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Kosinüs Benzerliğine Dayalı Çapraz-proje Hata Tahmini

Yıl 2019, Cilt: 12 Sayı: 3, 159 - 167, 31.07.2019
https://doi.org/10.17671/gazibtd.453436

Öz

Çapraz-proje tahmini özellikle metrik heterojenliği açısından
araştırmacıların ilgisini çekmekte, bu alanda yeni yöntemlere ihtiyaç
duyulmaktadır. Hata tahmin işleminin farklı projeler üzerinden yürütülmesi
geliştiricilere anlamlı bilgiler sunmaktadır. Bu çalışmada, çapraz-proje
tahmini için, Kosinüs benzerliğine dayalı metrik eşleştirmesi yapan CSCDP
isimli bir algoritma geliştirilmiştir. Yöntem 36 farklı veri setinde üç farklı
sınıflandırıcı ile test edilmiştir.  Elde
edilen sonuçlara göre ortalama tahmin performansının yapay sinir ağlarında
diğer sınıflandırıcılara göre daha yüksek olduğu tespit edilmiştir. Ayrıca,
seyreklik analizine dayalı olarak seçilen eğitim veri setlerinin test
başarısını olumlu etkilediği tespit edilmiştir. Son olarak, CSCDP kullanılarak yürütülen
çapraz-proje tahmininin sınıflandırma hatasını Random Forest algoritmasında
F-skor parametresi için 0.65 oranında azalttığı gözlemlenmiştir.

Kaynakça

  • J. Nam, W. Fu, S. Kim, T. Menzies, T., L. Tan, “Heterogeneous defect prediction”, IEEE Transactions on Software Engineering,(1), 2017.
  • S. Wang, L. Taiyue, L. Tan. "Automatically learning semantic features for defect prediction." Software Engineering (ICSE), 2016 IEEE/ACM 38th International Conference , 2016.
  • S. Herbold, "Training data selection for cross-project defect prediction", Proceedings of the 9th International Conference on Predictive Models in Software Engineering, ACM, 6, 2013.
  • Y. Zhang, X. Lo, J. Sun, “An empirical study of classifier combination for cross-project defect prediction”, Computer Software and Applications Conference (COMPSAC), 2, 264-269, 2015.
  • C. Ni, "A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction", Journal of Computer Science and Technology, 32(6), 2017.
  • Q. Yu, J. Shujuan, Y. Zhang, "A feature matching and transfer approach for cross-company defect prediction", Journal of Systems and Software, 132, 2017.
  • Q. Yu, S. Jiang, J. Qian, “Which is more important for cross-project defect prediction: instance or feature?”, Software Analysis, Testing and Evolution (SATE), International Conference, 90-95, 2016.
  • Y. Zhou, Y. Yang, H. Lu, L. Chen, L., Y., Zhao, “How Far We Have Progressed in the Journey?, An Examination of Cross-Project Defect Prediction”, ACM Transactions on Software Engineering and Methodology (TOSEM), 27(1), 1, 2018.
  • X. Xia, L. O. David, S. J. Pan, N. Nagappan, X.Wang, “Hydra: Massively compositional model for cross-project defect prediction”, IEEE Transactions on Software Engineering, 42(10), 2016.
  • H. V. Nguyen, L. Bai, L., “Cosine similarity metric learning for face verification”, Asian conference on computer vision, 709-720). 2010.
  • T. Zimmermann, N. Nagappan, H. Gall, E. Giger, B. Murphy, “Cross-project defect prediction: a large scale experiment on data vs. domain vs. process”, Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, 91-100, 2009.
  • F. Zhang, Q. Zheng, Y. Zou, A. E. Hassan, “Cross-project defect prediction using a connectivity-based unsupervised classifier”, Proceedings of the 38th International Conference on Software Engineering, 309-320, 2016.
  • D. Ryu, J. I. Jang, J. Baik, “A transfer cost-sensitive boosting approach for cross-project defect prediction”, Software Quality Journal, 25(1), 2017.
  • W. N Poon, K. E. Bennin, J. Huang, P. Phannachitta, J. W. Keung, “Cross-project defect prediction using a credibility theory based naive bayes classifier”, Software Quality, Reliability and Security (QRS), 2017 IEEE International Conference, 434-441.
  • N. Limsettho, K. E. Bennin, J. W. Keung, H. Hata, H., & K. Matsumoto, “Cross project defect prediction using class distribution estimation and oversampling”, Information and Software Technology, 100, 2018.
  • S. Herbold, A. Trautsch, J. Grabowski, “A comparative study to benchmark cross-project defect prediction approaches”, Proceedings of the 40th International Conference on Software Engineering, 1063-1063, 2017.
  • F. Wu, X. Y. Jing, X. Dong, J., “Cross-project and within-project semi-supervised software defect prediction problems study using a unified solution”, In Software Engineering Companion (ICSE-C), 2017 IEEE/ACM 39th International Conference, 195-197.
  • X. Jing, F. Wu, X. Dong, F. Qi, B. Xu, “Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning”, Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, 496-507, 2015.
  • S. Herbold, “Benchmarking cross-project defect prediction approaches with costs metrics”, arXiv preprint arXiv:1801.04107, 2018.
  • S. Herbold, "Crosspare: a tool for benchmarking cross-project defect predictions", 30th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW), IEEE, 2015.
  • C. Catal, M. Song, C. Muratli, E. H. J. Kim, M. A. Tosuner, Y. Kayikci, “Cross-Cultural Personality Prediction based on Twitter Data”, Journal of Software, 12(11), 2017.
  • J. Hann, M. Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufman Publishers, 2000.
  • T. A. Davis, S. Rajamanickam, W. Sid-Lakhdar, “A survey of direct methods for sparse linear systems”, Acta Numerica, 25, 2016.
  • T. Menzies, R. Krishna, D. Pryor, “The Promise Repository of Empirical Software Engineering Data”, http://openscience.us/repo. North Carolina State University, Department of Computer Science, 2016.

Cosine Similarity-based Cross-project Defect Prediction

Yıl 2019, Cilt: 12 Sayı: 3, 159 - 167, 31.07.2019
https://doi.org/10.17671/gazibtd.453436

Öz

Cross-project
defect prediction has been intriguing researchers in terms of metric
heterogeneity and new methods are needed in this field. Performing defect
prediction through different projects presents valuable information for
developers. In this work, a metric matching algorithm namely CSCDP is presented
for cross-project defect prediction. The method is then tested on 36 different
projects via three classifiers. According to the obtained results, neural
network predictor outperforms the others in terms of mean prediction values. Further,
selecting training data sets using sparsity analysis creates a favorable effect
on testing performance. Last, CSCDP was able to reduce classification error up
to 0.65 in Random Forest for F-score.

Kaynakça

  • J. Nam, W. Fu, S. Kim, T. Menzies, T., L. Tan, “Heterogeneous defect prediction”, IEEE Transactions on Software Engineering,(1), 2017.
  • S. Wang, L. Taiyue, L. Tan. "Automatically learning semantic features for defect prediction." Software Engineering (ICSE), 2016 IEEE/ACM 38th International Conference , 2016.
  • S. Herbold, "Training data selection for cross-project defect prediction", Proceedings of the 9th International Conference on Predictive Models in Software Engineering, ACM, 6, 2013.
  • Y. Zhang, X. Lo, J. Sun, “An empirical study of classifier combination for cross-project defect prediction”, Computer Software and Applications Conference (COMPSAC), 2, 264-269, 2015.
  • C. Ni, "A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction", Journal of Computer Science and Technology, 32(6), 2017.
  • Q. Yu, J. Shujuan, Y. Zhang, "A feature matching and transfer approach for cross-company defect prediction", Journal of Systems and Software, 132, 2017.
  • Q. Yu, S. Jiang, J. Qian, “Which is more important for cross-project defect prediction: instance or feature?”, Software Analysis, Testing and Evolution (SATE), International Conference, 90-95, 2016.
  • Y. Zhou, Y. Yang, H. Lu, L. Chen, L., Y., Zhao, “How Far We Have Progressed in the Journey?, An Examination of Cross-Project Defect Prediction”, ACM Transactions on Software Engineering and Methodology (TOSEM), 27(1), 1, 2018.
  • X. Xia, L. O. David, S. J. Pan, N. Nagappan, X.Wang, “Hydra: Massively compositional model for cross-project defect prediction”, IEEE Transactions on Software Engineering, 42(10), 2016.
  • H. V. Nguyen, L. Bai, L., “Cosine similarity metric learning for face verification”, Asian conference on computer vision, 709-720). 2010.
  • T. Zimmermann, N. Nagappan, H. Gall, E. Giger, B. Murphy, “Cross-project defect prediction: a large scale experiment on data vs. domain vs. process”, Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, 91-100, 2009.
  • F. Zhang, Q. Zheng, Y. Zou, A. E. Hassan, “Cross-project defect prediction using a connectivity-based unsupervised classifier”, Proceedings of the 38th International Conference on Software Engineering, 309-320, 2016.
  • D. Ryu, J. I. Jang, J. Baik, “A transfer cost-sensitive boosting approach for cross-project defect prediction”, Software Quality Journal, 25(1), 2017.
  • W. N Poon, K. E. Bennin, J. Huang, P. Phannachitta, J. W. Keung, “Cross-project defect prediction using a credibility theory based naive bayes classifier”, Software Quality, Reliability and Security (QRS), 2017 IEEE International Conference, 434-441.
  • N. Limsettho, K. E. Bennin, J. W. Keung, H. Hata, H., & K. Matsumoto, “Cross project defect prediction using class distribution estimation and oversampling”, Information and Software Technology, 100, 2018.
  • S. Herbold, A. Trautsch, J. Grabowski, “A comparative study to benchmark cross-project defect prediction approaches”, Proceedings of the 40th International Conference on Software Engineering, 1063-1063, 2017.
  • F. Wu, X. Y. Jing, X. Dong, J., “Cross-project and within-project semi-supervised software defect prediction problems study using a unified solution”, In Software Engineering Companion (ICSE-C), 2017 IEEE/ACM 39th International Conference, 195-197.
  • X. Jing, F. Wu, X. Dong, F. Qi, B. Xu, “Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning”, Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, 496-507, 2015.
  • S. Herbold, “Benchmarking cross-project defect prediction approaches with costs metrics”, arXiv preprint arXiv:1801.04107, 2018.
  • S. Herbold, "Crosspare: a tool for benchmarking cross-project defect predictions", 30th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW), IEEE, 2015.
  • C. Catal, M. Song, C. Muratli, E. H. J. Kim, M. A. Tosuner, Y. Kayikci, “Cross-Cultural Personality Prediction based on Twitter Data”, Journal of Software, 12(11), 2017.
  • J. Hann, M. Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufman Publishers, 2000.
  • T. A. Davis, S. Rajamanickam, W. Sid-Lakhdar, “A survey of direct methods for sparse linear systems”, Acta Numerica, 25, 2016.
  • T. Menzies, R. Krishna, D. Pryor, “The Promise Repository of Empirical Software Engineering Data”, http://openscience.us/repo. North Carolina State University, Department of Computer Science, 2016.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Muhammed Maruf Öztürk

Yayımlanma Tarihi 31 Temmuz 2019
Gönderilme Tarihi 14 Ağustos 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 12 Sayı: 3

Kaynak Göster

APA Öztürk, M. M. (2019). Kosinüs Benzerliğine Dayalı Çapraz-proje Hata Tahmini. Bilişim Teknolojileri Dergisi, 12(3), 159-167. https://doi.org/10.17671/gazibtd.453436