Deep cross-project software reliability growth model using project similarity-based clustering

Kyawt Kyawt San, Hironori Washizaki*, Yoshiaki Fukazawa, Kiyoshi Honda, Masahiro Taga, Akira Matsuzaki

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number2945
JournalMathematics
Volume9
Issue number22
DOIs
Publication statusPublished - 2021 Nov 1

Keywords

  • Cross-project prediction
  • Deep learning
  • Long short-term memory
  • Project similarity and clustering
  • Software reliability

ASJC Scopus subject areas

  • Mathematics(all)

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