Literature DB >> 25243670

Design pattern mining using distributed learning automata and DNA sequence alignment.

Mansour Esmaeilpour1, Vahideh Naderifar1, Zarina Shukur2.   

Abstract

CONTEXT: Over the last decade, design patterns have been used extensively to generate reusable solutions to frequently encountered problems in software engineering and object oriented programming. A design pattern is a repeatable software design solution that provides a template for solving various instances of a general problem.
OBJECTIVE: This paper describes a new method for pattern mining, isolating design patterns and relationship between them; and a related tool, DLA-DNA for all implemented pattern and all projects used for evaluation. DLA-DNA achieves acceptable precision and recall instead of other evaluated tools based on distributed learning automata (DLA) and deoxyribonucleic acid (DNA) sequences alignment.
METHOD: The proposed method mines structural design patterns in the object oriented source code and extracts the strong and weak relationships between them, enabling analyzers and programmers to determine the dependency rate of each object, component, and other section of the code for parameter passing and modular programming. The proposed model can detect design patterns better that available other tools those are Pinot, PTIDEJ and DPJF; and the strengths of their relationships.
RESULTS: The result demonstrate that whenever the source code is build standard and non-standard, based on the design patterns, then the result of the proposed method is near to DPJF and better that Pinot and PTIDEJ. The proposed model is tested on the several source codes and is compared with other related models and available tools those the results show the precision and recall of the proposed method, averagely 20% and 9.6% are more than Pinot, 27% and 31% are more than PTIDEJ and 3.3% and 2% are more than DPJF respectively.
CONCLUSION: The primary idea of the proposed method is organized in two following steps: the first step, elemental design patterns are identified, while at the second step, is composed to recognize actual design patterns.

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Year:  2014        PMID: 25243670      PMCID: PMC4171372          DOI: 10.1371/journal.pone.0106313

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  4 in total

1.  Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

Authors:  Hufeng Zhou; Shangzhi Gao; Nam Ninh Nguyen; Mengyuan Fan; Jingjing Jin; Bing Liu; Liang Zhao; Geng Xiong; Min Tan; Shijun Li; Limsoon Wong
Journal:  Biol Direct       Date:  2014-04-08       Impact factor: 4.540

2.  Comparative analysis and assessment of M. tuberculosis H37Rv protein-protein interaction datasets.

Authors:  Hufeng Zhou; Limsoon Wong
Journal:  BMC Genomics       Date:  2011-11-30       Impact factor: 3.969

3.  Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

Authors:  Hufeng Zhou; Javad Rezaei; Willy Hugo; Shangzhi Gao; Jingjing Jin; Mengyuan Fan; Chern-Han Yong; Michal Wozniak; Limsoon Wong
Journal:  BMC Syst Biol       Date:  2013-12-13

4.  IntPath--an integrated pathway gene relationship database for model organisms and important pathogens.

Authors:  Hufeng Zhou; Jingjing Jin; Haojun Zhang; Bo Yi; Michal Wozniak; Limsoon Wong
Journal:  BMC Syst Biol       Date:  2012-12-12
  4 in total

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