Uma proposta de representação e operadores genéticos para algoritmos evolucionários aplicados no reparo automatizado de software
保存先:
| 第一著者: | |
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| 出版日付: | 2017 |
| フォーマット: | Master thesis |
| 言語: | por |
| ソース: | Repositório Institucional da UFG |
| Download full: | http://repositorio.bc.ufg.br/tede/handle/tede/7767 |
要約: | Maintenance and software repair are responsible for most of the cost of a software in the course of its life. Software repair through genetic evolution may repair errors and improve software, reducing its high cost. GenProg is a technique that uses this approach and through patches evolution it is capable to fix errors in large and small softwares. A patch composed by low-granularity operations compromise the manipulation of these operations. These operations consist of three subspaces: operation, location of application of the operation and what the operation will apply at the location of the fault (operator, fault and fix, respectively). The recombination and mutation operators applied to a low granulation representation limits the ability of the technique to navigate in search space efficiently. It is proposed the reformulation of the representation, in order to allow greater search capability. Theoretical analysis of the representation showed that the new representation has a greater locality than the original one. Through experimentation, validation and genotypic analysis it is shown that the proposed changes have led to a better performance with respect to the original operators and parameters in terms of efficiency, in the first experiments the operator UnifSingle with memorization was 48.88% more effective than the Original operator and then the operator OPSingle_V2 was 26% more effective than the operator UnifSingle with memorization. Some characteristics of these cross-operators were observed through a genotype distance analysis and their influence on the automatic software reapair problem. The proposed mutation operator shown superior results if compared to original. Combination between operator UniSingle with memorization showed the best efficacy among all combinations of operators and parameters (28.29% superior to the best result of the original GenProg). |
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