Bug Severity Research Articles

A Machine Learning Approach for Classifying the Default Bug Severity Level

A Machine Learning Approach for Classifying the Default Bug Severity Level

Severity-Oriented Multi-Objective Crowdsourced Test Reports Prioritization

Crowdsourced testing for mobile applications is widespread. Efficiently and swiftly inspecting enormous test reports is a crucial challenge in crowdsourced testing. Most existing methods focus on finding different bugs earlier, i.e. the diversity of bugs, while ignoring bug severity. We present a multi-objective prioritization method for crowdsourced test reports that takes into account the diversity and severity of bugs. In the beginning, we ranked the test reports according to severity, then clustered the test reports by fusing textual and image features, and finally used the clustering results to adjust the initial ranking to ensure diversity. To validate our method, we conducted experiments using an industrial crowdsourced test report dataset comprised of six mobile application projects. The results demonstrate that our method can find more different high-severity bugs earlier than existing methods.

NATURE-BASED PREDICTION MODEL OF BUG REPORTS BASED ON ENSEMBLE MACHINE LEARNING MODEL

The rapid growth of software systems has led to an increase in the volume and complexity of bug reports, necessitating efficient and accurate prediction models to manage and address these reports effectively. This paper presents a Nature-Based Prediction Model of Bug Reports (NBPMBR) leveraging ensemble machine learning techniques to enhance the prediction accuracy and reliability of bug report classifications. By integrating various nature-inspired algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) within an ensemble framework, NBPMBR combines the strengths of these algorithms to improve performance.The model undergoes rigorous training on historical bug report data, using feature extraction methods to capture relevant information such as bug severity, priority, and textual descriptions. The ensemble approach ensures robustness by mitigating the weaknesses of individual algorithms, leading to a more balanced and accurate prediction outcome. Experimental results on benchmark datasets demonstrate that NBPMBR outperforms traditional machine learning models in terms of precision, recall, and overall prediction accuracyThis nature-based ensemble model not only advances the state-of-the-art in bug report prediction but also provides a scalable and adaptable solution for real-world software maintenance and quality assurance processes. By automating the classification and prioritization of bug reports, NBPMBR aids in the efficient allocation of resources, thereby improving the software development lifecycle and enhancing the overall quality of software products.

NATURE-BASED PREDICTION MODEL OF BUG REPORTS BASED ON ENSEMBLE MACHINE LEARNING MODEL

The rapid growth of software systems has led to an increase in the volume and complexity of bug reports, necessitating efficient and accurate prediction models to manage and address these reports effectively. This paper presents a Nature-Based Prediction Model of Bug Reports (NBPMBR) leveraging ensemble machine learning techniques to enhance the prediction accuracy and reliability of bug report classifications. By integrating various nature-inspired algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) within an ensemble framework, NBPMBR combines the strengths of these algorithms to improve performance.The model undergoes rigorous training on historical bug report data, using feature extraction methods to capture relevant information such as bug severity, priority, and textual descriptions. The ensemble approach ensures robustness by mitigating the weaknesses of individual algorithms, leading to a more balanced and accurate prediction outcome. Experimental results on benchmark datasets demonstrate that NBPMBR outperforms traditional machine learning models in terms of precision, recall, and overall prediction accuracyThis nature-based ensemble model not only advances the state-of-the-art in bug report prediction but also provides a scalable and adaptable solution for real-world software maintenance and quality assurance processes. By automating the classification and prioritization of bug reports, NBPMBR aids in the efficient allocation of resources, thereby improving the software development lifecycle and enhancing the overall quality of software products.

버그 심각도 예측에 버그 리포트의 다중 토픽 분석 및 CNN 알고리즘 적용

버그 심각도 예측에 버그 리포트의 다중 토픽 분석 및 CNN 알고리즘 적용

Software bug severity and priority prediction using SMOTE and intuitionistic fuzzy similarity measure

A software bug tracking system receives several bug reports in a rapid manner during the maintenance of software. In order to fix the important and urgent bugs, the triager has to assign severity and priority to individual bugs on time. However, there are a lot of uncertainties in the bug reports due to bias, noise, and abnormal data. At the same time, the presence of common terms in multiple severity and priority classes creates confusion in the mind of the triager. Furthermore, machine learning and deep learning approaches generally belong to discriminative learning with a clear-cut outcome. Instances of software bug reports are textual in nature. As a result, these are fuzzy and cannot be classified with a clear-cut outcome. To overcome the above problems, in this paper, an Intuitionistic Fuzzy Similarity Measure (IFSM) based severity prediction technique (IFSMSP) and priority prediction technique (IFSMPP) are proposed for predicting the severity and priority of a new bug by using already labeled bugs. Initially, the Synthetic Minority Oversampling Technique (SMOTE) is used to balance the severity and priority label of software bugs. Then the severity-term dictionary or priority-term dictionary is created by extracting the most frequent terms from the bug summary using text mining and Natural Language Processing (NLP). Then the data is represented using an intuitionistic fuzzy set (IFS) by calculating the membership, non-membership, and hesitancy degrees. Then 15 different IFSM techniques are investigated for predicting the severity and priority of software bugs. Experiments are carried out on large software bug repositories (Eclipse, Mozilla, Apache, and NetBeans) with a 10-fold cross-validation technique. IFSMSP outperformed other state-of-the-art priority models by obtaining an accuracy of 92.3%, 90.6%, 91.9%, and 91.2%, and IFSMPP outperformed other state-of-the-art models by obtaining an accuracy of 93.2%, 91.9%, 92.7%, and 92.3% on the Eclipse, Mozilla, Apache, and NetBeans software bug repositories, respectively.

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.

Enhanced Approach for Bug Severity Prediction: Experimentation and Scope for Improvements

Software development is an iterative process, where developers create, test, and refine their code until it is ready for release. Along the way, bugs and issues are inevitable. A bug can be any error identified in requirement specification, design or implementation of any project. These bugs need to be categorized and assigned to developers to be resolved. the number of bugs generated in any large scale project are vast in number. These bugs can have significant or no impact on the project depending on the type of bug. The aim of this study is to develop a deep learning-based bug severity prediction model that can accurately predict the severity levels of software bugs. This study aims to address the limitations of the current manual bug severity assessment process and provide an automated solution using various classifiers e.g. Naïve Bayes, Logistic regression, KNN and Support vector machine along with Mutual information as feature selection method, that can assist software development teams in giving severity code to bugs effectively. It seeks to improve the overall software development process by reducing the time and effort required for bug resolution and enhancing the quality and reliability of software.

Evaluation of Stratified K-Fold Cross Validation for Predicting Bug Severity in Game Review Classification

Steam review data provided a lot of information for the game development team, either positive or negative review. It contained an essential point that negative and positive reviews provide crucial information and 7% of positive reviews contained bug reports. These bug reports occurred after the game was released, and many reports of common incidents still exist. If players found an issue in the game, they could report it directly through the review feature provided by the online game platform. Nevertheless, the development team took a long time manually analyzing and categorizing the game review. This study proposed a new approach to automatically categorizing game reviews on Steam based on the bug severity level. Hence, to solve this problem, we suggested introducing a solution based on the research background indicated above. For this experiment, we analyzed reviews on two popular game titles namely, FIFA 23 and Apex Legends. We implemented three different classifiers namely, KNN, Decision Tree, and Naïve Bayes, which would be used to train a dataset to classify the bug severity level. Due to an imbalanced dataset, we performed cross-validation to reduce bias in the dataset. Performance in this model would be evaluated using accuracy rate, precision, recall, and F1 score. As a result, the experiment showed that game reviews of different game titles achieved different accuracy scores. The game review classification for FIFA 23 performed better than the game review classification for Apex Legends. The mean accuracy score of FIFA 23 was 72% with Decision Tree and Apex Legend was 64% with KNN.

Bug severity classification in software using ant colony optimization based feature weighting technique

At the present, the delivery of the software should be time-bound without affecting the quality of the software. However, bug severity can affect the timely delivery of software. It is a crucial component of the software engineering, including maintenance and testing. Both phases are essential for bug severity classification but require much time. Generally, bug triage is responsible for classifying the bugs based on criticality/severeness. The manual execution of this process is error-prone. Consequently, a model for automatic bug classification is required to help the bug triage. In this work, the ant colony optimization (ACO) based feature extraction technique is proposed to extract more relevant features for bug severity classification. Furthermore, the ACO technique is integrated with NB, SVM, DeepFM and F-SVM techniques for predicting bug severity and classifying bugs into multi-severity classes. Several benchmark projects such as Eclipse, Mozilla, OpenFOAM, JBoss, and Firefox, are considered to evaluate the efficacy of the techniques above. The simulation outcomes are expressed in terms of Accuracy, Precision, Recall, and F1-measure. It is noted that the outcomes of the SVM, NB, DeepFM and F-SVM approaches are improved by the ACO-based feature weighting technique. The accuracy rate of ACO-F-SVM, ACO-NB, ACO-SVM, ACO-DeepFM, NB, SVM, F-SVM, DeepFM techniques are ranging in between 85.73 and 89.38%, 78% to 80%, 73% to 76%, 92.67% to 97.27 %, 71% to 77%,65% to 74%, 78.21% to 81.28% and 90.02% to 95.24% respectively for five benchmark projects. Further, proposed techniques are also produced better simulation results as compared with state-of –the-art techniques. Friedman and post hoc statistical tests are also conducted on proposed techniques.

An intuitionistic fuzzy representation based software bug severity prediction approach for imbalanced severity classes

In order to improve software reliability and quality, the triager must assess the severity of the software bug and allocate suitable resources on time. However, the triager faces many difficulties in understanding various software bugs that involve lots of uncertainty and irregularities. Additionally, it can be challenging for the triager to determine the severity of bugs that are semantically close to multiple severity labels. To address these problems, a topic modeling and intuitionistic fuzzy similarity measure-based software bug severity prediction technique (IFSBSP) is proposed in this paper. Initially, the Synthetic Minority Oversampling Technique (SMOTE) is applied to balance the severity classes in software bug repositories. Then topic modeling is used to generate topics based on the probability of underlying uncertainty in software bugs. Using these topics, the intuitionistic fuzzy membership, non-membership, and hesitancy membership degrees of a software bug are calculated for multiple severity labels. Then, 15 IFS techniques are investigated for a new bug in order to compute its similarity to multiple severity labels. The Eclipse, Mozilla, Apache, and NetBeans software bug repositories are used to evaluate the performance of IFSBSP and the state-of-the-art models. On these software bug repositories, the IFSBSP model outperforms state-of-the-art models by achieving accuracy of 91.6%, 90.9%, 88.1%, and 92.9% and an F-measure of 90.7%, 91.1%, 89.3%, and 91.7%, respectively.

Breaking Down Break-It-Fix-It: An Automated Software Repair Replication

Software quality is strongly correlated with the quantity and severity of bugs. While there are a variety of tools, techniques, and practices to aid production of robust and resilient code, low quality software is draining trillions of dollars from organizations annually. Meanwhile, debugging and fixing coding errors consumes upwards of half of developer labor. To say this situation is untenable is an understatement. Fortunately, automated software repair offers a possible solution. The literature around automated code fixing has been expanding with a variety of implementations ranging from genetic programming, code translation, and various machine learning algorithms. All report positive results, however there has not yet been a dedicated effort to measure to what extent the various implementations are generalizable. Accordingly, we sought to replicate a prominent study in the field in two parts. The first part consisted of replicating the training of the machine learning model using the source study materials. We found training to be impossible at first due to package dependencies and missing package files. However, we were able to replicate the self-repair evaluation. The results were identical to the source study. Later, using a Docker compose file obtained from the original authors, we were able to replicate BIFI model training and again match outcomes. Overall, based on the replication outcomes, we offer future leaning recommendations and ideas for future work.

Improving Bug Severity Prediction With Domain-Specific Representation Learning

Improving Bug Severity Prediction With Domain-Specific Representation Learning

Evaluating the Veracity of Software Bug Reports using Entropy-based Measures

The wide usage of open source software (OSS) results in an increase of bug data forming an integral part of the extensive data ecosystem. This bug report data needs to be analyzed for bug fixing and prediction of various important attributes like bug severity, priority, fix time, assignees, etc. The increased volume of bug data and different bug reporters from different geographical locations make veracity an important concern. We assume that the bug reports (i.e., different bug attributes) reported in software bug repositories are trustworthy during the bug triaging process. In reality, the bug report data are not trustworthy regarding various aspects like integrity, authenticity, and trusted origin as the bugs are reported by users who may or may not have proper knowledge of the software. In this paper, we proposed entropy-based models for veracity estimation of different bug attributes.

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer's-Based IoT System.

Softwares are involved in all aspects of healthcare, such as booking appointments to software systems that are used for treatment and care of patients. Many vendors and consultants develop high quality software healthcare systems such as hospital management systems, medical electronic systems, and middle-ware softwares in medical devices. Internet of Things (IoT) medical devices are gaining attention and facilitate the people with new technology. The health condition of the patients are monitored by the IoT devices using sensors, specifically brain diseases such as Alzheimer, Parkinson's, and Traumatic brain injury. Embedded software is present in IoT medical devices and the complexity of software increases day-by-day with the increase in the number and complexity of bugs in the devices. Bugs present in IoT medical devices can have severe consequences such as inaccurate records, circulatory suffering, and death in some cases along with delay in handling patients. There is a need to predict the impact of bugs (severe or nonsevere), especially in case of IoT medical devices due to their critical nature. This research proposes a hybrid bug severity prediction model using convolution neural network (CNN) and Harris Hawk optimization (HHO) based on an optimized hyperparameter of CNN with HHO. The dataset is created, that consists of the bugs present in healthcare systems and IoT medical devices, which is used for evaluation of the proposed model. A preprocessing technique on textual dataset is applied along with a feature extraction technique for CNN embedding layer. In HHO, we define the hyperparameter values of “Batch Size, Learning Rate, Activation Function, Optimizer Parameters, and Kernel Initializers,” before training the model. Hybrid model CNN-HHO is applied, and a 10-fold cross validation is performed for evaluation. Results indicate an accuracy of 96.21% with the proposed model.

Incidence of invasive mealybugs, Phenacoccus solenopsis Tinsley and Paracoccus marginatus Williams & Granara de Willink (Hemiptera: Pseudococcidae) on Gymnema sylvestre (R. Br) and its natural enemies in the semi-arid region of India

The invasive mealybugs, Phenacoccus solenopsis Tinsley and Paracoccus marginatus Williams & Granara de Willink (Hemiptera: Pseudococcidae) are causing significant damage to Gymnema sylvestre in the semi-arid region. This study highlights the nature of the damage, severity of mealy bugs and natural enemy complex association. The severity of infestation was maximum during 23rd and 22nd Standard Meteorological Weeks (SMW) (3.67 and 3.66) of 2019 and 2020, respectively. The pooled data of both the years revealed that temperature has a significant positive correlation and rainfall has a negative correlation towards the severity of the pest incidence with respective r-values of 0.678** and −0.05. A total of 19 different natural enemies identified belong to 7 families of 4 insect orders. Cheilomenes sexmaculata (Fabricius) was the most common predator and among the parasitoids, three species of primary parasitoids viz., Aenasius arizonensis (Girault) and Pseudleptomastix mexicana Noyes & Schauff (Hymenoptera: Encyrtidae) were more abundant. Besides, six species are hyperparasitoids viz., Aprostocetus sp, Chartocerus sp., Cheiloneurus sp., Homalotylus turkmenicus Myartseva, Marietta leopardina Motschulsky and Prochiloneurus sp. were recorded from primary parasitoids.

An Automated Approach for the Prediction of the Severity Level of Bug Reports Using GPT-2

Manual investigation is warranted in traditional approaches for estimating the bug severity level, which adds to the effort and time required. For bug severity report prediction, numerous automated strategies have been proposed in addition to manual ones. However, the current bug report predictors by facing several issues, such as overfitting and weight computation, and therefore, their efficiency for specific levels of data noise needs to improve. As a result, a bug report predictor is required to solve these concerns (e.g., overfitting and avoiding weight calculation, which increases computing complexity) and perform better in the situation of data noise. We use GPT-2’s features (limiting overfitting and supplying sequential predictors rather than weight computation) to develop a new approach for predicting the severity level of bug reports in this study. The proposed approach is divided into four stages. First, the bug reports are subjected to text preprocessing. Second, we assess each bug report’s emotional score. Third, each report is presented in vector format. Finally, an emotion score is assigned to each bug report, and a vector of each bug report is produced and sent to GPT-2. We employ statistical indicators like recall, precision, and F1-score to evaluate the suggested method’s effectiveness and efficacy. A comparison was also made using state-of-the-art bug report predictors such as Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) Network, Support Vector Machine (SVM), XGBoost, and Naive Bayes Multinomial (NBM). The proposed method’s promising result indicates its efficacy in bug information retrieval.

Characterizing duplicate bugs: Perceptions of practitioners and an empirical analysis

AbstractBug handling is an essential part of the software development process. Ideally, in a bug‐tracking system, bugs are reported, fixed, verified, and closed. In some cases, bugs have to be reopened mostly due to an incorrect fix. However, instead of reopening the existing bug report, users may submit a new report for a previously reported bug, which results in duplicate bug reports. Users might report duplicate bugs if they miss the previous bug report or if the previous bug is unresolved (i.e., in progress) and therefore cannot be reopened. These duplicate bug reports may cost extra maintenance efforts in triaging and bug fixing. There have been several studies on characterizing reopened bugs and duplicate bug reports; however, to the best of our knowledge, there has been no prior work on understanding the dynamics of their interaction, which is missed reopen bugs. Our study is based on analyzing the difference between duplicate and nonduplicate bugs and further categorizing the duplicate bugs. In this regard, we categorize duplicate bugs according to the original bug's resolution status at the duplicate's creation time as Master‐Unresolved bugs and Master‐Resolved (Missed Reopen) bugs to distinguish their properties. We compare these two types of bugs in terms of their relationship to their master bug, bug surface time, bug fix time, bug's severity, and the number of users involved. We perform case studies using the Eclipse and Mozilla projects' bug repositories that include more than 165,500 and 394,000 bug reports, respectively. Additionally, we investigate the perceived importance, impact, and causes of duplicate bugs, as well as the difference between nonduplicate and duplicate bugs and its categories for practitioners in the software industry by conducting a survey.

Bug Severity Prediction Algorithm Using Topic-Based Feature Selection and CNN-LSTM Algorithm

Increasing software usage has gradually increased the occurrence of bugs. When writing a bug report, the severity of the bug can be freely selected, so the subjective judgment of the author is involved. In subjective judgment, a severity error may occur depending on the background knowledge between the user and the developer. To resolve this problem, in this paper, the severity was predicted using the feature selection algorithm of the severity of each topic. We utilize the dataset in Eclipse and Mozilla open source projects. First, we classify bug reports by topic-based severity, and extract features from the severity of each topic. The severity was predicted by learning the characteristics from the CNN-LSTM algorithm, and the F-measure was 90.62% and 93.22% of Mozilla. To evaluate the effectiveness of the proposed model, we compared the baselines including DeepSeverity and EWD-Multinomial studies with Eclipse and Mozilla open source projects and showed that the proposed model is more efficient.

Text Mining Approaches for Dependent Bug Report Assembly and Severity Prediction

In general, most existing bug report studies focus only on solving a single specific issue. Considering of multiple issues at one is required for a more complete and comprehensive process of bug fixing. We took up this challenge and proposed a method to analyze two issues of bug reports based on text mining techniques. Firstly, dependent bug reports are assembled into an individual cluster and then the bug reports in each cluster are analyzed for their severity. The method of dependent bug report assembly is experimented with threshold-based similarity analysis. Cosine similarity and BM25 are compared with term frequency (tf) weighting to obtain the most appropriate method. Meanwhile, four classification algorithms namely Random Forest (RF), Support Vector Machines (SVM) with the RBF kernel function, Multinomial Naïve Bayes (MNB), and k-Nearest Neighbor (k-NN) are utilized to model the bug severity predictor with four term weighting schemes, i.e., tf, term frequency-inverse document frequency (tf-idf), term frequency-inverse class frequency (tf-icf), and term frequency-inverse gravity moment (tf-igm). After the experimentation process, BM25 was found to be the most appropriate for dependent bug report assemblage, while for severity prediction using tf-icf weighting on the RF method yielded the best performance value.