کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
495598 862831 2013 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hybrid intelligent systems for predicting software reliability
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Hybrid intelligent systems for predicting software reliability
چکیده انگلیسی

In this paper, we propose novel recurrent architectures for Genetic Programming (GP) and Group Method of Data Handling (GMDH) to predict software reliability. The effectiveness of the models is compared with that of well-known machine learning techniques viz. Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS), Backpropagation Neural Network (BPNN), Counter Propagation Neural Network (CPNN), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), TreeNet, GMDH and GP on three datasets taken from literature. Further, we extended our research by developing GP and GMDH based ensemble models to predict software reliability. In the ensemble models, we considered GP and GMDH as constituent models and chose GP, GMDH, BPNN and Average as arbitrators. The results obtained from our experiments indicate that the new recurrent architecture for GP and the ensemble based on GP outperformed all other techniques.

Figure optionsDownload as PowerPoint slideHighlights
► In this paper, we propose novel recurrent architectures for Genetic Programming (GP) and Group Method of Data Handling (GMDH) to predict software reliability.
► Further, we extended our research by developing GP and GMDH based ensemble models, where GP, GMDH, BPNN and Average are taken as arbitrators, to predict software reliability.
► The results obtained from our experiments indicate that the new recurrent architecture for GP and the ensemble based on GP outperformed all other techniques.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 13, Issue 1, January 2013, Pages 189–200
نویسندگان
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