Case Study: Project Management Using Cross Project Software Reliability Growth Model Considering System Scale

2016 ◽  
Author(s):  
Kiyoshi Honda ◽  
Nobuhiro Nakamura ◽  
Hironori Washizaki ◽  
Yoshiaki Fukazawa
Keyword(s):  
Project Management ◽  
Growth Model ◽  
Software Reliability ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Software Reliability Growth ◽  
Cross Project

scholarly journals Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2945
Author(s):  
Kyawt Kyawt San ◽  
Hironori Washizaki ◽  
Yoshiaki Fukazawa ◽  
Kiyoshi Honda ◽  
Masahiro Taga ◽  
...  
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Prediction Method ◽  
Development Project ◽  
Training Data ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Initial Development ◽  
Software Reliability Growth ◽  
Cross Project

Software reliability is an essential characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein, we propose a new software reliability modeling method called a deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for the training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

scholarly journals Deep Cross-Project Software Reliability Growth Model using Project Similarity Based Clustering

2021 ◽  
Author(s):  
Kyawt Kyawt San ◽  
Hironori Washizaki ◽  
Yoshiaki Fukazawa ◽  
Kiyoshi Honda ◽  
Masahiro Taga ◽  
...  
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Prediction Method ◽  
Development Project ◽  
Training Data ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Initial Development ◽  
Software Reliability Growth ◽  
Cross Project

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

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