Abstract Currently, software projects require a significant amount of time, effort and other resources to be invested in software testing to reduce the number of code defects. However, this process decreases the efficiency of software development and leads to a significant waste of workforce and resources. To address this challenge, researchers developed various solutions utilising deep neural networks. However, these solutions are frequently challenged by issues, such as a vast vocabulary, network training difficulties and elongated training processes resulting from the handling of redundant information. To overcome these limitations, the authors proposed a new neural network‐based model named HopFix, designed to detect software defects that may be introduced during the coding process. HopFix consists of four parts: data preprocessing, encoder, decoder and code generation components, which were used for preprocessing data, extracting information about software defects, analysing defect information, generating software patches and controlling the generation process of software patches, respectively. Experimental studies on Bug‐Fix Pairs (BFP) show that HopFix correctly fixed 47.2% ( BFP small datasets) and 25.7% ( BFP medium datasets) of software defects.

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.