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Refactoring aims to improve the quality of a software systems’ structure, which tends to degrade as the system evolves. While manually determining useful refactorings can be challenging, search based techniques can automatically discover useful refactorings. Current search based refactoring approaches require metrics to be combined in a complex fashion, and produce a single sequence of refactorings. In this paper we show how Pareto optimality can improve search based refactoring, making the combination of metrics easier, and aiding the presentation of multiple sequences of optimal refactorings to users.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • [6] D. Fatiregun, M. Harman, and R. Hierons. Evolving transformation sequences using genetic algorithms. In 4th International Workshop on Source Code Analysis and Manipulation (SCAM 04), pages 65-74, Los Alamitos, California, USA, Sept. 2004. IEEE Computer Society Press.
    • [7] D. Fatiregun, M. Harman, and R. Hierons. Search-based amorphous slicing. In 12th International Working Conference on Reverse Engineering (WCRE 05), pages 3-12, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA, Nov. 2005.
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    • [12] Z. Li, M. Harman, and R. Hierons. Meta-heuristic search algorithms for regression test case prioritization. IEEE Transactions on Software Engineering. To appear.
    • [13] Nashat Mansour, Rami Bahsoon and G. Baradhi. Empirical comparison of regression test selection algorithms. Systems and Software, 57(1):79-90, 2001.
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    • [17] C. Ryan. Automatic re-engineering of software using genetic programming. Kluwer Academic Publishers, 2000.
    • [18] O. Seng, J. Stammel, and D. Burkhart. Search-based determination of refactorings for improving the class structure of object-oriented systems. In Proc. Genetic and Evolutionary Computation, 2006.
    • [19] L. Tratt. Converge Reference Manual, September 2004. http://www.convergepl.org/documentation/refmanual/ Accessed Jan 3 2006.
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