Software maintenance severity prediction with soft computing approach
Küçük Resim Yok
Tarih
2009
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA's public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software. © 2009 WASET.ORG.
Açıklama
Anahtar Kelimeler
Accuracy; Fuzzy; Mae; Neuro-Fuzzy; Rmse; Software Faults; Software Metrics, Accuracy; Fuzzy; Mae; Neuro-Fuzzy; Rmse; Software Faults; Software Metrics; Computer Programming; Computer Software; Fuzzy Inference; Learning Algorithms; Learning Systems; Nasa; Soft Computing; Computer Software Maintenance
Kaynak
World Academy of Science, Engineering and Technology
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
38
