Dc-srgm: Deep cross-project software reliability growth model

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Previous studies have suggested that software reliability growth models (SRGMs) for cross-project predictions are more practical for ongoing development projects. Several software reliability growth models (SRGMs) have been proposed based on various factors to measure the reliability and are helpful to indicate the number of remaining defects before release. Software industries want to predict the number of bugs and monitor the situation of projects for new or ongoing development projects. However, the available data is limited for projects in the initial development phases. In this situation, applying SRGMs may incorrectly predict the future number of bugs. This paper proposes a new SRGM method using the features of previous projects to predict the number of bugs for ongoing development projects. Through a case study, we identify similar projects for a target project by k-means clustering and form new training datasets. The Recurrent Neural Network based deep long short-Term memory model is built over the obtained new dataset for prediction model. According to experiment results, the prediction by the proposed deep cross-project (DC) SRGM performs better than traditional SRGMs and deep SRGMs for ongoing projects.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 30th International Symposium on Software Reliability Engineering Workshops, ISSREW 2019
EditorsKatinka Wolter, Ina Schieferdecker, Barbara Gallina, Michel Cukier, Roberto Natella, Naghmeh Ivaki, Nuno Laranjeiro
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages61-66
Number of pages6
ISBN (Electronic)9781728151380
DOIs
Publication statusPublished - 2019 Oct
Event30th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2019 - Berlin, Germany
Duration: 2019 Oct 282019 Oct 31

Publication series

NameProceedings - 2019 IEEE 30th International Symposium on Software Reliability Engineering Workshops, ISSREW 2019

Conference

Conference30th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2019
CountryGermany
CityBerlin
Period19/10/2819/10/31

Keywords

  • Deep Cross-project
  • k-means Clustering
  • Long Short-Term Memory
  • Software Reliability Growth Model

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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  • Cite this

    San, K. K., Washizaki, H., Fukazawa, Y., Honda, K., Taga, M., & Matsuzaki, A. (2019). Dc-srgm: Deep cross-project software reliability growth model. In K. Wolter, I. Schieferdecker, B. Gallina, M. Cukier, R. Natella, N. Ivaki, & N. Laranjeiro (Eds.), Proceedings - 2019 IEEE 30th International Symposium on Software Reliability Engineering Workshops, ISSREW 2019 (pp. 61-66). [8990200] (Proceedings - 2019 IEEE 30th International Symposium on Software Reliability Engineering Workshops, ISSREW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSREW.2019.00044