An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization; Mathematics; Vol. 9, iss. 15
| Parent link: | Mathematics Vol. 9, iss. 15.— 2021.— [1722, 20 p.] |
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| Institution som forfatter: | |
| Andre forfattere: | , , , , |
| Summary: | Title screen Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has 2N feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%. |
| Sprog: | engelsk |
| Udgivet: |
2021
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| Fag: | |
| Online adgang: | https://doi.org/10.3390/math9151722 |
| Format: | xMaterials Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665211 |
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| 200 | 1 | |a An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization |f F. S. Khurma, H. Alsawalqah, I. Aljarah [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 52 tit.] | ||
| 330 | |a Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has 2N feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%. | ||
| 461 | |t Mathematics | ||
| 463 | |t Vol. 9, iss. 15 |v [1722, 20 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a moth flame optimization | |
| 610 | 1 | |a island-based model | |
| 610 | 1 | |a feature selection | |
| 610 | 1 | |a software defect prediction | |
| 610 | 1 | |a software reliability | |
| 701 | 1 | |a Khurma |b F. S. |g Ruba Abu | |
| 701 | 1 | |a Alsawalqah |b H. |g Hamad | |
| 701 | 1 | |a Aljarah |b I. |g Ibrahim | |
| 701 | 1 | |a Mokhamed Elsaed |b A. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1987- |g Akhmed Mokhamed |3 (RuTPU)RU\TPU\pers\46943 | |
| 701 | 1 | |a Damasevicius |b R. |g Robertas | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение информационных технологий |3 (RuTPU)RU\TPU\col\23515 |
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| 856 | 4 | |u https://doi.org/10.3390/math9151722 | |
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