An Improved J-M Software Reliability Growth Model

2012 ◽  
Vol 241-244 ◽  
pp. 2741-2750
Author(s):  
Yan Zhao Liu ◽  
Xun Luo ◽  
Jian Xun Ao ◽  
Kai Xue ◽  
Ping Luo
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Forecast Accuracy ◽  
Prediction Method ◽  
Reliability Growth ◽  
Reliability Model ◽  
Software Faults ◽  
Software Reliability Model ◽  
Software Reliability Growth ◽  
Improved Model

Reliability is an important software trustworthy attribute. But most existent reliability model don’t consider the severity degree of software fault, so traditional software reliability model can’t reflect the trustworthiness of software. Based on the analysis of typical J-M reliability growth model, this paper modifies some of the model assumptions and classifies the software faults. Besides, this paper presents a new method based on weight to calculate the degree of software reliability. Finally, according to the different of frequency of software faults, a practical reliability prediction method is proposed and the experiment results shows that the improved model has a better forecast accuracy.

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.

Software Shipping Time Estimation for Markovian Change-Point Reliability Model

2020 ◽  
pp. 2150021
Author(s):  
Shinji Inoue ◽  
Shigeru Yamada
Keyword(s):  
Software Reliability ◽  
Change Point ◽  
Growth Process ◽  
Time Estimation ◽  
Reliability Growth ◽  
Reliability Model ◽  
Software Reliability Model ◽  
Software Reliability Growth ◽  
Shipping Policy ◽  
Software Development Management

A Markovian software reliability model is known as one of the useful models which enable us to describe the details on the dynamic behavior of software reliability growth process with imperfect debugging activities. As the application of this model for software development management, we discuss software shipping time estimation based on the extended Markovian software reliability model considering with the effect of change-point, which is the testing-time when the characteristic of the software failure-occurrence or fault-detection phenomenon changes notably. Especially in our discussion on the software shipping time estimation, we derive an analytical software shipping policy under certain software reliability objective and show numerical examples of the application of the software shipping policy by using actual software reliability data. Finally, we give some useful consideration on the relationship between the software shipping time and the software reliability growth process for conducing quality-oriented software development management.

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.

Sign in / Sign up

Export Citation Format

Share Document