodology facilitates the simultaneous optimization of multiple performance objectives for a MOM. Case studies of two industrial software systems are presented, the empirical results of which demonstrate a new promise for goal-oriented software quality modeling.",
- notes = "lilgp. Also known as \cite{1369249}",
