Design Pattern Mining Using Distributed Learning Automata and DNA Sequence Alignment

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