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Furthermore, traditional defect detection is performed on the entire project, and the detection efficiency is relatively low, especially on large-scale software projects. To this end, we propose<jats:sc>BugPre<\/jats:sc>, a CVDP approach to address these two issues.<jats:sc>BugPre<\/jats:sc>is a novel framework that only conducts efficient defect prediction on changed modules in the current version.<jats:sc>BugPre<\/jats:sc>utilizes variable propagation tree-based associated analysis method to obtain the changed modules in the current version. Besides,<jats:sc>BugPre<\/jats:sc>constructs graph leveraging code context dependences and uses a graph convolutional neural network to learn representative characteristics of code, thereby improving defect prediction capability when version changes occur. Through extensive experiments on open-source Apache projects, the experimental results indicate that our<jats:sc>BugPre<\/jats:sc>outperforms three state-of-the-art defect detection approaches, and the F1-score has increased by higher than 16%.<\/jats:p>","DOI":"10.1007\/s40747-022-00848-w","type":"journal-article","created":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T06:04:31Z","timestamp":1661580271000},"page":"3835-3855","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5402-1777","authenticated-orcid":false,"given":"Zixu","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9907-5859","authenticated-orcid":false,"given":"Weiyuan","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guixin","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiaoqing","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Zhanyong","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"issue":"1","key":"848_CR1","first-page":"1","volume":"1","author":"RS Wahono","year":"2015","unstructured":"Wahono RS (2015) A systematic literature review of software defect prediction. 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