Severity Of Bug Reports Research Articles

BERT based severity prediction of bug reports for the maintenance of mobile applications

Mobile application maintenance is crucial to ensuring the accurate operation and continuous improvement of mobile applications (mobile apps). To effectively address issues and enhance the user experience, developers utilize issue-tracking systems that gather bug reports to refine mobile apps. Users can submit bugs through these systems, allowing them to determine the severity of each reported issue. The severity level plays a pivotal role in prioritizing bug resolution, enabling developers to address critical bugs promptly. Nonetheless, manually assessing the severity of each issue can be laborious and prone to errors. To overcome this challenge, this paper presents Bidirectional Encoder Representations from Transformers (BERT) based severity prediction of bug reports (called BERT-SBR) that leverages a deep neural network for automatic bug severity classification for mobile app maintenance. We collect the publicly available mobile apps bug reports dataset from the Hugging Face. BERT-SBR first computes the sentiment of reporters of bug reports and preprocesses them by leveraging BertTokenizer input formatting techniques. Next, it passes the formatted text and computed sentiment of each bug report to generate word embeddings. Then, it introduces a fine-tuned BERT classifier for bug report severity prediction. After that, it passes the generated word embeddings to the fine-tuned BERT classifier for training and testing. Finally, the proposed classifier’s performance is evaluated. The BERT-SBR assessment results confirm that the fine-tuned BERT classifies bug reports significantly more effectively than other deep learning classifiers. On average, BERT-SBR achieves a remarkable improvement of 40.43%, 67.78%, 40.71%, and 58.14% in the accuracy, precision, recall, and f-measure. This indicates its superiority in accurately predicting the severity of bug reports for mobile application maintenance.

Identifying High-impact Bug Reports with Imbalance Distribution by Instance Fuzzy Entropy

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.

Severity Prediction for Bug Reports Using Multi-Aspect Features: A Deep Learning Approach

The severity of software bug reports plays an important role in maintaining software quality. Many approaches have been proposed to predict the severity of bug reports using textual information. In this research, we propose a deep learning framework called MASP that uses convolutional neural networks (CNN) and the content-aspect, sentiment-aspect, quality-aspect, and reporter-aspect features of bug reports to improve prediction performance. We have performed experiments on datasets collected from Eclipse and Mozilla. The results show that the MASP model outperforms the state-of-the-art CNN model in terms of average Accuracy, Precision, Recall, F1-measure, and the Matthews Correlation Coefficient (MCC) by 1.83%, 0.46%, 3.23%, 1.72%, and 6.61%, respectively.

Bug severity prediction using question-and-answer pairs from Stack Overflow

Nowadays, bugs have been common in most software systems. For large-scale software projects, developers usually conduct software maintenance tasks by utilizing software artifacts (e.g., bug reports). The severity of bug reports describes the impact of the bugs and determines how quickly it needs to be fixed. Bug triagers often pay close attention to some features such as severity to determine the importance of bug reports and assign them to the correct developers. However, a large number of bug reports submitted every day increase the workload of developers who have to spend more time on fixing bugs. In this paper, we collect question-and-answer pairs from Stack Overflow and use logical regression to predict the severity of bug reports. In detail, we extract all the posts related to bug repositories from Stack Overflow and combine them with bug reports to obtain enhanced versions of bug reports. We achieve severity prediction on three popular open source projects (e,g., Mozilla, Ecplise, and GCC) with Naïve Bayesian, k-Nearest Neighbor algorithm (KNN), and Long Short-Term Memory (LSTM). The results of our experiments show that our model is more accurate than the previous studies for predicting the severity. Our approach improves by 23.03%, 21.86%, and 20.59% of the average F-measure for Mozilla, Eclipse, and GCC by comparing with the Naïve Bayesian based approach which performs the best among all baseline approaches.

An uncertainty based incremental learning for identifying the severity of bug report

To ensure the reliability of software system, software developers have to keep track of the severity of bug reports, and fix critical bugs as soon as possible. Recently, automatic methods to identify the severity of bug reports have emerged as a promising tool to lessen the work burden of software developers. However, most of such methods are supervised and data-driven models which fail to provide favorable performance in the presence of insufficient labeled sample or limited training data. In order to tackle with these issues, we propose an incremental learning for bug reports recognition. According to this framework of incremental learning, one active learning method is developed for tagging unlabeled bug reports, meanwhile, a sample augmentation method is utilized for sufficient training data. Both of these methods are based on uncertainty which is correlated to the informativeness and the classification risk of samples. Moreover, different types of connectionist models are employed to identify bug reports, and comprehensive experiments on real bug report datasets demonstrate that the generalization abilities of these models can be improved by this proposed incremental learning.

Identify Severity Bug Report with Distribution Imbalance by CR-SMOTE and ELM

Manually inspecting bugs to determine their severity is often an enormous but essential software development task, especially when many participants generate a large number of bug reports in a crowdsourced software testing context. Therefore, boosting the capabilities of methods of predicting bug report severity is critically important for determining the priority of fixing bugs. However, typical classification techniques may be adversely affected when the severity distribution of the bug reports is imbalanced, leading to performance degradation in a crowdsourcing environment. In this study, we propose an enhanced oversampling approach called CR-SMOTE to enhance the classification of bug reports with a realistically imbalanced severity distribution. The main idea is to interpolate new instances into the minority category that are near the center of existing samples in that category. Then, we use an extreme learning machine (ELM) — a feedforward neural network with a single layer of hidden nodes — to predict the bug severity. Several experiments were conducted on three datasets from real bug repositories, and the results statistically indicate that the presented approach is robust against real data imbalance when predicting the severity of bug reports. The average accuracies achieved by the ELM in predicting the severity of Eclipse, Mozilla, and GNOME bug reports were 0.780, 0.871, and 0.861, which are higher than those of classifiers by 4.36%, 6.73%, and 2.71%, respectively.

Predicting the Severity of Open Source Bug Reports Using Unsupervised and Supervised Techniques

The severity of the bug report helps for the bug triagers to prioritize the handling of bug reports for giving more importance to high critical bugs than less critical bugs, since the inexperienced developers and new users can make mistakes while assigning the severity. The manual labeling of severity is labor-intensive and time-consuming. In this article, both unsupervised and supervised learning algorithms are used to automate the prediction of bug report severity. Because the data was unlabeled, the Gaussian Mixture Model is used to group similar kinds of bug reports. The result is labeled data with the severity level given for each bug reports. Then, the training of classifiers is performed to predict the severity of new bug reports submitted by the user using Multinomial Naïve Bayes Classifier, Logistic Regression Classifier and Stochastic Gradient Descent Classifier. Using these methods, around 85% accuracy is obtained. More accurate predictions can be done using the authors approach.

Predicting the Severity of Bug Reports Based on Feature Selection

In software maintenance process, it is a fairly important activity to predict the severity of bug reports. However, manually identifying the severity of bug reports is a tedious and time-consuming task. So developing automatic judgment methods for predicting the severity of bug reports has become an urgent demand. In general, a bug report contains a lot of descriptive natural language texts, thus resulting in a high-dimensional feature set which poses serious challenges to traditionally automatic methods. Therefore, we attempt to use automatic feature selection methods to improve the performance of the severity prediction of bug reports. In this paper, we introduce a ranking-based strategy to improve existing feature selection algorithms and propose an ensemble feature selection algorithm by combining existing ones. In order to verify the performance of our method, we run experiments over the bug reports of Eclipse and Mozilla and conduct comparisons with eight commonly used feature selection methods. The experiment results show that the ranking-based strategy can effectively improve the performance of the severity prediction of bug reports by up to 54.76% on average in terms of [Formula: see text]-measure, and it also can significantly reduce the dimension of the feature set. Meanwhile, the ensemble feature selection method can get better results than a single feature selection algorithm.

Ensemble Data Reduction Techniques and Multi-RSMOTE via Fuzzy Integral for Bug Report Classification

Due to the unavoidable bugs appearing in the most of the software systems, bug resolution has become one of the most important activities in software maintenance. To decrease the time cost in manual work, text classification techniques are applied to automatically identify severity of bug reports. In this paper, we address the problem of low-quality and class imbalance for identifying the severity of bug reports. First, we combine feature selection with instance selection to simultaneously reduce the bug report dimension and the word dimension, which could get small-scale and high-quality reduced data set. Then, an improve random oversampling technique, named, RSMOTE, which is presented to weaken the imbalancedness degree of class distribution. Finally, to avoid the random over-sampling uncertainty of RSMOTE, we develop an ensemble learning algorithm, which is based on Choquet fuzzy integral, to combine multiple RSMOTE. We empirically investigate the performance of data reduction on ten data sets of three large open source projects, namely, Eclipse, Mozilla, and GNOME. The results show that our approach can effectively reduce the data scale and improve the performance of identifying the severity of bug reports.