Evolution of legacy system comprehensibility through automated refactoring

  • Authors:

  • Isaac Griffith;Scott Wahl;Clemente Izurieta

  • Affiliations:

  • Montana State University, Bozeman, MT;Montana State University, Bozeman, MT;Montana State University, Bozeman, MT

  • Venue:
  • Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
  • Year:
  • 2011

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Abstract

Software engineering is a continually evolving discipline, wherein researchers and members of industry are working towards defining and refining what are known as "best practices." Best practices are the set of known correct engineering techniques that lead to quality software. When a software artifact is produced, it becomes temporally locked into a single instantiation of these best practices at a given point in time. If such software is not maintained in such a way as to keep it current with the evolution of practice, then there is a good chance that subsequent engineers may not understand the design choices made. There are known techniques, called refactorings, which allow for structural changes to software without altering the outward appearance and behavior, thus maintaining the intent of the original design. Unfortunately, refactoring requires an engineer to both understand the techniques to be applied and the code to which they are applied to. This is not always feasible. We have developed an automated system utilizing Evolutionary Algorithms to manipulate refactorings correctly without requiring an underlying understanding of the software. This then allows for sustained levels of quality of evolving software systems. The understandability, maintainability, and reusability of the software regenerate as best practices evolve.