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.
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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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Vaucher, S., Boclinville, S., Sahraoui, H., Habra, N. (2009). Recommending Improvements to Web Applications Using Quality-Driven Heuristic Search. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds) Web Information Systems Engineering - WISE 2009. WISE 2009. Lecture Notes in Computer Science, vol 5802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04409-0_33
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DOI: https://doi.org/10.1007/978-3-642-04409-0_33
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