Recommendation System for Design Patterns in Software Development: A DPR Overview

Recommendation System for Design Patterns in Software Development: A DPR Overview. Palma, F., Farzin, H., Gu�h�neuc, Y., & Moha, N. In Maalej, W., Robillard, M., Walker, R. J., & Zimmermann, T., editors, Proceedings of the 3rd International Workshop on Recommendation Systems for Software (RSSE), pages N/A, June, 2012. ACM Press. 5 pages.

Paper abstract bibtex

Software maintenance can become monotonous and expensive due to ignorance and misapplication of appropriate design patterns during the early phases of design and development. To have a good and reusable system, designers and developers must be aware of large information set and many quality concerns, e.g., design patterns. Systems with correct design pattern may ensure easy maintenance and evolution. However, without assistance, designing and development of software systems following certain design patterns is difficult for engineers. Recommendation systems for software engineering can assist designers and developers with a wide range of activities including suggesting design patterns. With the help of pattern recommenders, designers can come up with a reusable design. We provide a Design Pattern Recommender (DPR) process overview for software design to suggest design patterns, based on a simple Goal-Question-Metric (GQM) approach. Our prototype provides two-fold solution. In the primary-level, DPR only proposes one or more design patterns for a problem context, and in the secondary level, for a initial set of design, DPR refactors models and suggests design patterns. Our preliminary evaluation shows that DPR has a good trade-off between accuracy and procedural complexity, comparing to other state-of-the-art approaches.

@INPROCEEDINGS{Palma12-RSSE-DPR,
   AUTHOR       = {Francis Palma and Hadi Farzin and Yann-Ga�l Gu�h�neuc and 
      Naouel Moha},
   BOOKTITLE    = {Proceedings of the 3<sup>rd</sup> International Workshop on Recommendation Systems for Software (RSSE)},
   TITLE        = {Recommendation System for Design Patterns in Software 
      Development: A DPR Overview},
   YEAR         = {2012},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Walid Maalej and Martin Robillard and Robert J. Walker and 
      Thomas Zimmermann},
   MONTH        = {June},
   NOTE         = {5 pages.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {N/A},
   PUBLISHER    = {ACM Press},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Design patterns</b>, Venue: <i>RSSE</i>},
   URL          = {http://www.ptidej.net/publications/documents/RSSE12.doc.pdf},
   PDF          = {http://www.ptidej.net/publications/documents/RSSE12.ppt.pdf},
   ABSTRACT     = {Software maintenance can become monotonous and expensive 
      due to ignorance and misapplication of appropriate design patterns 
      during the early phases of design and development. To have a good and 
      reusable system, designers and developers must be aware of large 
      information set and many quality concerns, e.g., design patterns. 
      Systems with correct design pattern may ensure easy maintenance and 
      evolution. However, without assistance, designing and development of 
      software systems following certain design patterns is difficult for 
      engineers. Recommendation systems for software engineering can assist 
      designers and developers with a wide range of activities including 
      suggesting design patterns. With the help of pattern recommenders, 
      designers can come up with a reusable design. We provide a 
      \textit{Design Pattern Recommender} (DPR) process overview for 
      software design to suggest design patterns, based on a simple 
      Goal-Question-Metric (GQM) approach. Our prototype provides two-fold 
      solution. In the primary-level, DPR only proposes one or more design 
      patterns for a problem context, and in the secondary level, for a 
      initial set of design, DPR refactors models and suggests design 
      patterns. Our preliminary evaluation shows that DPR has a good 
      trade-off between accuracy and procedural complexity, comparing to 
      other state-of-the-art approaches.}
}

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