The exercise of detecting similar bug reports in bug tracking systems is known as duplicate bug report detection. Having prior knowledge of a bug report's existence reduces efforts put into debugging problems and identifying the root cause. Rule and Query-based solutions recommend a long list of potential similar bug reports with no clear ranking. In addition, triage engineers are less motivated to spend time going through an extensive list. Consequently, this deters the use of duplicate bug report retrieval solutions. In this paper, we have proposed a solution using a combination of NLP techniques. Our approach considers unstructured and structured attributes of a bug report like summary, description and severity, impacted products, platforms, categories, etc. It uses a custom data transformer, a deep neural network, and a non-generalizing machine learning method to retrieve existing identical bug reports. We have performed numerous experiments with significant data sources containing thousands of bug reports and showcased that the proposed solution achieves a high retrieval accuracy of 70% for recall@5.
Automated Duplicate Bug Report Detection in Large Open Bug Repositories
Department of Computer Science University of Colorado Colorado Springs United States（计算机科学系，科罗拉多州立大学，美国）；George Mason University, Fairfax, VA, United States（乔治·马歇尔大学，弗吉尼亚州 Fairfax，美国）
Agents in the Sandbox: End-to-End Crash Bug Reproduction for Minecraft
Electronics Engineering Department Bilkent University Ankara, Turkey（电子工程系比尔肯特大学安卡拉土耳其）；Computer Engineering Department Bilkent University Ankara, Turkey（计算机工程系比尔肯特大学安卡拉土耳其）
Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；School of Information and Telecommunication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与电信工程学院）；Peking University（北京大学）；G Labs（0G实验室）
Labelling Bug-Fixing Commits with Local Open-Weight Language Models
Faculty of Computer Science, University of Vienna（维也纳大学计算机学院）；SBA Research gGmbH（SBA研究所）；Christian Doppler Laboratory AsTra, University of Vienna（维也纳大学AsTra克里斯蒂安·多普勒实验室）
KAIST（韩国科学技术院）；Hyundai Motor Group（现代汽车集团）；Chalmers University（查尔姆斯理工大学）
Deep Neural Networks (DNNs) are rapidly being adopted by the automotive industry, due to their impressive performance in tasks that are essential for autonomous driving. Object segmentation is one such task: its aim is to precisely locate boundaries of objects and classify the identified objects, helping autonomous cars to recognise the road environment and the traffic situation. Not only is this task safety critical, but developing a DNN based object segmentation module presents a set of challenges that are significantly different from traditional development of safety critical software. The …
Context: The identification of bugs within the reported issues in an issue tracker is crucial for the triage of issues. Machine learning models have shown promising results regarding the performance of automated issue type prediction. However, we have only limited knowledge beyond our assumptions how such models identify bugs. LIME and SHAP are popular technique to explain the predictions of classifiers. Objective: We want to understand if machine learning models provide explanations for the classification that are reasonable to us as humans and align with our assumptions of what the models sh…
As part of the process of resolving issues submitted by users via bug reports, Android developers attempt to reproduce and observe the failures described by the bug report. Due to the low-quality of bug reports and the complexity of modern apps, the reproduction process is non-trivial and time-consuming. Therefore, automatic approaches that can help reproduce Android bug reports are in great need. However, current approaches to help developers automatically reproduce bug reports are only able to handle limited forms of natural language text and struggle to successfully reproduce failures for w…
Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using software. However, it is difficult for non-technical users to write clear descriptions about the bug occurrence. Therefore, more and more users begin to record the screen for reporting bugs as it is easy to be created and contains detailed procedures triggering the bug. But it is still tedious and time-consuming for developers to reproduce the bug due to the length and unclear actions within the recording. To overcome these issues, we propose GIFdroid, a light-weight app…
With the rapid growth of software scale and complexity, a large number of bug reports are submitted to the bug tracking system. In order to speed up defect repair, these reports need to be accurately classified so that they can be sent to the appropriate developers. However, the existing classification methods only use the text information of the bug report, which leads to their low performance. To solve the above problems, this paper proposes a new automatic classification method for bug reports. The innovation is that when categorizing bug reports, in addition to using the text information o…
Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in any software application, DNN-based apps are afflicted with bugs. Previous work observed that DNN bug fix patterns are different from traditional bug fix patterns. Furthermore, those buggy models are non-trivial to diagnose and fix due to inexplicit errors with several options to fix them. To support developers in locating and fixing bugs, we propose DeepDiagnosis, a novel debugging approach that localizes the faults, reports error symptoms and suggests fixes for DNN programs. In the first phase, our techniq…
Logs are one of the most critical data for service management. It contains rich runtime information for both services and users. Since size of logs are often enormous in size and have free handwritten constructions, a typical log-based analysis needs to parse logs into structured format first. However, we observe that most existing log parsing methods cannot parse logs online, which is essential for online services. In this paper, we present an automatic online log parsing method, name as LogStamp. We extensively evaluate LogStamp on five public datasets to demonstrate the effectiveness of our…
Many automated program repair techniques have been proposed for fixing bugs. Some of these techniques use the information beyond the given buggy program and test suite to improve the quality of generated patches. However, there are several limitations that hinder the wide adoption of these techniques, including (1) they rely on a fixed set of repair templates for patch generation or reference implementation, (2) searching for the suitable reference implementation is challenging, (3) generated patches are not explainable. Meanwhile, a recent approach shows that similar bugs exist across differe…
One of the key tasks related to ensuring mobile app quality is the reporting, management, and resolution of bug reports. As such, researchers have committed considerable resources toward automating various tasks of the bug management process for mobile apps, such as reproduction and triaging. However, the success of these automated approaches is largely dictated by the characteristics and properties of the bug reports they operate upon. As such, understanding mobile app bug reports is imperative to drive the continued advancement of report management techniques. While prior studies have examin…
[Background/Context] The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in 2011-2016. The prototype evolved into an internal Ericsson product, TRR, in 2017-2018. TRR's first bug assignment without human intervention happened in 2019. [Objective/Aim] Our exploratory study will evaluate the adoption of TRR within its industrial context at Ericsson. We seek to understand 1) how TRR performs in the field, 2) what value…
App stores allow users to give valuable feedback on apps, and developers to find this feedback and use it for the software evolution. However, finding user feedback that matches existing bug reports in issue trackers is challenging as users and developers often use a different language. In this work, we introduce DeepMatcher, an automatic approach using state-of-the-art deep learning methods to match problem reports in app reviews to bug reports in issue trackers. We evaluated DeepMatcher with four open-source apps quantitatively and qualitatively. On average, DeepMatcher achieved a hit ratio …
Bug triage can be defined as the process of assigning a developer to a bug report. The duty of the bug triage team is to study the developers profiles well in order to make an appropriate match between the developers and the incoming bug reports. Thus, this process is a vital step in issue management system. In fact, the number of bug reports submitted every day is gradually increasing which affects the developer workload. Thus, the triage team should consider this factor in distributing the bugs and because of the manual approach, many developers are burden. In particular, triaging bug report…
