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Recommending Improvements to Web Applications Using Quality-Driven Heuristic Search

  • Stephane Vaucher
  • Samuel Boclinville
  • Houari Sahraoui
  • Naji Habra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5802)

Abstract

Planning out maintenance tasks to increase the quality of Web applications can be difficult for a manager. First, it is hard to evaluate the precise effect of a task on quality. Second, quality improvement will generally be the result of applying a combination of available tasks; identifying the best combination can be complicated. We present a general approach to recommend improvements to Web applications. The approach uses a meta-heuristic algorithm to find the best sequence of changes given a quality model responsible to evaluate the fitness of candidate sequences. This approach was tested using a navigability model on 15 different Web pages. The meta-heuristic recommended the best possible sequence for every tested configuration, while being much more efficient than an exhaustive search with respect to execution time.

Keywords

Quality Model Simulated Annealing Algorithm Neighbourhood Function Cost Constraint Transformation Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stephane Vaucher
    • 1
  • Samuel Boclinville
    • 2
  • Houari Sahraoui
    • 1
  • Naji Habra
    • 2
  1. 1.DIRO, Université de MontréalQuébecCanada
  2. 2.PReCISE Research Center, FUNDPUniversity of NamurBelgium

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