Kyawt San Kyawt, Hironori Washizaki, Yoshiaki Fukazawa, Kiyoshi Honda, Masahiro Taga and Akira Matsuzaki, “Deep Cross-Project Software Reliability Growth Model,” The 30th IEEE International Symposium on Software Reliability Engineering (ISSRE 2019)(CORE Rank A), Industry Track, Berlin, Germany, Oct 28 – 31, 2019

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 shortterm 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 SRGM for ongoing projects.