Studying software metrics based on real-world software systems
Journal of Computing Sciences in Colleges
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Editorial: Search-based software engineering
Computers and Operations Research
Locating dependence structures using search-based slicing
Information and Software Technology
Optimization of software components selection for component-based software system development
Computers and Industrial Engineering
The relationship between search based software engineering and predictive modeling
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Expert Systems with Applications: An International Journal
Proceedings of the 33rd International Conference on Software Engineering
A prediction approach to support alternative design decision for component-based system development
SEPADS'12/EDUCATION'12 Proceedings of the 11th WSEAS international conference on Software Engineering, Parallel and Distributed Systems, and proceedings of the 9th WSEAS international conference on Engineering Education
Advances in Software Engineering
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Investigating of high and low impact faults in object-oriented projects
ACM SIGSOFT Software Engineering Notes
Automatic generation of basis test paths using variable length genetic algorithm
Information Processing Letters
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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 100% of the faults should be detected if the top 50% of modules with highest number of faults are subjected to quality improvements. Moreover, the management team for the same system may also desire that 80% of the faults should be accounted if the top 20% 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 multiobjective MOM using genetic programming. Other estimation techniques, e.g., multiple linear regression and neural networks cannot achieve multiobjective 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.