scholarly journals An Automated Approach for Mapping Bug Reports to Source Code and Bug Triaging

2018 ◽  
Vol 6 (6) ◽  
pp. 94-101
Author(s):  
Alphy Jose
Keyword(s):  
Source Code ◽  
Bug Triaging ◽  
Bug Reports

A Multi-Source Approach for Bug Triage

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1593-1604 ◽  
Author(s):  
Jin Liu ◽  
Yiqiuzi Tian ◽  
Xiao Yu ◽  
Zhijiang Yang ◽  
Xiangyang Jia ◽  
...  
Keyword(s):  
Portfolio Theory ◽  
Time Cost ◽  
Modern Portfolio Theory ◽  
Bug Triaging ◽  
Bug Reports ◽  
Bug Fixing ◽  
History Of ◽  
Recommendation Accuracy

Bug triaging refers to the process of assigning a bug to the most appropriate fixer. As the scale and complexity of software increases, bug triaging becomes a tedious and time-consuming work. Existing bug triaging approaches typically treat it as a problem of optimizing recommendation accuracy. However, the time that different fixers may spend also varies. Thus, we take time cost as another optimizing objective aside from accuracy and use modern portfolio theory to strike a balance between them. In addition, for fixers with little fixing records, we need more data to build profiles about their expertise. To address these problems, we propose a bug triaging approach with awareness of accuracy and time cost, and we use bug reports from other projects to enrich the bug fixing history of fixers. We evaluate our approach with experiments on data collected from Bugzilla. The experiment results validate the effectiveness of our approach.

scholarly journals Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code

2017 ◽  
Author(s):  
Xuan Huo ◽  
Ming Li
Keyword(s):  
Programming Languages ◽  
Software Maintenance ◽  
Structural Information ◽  
Source Code ◽  
Semantic Features ◽  
Software Projects ◽  
Bug Reports ◽  
Bug Report ◽  
Structural Semantics ◽  
Sequential Nature

Bug reports provide an effective way for end-users to disclose potential bugs hidden in a software system, while automatically locating the potential buggy source files according to a bug report remains a great challenge in software maintenance. Many previous approaches represent bug reports and source code from lexical and structural information correlated their relevance by measuring their similarity, and recently a CNN-based model is proposed to learn the unified features for bug localization, which overcomes the difficulty in modeling natural and programming languages with different structural semantics. However, previous studies fail to capture the sequential nature of source code, which carries additional semantics beyond the lexical and structural terms and such information is vital in modeling program functionalities and behaviors. In this paper, we propose a novel model LS-CNN, which enhances the unified features by exploiting the sequential nature of source code. LS-CNN combines CNN and LSTM to extract semantic features for automatically identifying potential buggy source code according to a bug report. Experimental results on widely-used software projects indicate that LS-CNN significantly outperforms the state-of-the-art methods in locating buggy files.

scholarly journals Mapping Bug Reports to Relevant Source Code Files Based on the Vector Space Model and Word Embedding

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 78870-78881 ◽  
Author(s):  
Guangliang Liu ◽  
Yang Lu ◽  
Ke Shi ◽  
Jingfei Chang ◽  
Xing Wei
Keyword(s):  
Vector Space ◽  
Source Code ◽  
Vector Space Model ◽  
Word Embedding ◽  
Space Model ◽  
Bug Reports

Analytical Study on Bug Triaging Practices

2020 ◽  
pp. 1698-1725 ◽  
Author(s):  
Anjali Goyal ◽  
Neetu Sardana
Keyword(s):  
Source Code ◽  
Five Dimensions ◽  
Software Bugs ◽  
Bug Triaging ◽  
Current State ◽  
Bug Report ◽  
Code Changes ◽  
Research Questions ◽  
Evaluation Parameters ◽  
Source Code Changes

Software bugs are inevitable and fixing these bugs is a difficult and time consuming task. Bug report assignment is the activity of designating a developer who makes source code changes in order to fix the bug. Many bug assignment techniques have been proposed in the existing studies. These studies use different datasets, varied input and evaluation parameters to validate their work. This diversification in bug triaging results in perplexity among researchers. Hence, this paper organizes the work performed in bug triaging in a structured manner. This paper aims to present current state of the art to provide a structured consolidation of bug triaging approaches. The paper has identified six research questions under five dimensions to address the various aspects of bug triaging. 60 articles from 36 venues have been reviewed and categorized in order to organize and substructure existing work in the field of bug report assignment. This study will help researchers to wisely decide the weapons for bug triaging. Also, it will act as a ready reference for the bug triaging practitioners.

Analytical Study on Bug Triaging Practices

2021 ◽  
pp. 1068-1094
Author(s):  
Anjali Goyal ◽  
Neetu Sardana
Keyword(s):  
Source Code ◽  
Five Dimensions ◽  
Software Bugs ◽  
Bug Triaging ◽  
Current State ◽  
Bug Report ◽  
Code Changes ◽  
Research Questions ◽  
Evaluation Parameters ◽  
Source Code Changes

Software bugs are inevitable and fixing these bugs is a difficult and time consuming task. Bug report assignment is the activity of designating a developer who makes source code changes in order to fix the bug. Many bug assignment techniques have been proposed in the existing studies. These studies use different datasets, varied input and evaluation parameters to validate their work. This diversification in bug triaging results in perplexity among researchers. Hence, this paper organizes the work performed in bug triaging in a structured manner. This paper aims to present current state of the art to provide a structured consolidation of bug triaging approaches. The paper has identified six research questions under five dimensions to address the various aspects of bug triaging. 60 articles from 36 venues have been reviewed and categorized in order to organize and substructure existing work in the field of bug report assignment. This study will help researchers to wisely decide the weapons for bug triaging. Also, it will act as a ready reference for the bug triaging practitioners.

scholarly journals Control Flow Graph Embedding Based on Multi-Instance Decomposition for Bug Localization

2020 ◽  
Vol 34 (04) ◽  
pp. 4223-4230
Author(s):  
Xuan Huo ◽  
Ming Li ◽  
Zhi-Hua Zhou
Keyword(s):  
Programming Languages ◽  
Software Maintenance ◽  
Source Code ◽  
Control Flow ◽  
Control Flow Graph ◽  
Bug Localization ◽  
Flow Graph ◽  
Bug Reports ◽  
Bug Report ◽  
Sequential Nature

During software maintenance, bug report is an effective way to identify potential bugs hidden in a software system. It is a great challenge to automatically locate the potential buggy source code according to a bug report. Traditional approaches usually represent bug reports and source code from a lexical perspective to measure their similarities. Recently, some deep learning models are proposed to learn the unified features by exploiting the local and sequential nature, which overcomes the difficulty in modeling the difference between natural and programming languages. However, only considering local and sequential information from one dimension is not enough to represent the semantics, some multi-dimension information such as structural and functional nature that carries additional semantics has not been well-captured. Such information beyond the lexical and structural terms is extremely vital in modeling program functionalities and behaviors, leading to a better representation for identifying buggy source code. In this paper, we propose a novel model named CG-CNN, which is a multi-instance learning framework that enhances the unified features for bug localization by exploiting structural and sequential nature from the control flow graph. Experimental results on widely-used software projects demonstrate the effectiveness of our proposed CG-CNN model.

scholarly journals Towards the Integration of Versioning Systems, Bug Reports and Source Code Meta-Models

2005 ◽  
Vol 127 (3) ◽  
pp. 87-99 ◽  
Author(s):  
Giuliano Antoniol ◽  
Massimiliano Di Penta ◽  
Harald Gall ◽  
Martin Pinzger
Keyword(s):  
Source Code ◽  
Bug Reports

scholarly journals Utilizing Topic-Based Similar Commit Information and CNN-LSTM Algorithm for Bug Localization

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 406
Author(s):  
Geunseok Yang ◽  
Byungjeong Lee
Keyword(s):  
Software Maintenance ◽  
Short Term Memory ◽  
Source Code ◽  
Performance Comparison ◽  
Bug Localization ◽  
Source Codes ◽  
Software Bugs ◽  
Bug Reports ◽  
Bug Report ◽  
Model Training

With the use of increasingly complex software, software bugs are inevitable. Software developers rely on bug reports to identify and fix these issues. In this process, developers inspect suspected buggy source code files, relying heavily on a bug report. This process is often time-consuming and increases the cost of software maintenance. To resolve this problem, we propose a novel bug localization method using topic-based similar commit information. First, the method determines similar topics for a given bug report. Then, it extracts similar bug reports and similar commit information for these topics. To extract similar bug reports on a topic, a similarity measure is calculated for a given bug report. In the process, for a given bug report and source code, features shared by similar source codes are classified and extracted; combining these features improves the method’s performance. The extracted features are presented to the convolutional neural network’s long short-term memory algorithm for model training. Finally, when a bug report is submitted to the model, a suspected buggy source code file is detected and recommended. To evaluate the performance of our method, a baseline performance comparison was conducted using code from open-source projects. Our method exhibits good performance.

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