Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering
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 pha...
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MDPI AG
2021
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oai:doaj.org-article:3b8cfe7004ee41788bbe8bc1602e1a4d2021-11-25T18:17:23ZDeep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering10.3390/math92229452227-7390https://doaj.org/article/3b8cfe7004ee41788bbe8bc1602e1a4d2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2945https://doaj.org/toc/2227-7390Software 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.Kyawt Kyawt SanHironori WashizakiYoshiaki FukazawaKiyoshi HondaMasahiro TagaAkira MatsuzakiMDPI AGarticlesoftware reliabilitydeep learninglong short-term memoryproject similarity and clusteringcross-project predictionMathematicsQA1-939ENMathematics, Vol 9, Iss 2945, p 2945 (2021) |
| institution |
DOAJ |
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DOAJ |
| language |
EN |
| topic |
software reliability deep learning long short-term memory project similarity and clustering cross-project prediction Mathematics QA1-939 |
| spellingShingle |
software reliability deep learning long short-term memory project similarity and clustering cross-project prediction Mathematics QA1-939 Kyawt Kyawt San Hironori Washizaki Yoshiaki Fukazawa Kiyoshi Honda Masahiro Taga Akira Matsuzaki Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering |
| description |
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. |
| format |
article |
| author |
Kyawt Kyawt San Hironori Washizaki Yoshiaki Fukazawa Kiyoshi Honda Masahiro Taga Akira Matsuzaki |
| author_facet |
Kyawt Kyawt San Hironori Washizaki Yoshiaki Fukazawa Kiyoshi Honda Masahiro Taga Akira Matsuzaki |
| author_sort |
Kyawt Kyawt San |
| title |
Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering |
| title_short |
Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering |
| title_full |
Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering |
| title_fullStr |
Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering |
| title_full_unstemmed |
Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering |
| title_sort |
deep cross-project software reliability growth model using project similarity-based clustering |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/3b8cfe7004ee41788bbe8bc1602e1a4d |
| work_keys_str_mv |
AT kyawtkyawtsan deepcrossprojectsoftwarereliabilitygrowthmodelusingprojectsimilaritybasedclustering AT hironoriwashizaki deepcrossprojectsoftwarereliabilitygrowthmodelusingprojectsimilaritybasedclustering AT yoshiakifukazawa deepcrossprojectsoftwarereliabilitygrowthmodelusingprojectsimilaritybasedclustering AT kiyoshihonda deepcrossprojectsoftwarereliabilitygrowthmodelusingprojectsimilaritybasedclustering AT masahirotaga deepcrossprojectsoftwarereliabilitygrowthmodelusingprojectsimilaritybasedclustering AT akiramatsuzaki deepcrossprojectsoftwarereliabilitygrowthmodelusingprojectsimilaritybasedclustering |
| _version_ |
1718411373223870464 |