Abstract The rapid development of Open‐Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code changes. Researchers have investigated how to learn meaningful code changes to assist developers in anticipating changes that code reviewers may suggest for the submitted code. However, there are two main limitations to be addressed, including the limitation of long‐range dependencies of the source code and the missing syntactic structural information of the source code. To solve these limitations, a novel method is proposed, named Graph Transformer for learning meaningful Code Transformations (GTCT), to provide developers with preliminary and quick feedback when developers submit code changes, which can improve the quality of code changes and improve the efficiency of code review. GTCT comprises two components: code graph embedding and code transformation learning. To address the missing syntactic structural information of the source code limitation, the code graph embedding component captures the types and patterns of code changes by encoding the source code into a code graph structure from the lexical and syntactic representations of the source code. Subsequently, the code transformation learning component uses the multi‐head attention mechanism and positional encoding mechanism to address the long‐range dependencies limitation. Extensive experiments are conducted to evaluate the performance of GTCT by both quantitative and qualitative analyses. For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. Meanwhile, the qualitative analysis shows that each type of code change by GTCT outperforms that of the baseline method in terms of bug fixed, refactoring code and others' taxonomy of code changes.

Bug tracking systems, such as Bugzilla, contain bug reports collected from sources such as development teams, testing teams and end users. Developers often depend on bug reports to fix identified bugs. Frequently used bug reports are the so-called severe bug reports. Although severe bug reports can be manually detected within bug reports in bug tracking systems, they impose heavy burdens on management of bug tracking systems. Consequently, an automated mechanism to examine the severity of bug reports is desirable to augment productivity. Unfortunately, identifying the severity of bug reports from thousands of bug reports in a bug tracking system is not an easy feat, because of the problem of low-quality and imbalance distributions that could affect the performance of automated mechanisms. In this paper, we propose an approach, namely FER, to counter low-quality and imbalanced distributions of bug reports relative to their severity. First, FER approach gets high-quality bug reports based on instance fuzzy entropy. Then, FER approach weakens the imbalancedness degree of class distribution according to the high-quality bug reports to train classifiers to recognize the severity of bug reports. Several experiments are conducted on bug reports from three open source projects (Eclipse, Mozilla, GNOME) and they reveal that our approach is robust against the low-quality and imbalance distributions of bug reports, while identifying the severity of bug reports.