Search Results (9)

Fuzz testing plays a key role in improving Linux kernel security, but large-scale fuzzing often generates a high number of crash reports, many of which are redundant. These duplicated reports burden triage efforts and delay the identification of truly impactful bugs. Syzkaller, a widely used kernel fuzzer, clusters crashes using instruction pointers and sanitizer metadata. However, this heuristic may misgroup distinct issues or split similar ones caused by the same root cause. To address this, we present ECHO, a lightweight call stack-based deduplication tool that analyzes structural similarity among kernel stack traces. By computing the longest common subsequence (LCS) between normalized call stacks, ECHO groups semantically related crashes and improves post-fuzzing analysis. We integrate ECHO into the Syzkaller fuzzing workflow and use it to prioritize inputs that trigger deeper, previously untested kernel paths. Evaluated across multiple Linux kernel versions, ECHO improves average code coverage by 15.2% and discovers 20 previously unknown bugs, all reported to the Linux kernel community. Our results highlight that stack-aware crash grouping not only streamlines triage, but also enhances fuzzing efficiency by guiding seed selection toward unexplored execution paths. Full article

A large number of bug reports are submitted by users and developers in bug-tracking system every day. It is time-consuming for software maintainers to assign bug reports to appropriate developers for fixing manually. Many bug-triaging methods have been developed to automate this process. However, most previous studies mainly focused on analyzing textual content and failed to make full use of the structured information embedded in the bug-tracking system. In fact, this structured information, which plays an important role in bug triaging, reflects the process of bug tracking and the historical activities. To further improve the performance of automatic bug triaging, in this study, we propose a new representation learning model, PTITransE, for knowledge bases, which extends TransE via enhancing the embeddings with textual entity descriptions and is more suitable for bug triaging. Moreover, we make the first attempt to apply knowledge base and link prediction techniques to bug triaging. For each new bug report, the proposed framework can recommend top-k developers for fixing the bug report by using the learned embeddings of entities and relations. Evaluation is performed on three real-world projects, and the results indicate that our method outperforms baseline bug triaging approaches and can alleviate the cold-start problem in bug triaging. Full article

The prioritization of bug reports based on severity is a crucial aspect of bug triaging, enabling a focus on more critical issues. Traditional methods for assessing bug severity range from manual inspection to the application of machine and deep learning techniques. However, manual evaluation tends to be resource-intensive and inefficient, while conventional learning models often lack contextual understanding. This study explores the effectiveness of large language models (LLMs) in predicting bug report severity. We propose a novel approach called SevPredict using GPT-2, an advanced LLM, and compare it against state-of-the-art models. The comparative analysis between the proposed approach and state-of-the-art approaches suggests that the proposed approach outperforms the state-of-the-art approaches in terms of performance evaluation metrics. SevPredict shows improvements over the best-performing state-of-the-art approach (BERT-SBR) with 1.72% higher accuracy, 2.18% higher precision, and 4.94% higher MCC. The improvements are even more substantial when compared to the approach by Ramay et al., with SevPredict demonstrating 10.66% higher accuracy, 10.39% higher precision, 3.29% higher recall, 7.19% higher F1-score, and a remarkable 41.27% higher MCC. These findings not only demonstrate the superiority of our GPT-2-based approach in predicting the severity of bug reports but also highlight its potential to significantly advance automated bug triaging and software maintenance. This research introduces a severity prediction tool named SevPredict. Full article

To address the issue of insufficient testing caused by the continuous reduction of software development cycles, many organizations maintain bug repositories and bug tracking systems to ensure real-time updates of bugs. However, each day, a large number of bugs is discovered and sent to the repository, which imposes a heavy workload on bug fixers. Therefore, effective bug deduplication and triage are of great significance in software development. This paper provides a comprehensive investigation and survey of the recent developments in bug deduplication and triage. The study begins by outlining the roadmap of the existing literature, including the research trends, mathematical models, methods, and commonly used datasets in recent years. Subsequently, the paper summarizes the general process of the methods from two perspectives—runtime information-based and bug report-based perspectives—and provides a detailed overview of the methodologies employed in relevant works. Finally, this paper presents a detailed comparison of the experimental results of various works in terms of usage methods, datasets, accuracy, recall rate, and F1 score. Drawing on key findings, such as the need to improve the accuracy of runtime information collection and refine the description information in bug reports, we propose several potential future research directions in the field, such as stack trace enrichment and the combination of new NLP models. Full article

Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug’s class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers’ profiles and analyzing the history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with “black box” AI, which even its designers cannot explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system’s effectiveness, transparency, and confidence. The trained model is utilized in the recommendation stage to calculate relevance scores for developers based on expertise and past bug handling performance, ultimately presenting the developers with the highest scores as recommendations for new bugs. This approach aims to strike a balance between computational efficiency and accurate predictions, enabling efficient bug assignment while considering developer expertise and historical performance. In this paper, we propose two explainable models for recommendation. The first is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to identify the most suitable developer for each bug from bug history. Full article

With an increase in the use of software, the incidence of bugs and resulting maintenance costs also increase. In open source projects, developer reassignment accounts for approximately 50%. Software maintenance costs can be reduced if appropriate developers are recommended to resolve bugs. In this study, features are extracted by applying feature selection for each developer. These features are entered into CNN-LSTM algorithm to learn the model and recommend appropriate developers. To compare the performance of the proposed model, open source projects (Google Chrome, Mozilla Core, and Mozilla Firefox) were used to compare the performance of the proposed method with a baseline for developer recommendation. In this paper, the performance showed 54% for F-measure and 52% for accuracy in open source projects. The proposed model has improved and showed about a 13% more effective performance improvement than with DeepTriage. It was discovered that the performance of the proposed model was better. Full article

Software maintenance and evolution account for approximately 90% of the software development process (e.g., implementation, testing, and maintenance). Bug triaging refers to an activity where developers diagnose, fix, test, and document bug reports during software development and maintenance to improve the speed of bug repair and project progress. However, the large number of bug reports submitted daily increases the triaging workload, and open-source software has a long maintenance cycle. Meanwhile, the developer activity is not stable and changes significantly during software development. Hence, we propose a novel bug triaging model known as auto bug triaging via deep reinforcement learning (BT-RL), which comprises two models: a deep multi-semantic feature (DMSF) fusion model and an online dynamic matching (ODM) model. In the DMSF model, we extract relevant information from bug reports to obtain high-quality feature representation. In the ODM model, through bug report analysis and developer activities, we use a strategy based on the reinforcement learning framework, through which we perform training while learning and recommend developers for bug reports. Extensive experiments on open-source datasets show that the BT-RL method outperforms state-of-the-art methods in bug triaging. Full article

The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques. Full article

With the increasing number of bug reports coming into the open bug repository, it is impossible to triage bug reports manually by software managers. This paper proposes a novel approach called En-LDA (Entropy optimized Latent Dirichlet Allocation (LDA)) for automatic bug report assignment. Specifically, we propose entropy to optimize the number of topics of the LDA model and further use the entropy optimized LDA to capture the expertise and interest of developers in bug resolution. A developer’s interest in a topic is modeled by the number of the developer’s comments on bug reports of the topic divided by the number of all the developer’s comments. A developer’s expertise in a topic is modeled by the number of the developer’s comments on bug reports of the topic divided by the number of all developers’ comments on the topic. Given a new bug report, En-LDA recommends a ranked list of developers who are potentially adequate to resolve the new bug. Experiments on Eclipse JDT and Mozilla Firefox projects show that En-LDA can achieve high recall up to 84% and 58%, and precision up to 28% and 41%, respectively, which indicates promising aspects of the proposed approach. Full article