The Automatic Classification of Fault Trigger Based Bug Report

Du, Xiaoting; Zhang, Zheng; Xiao, Guanping; Yin, Beibei

doi:10.1109/issrew.2017.28

Cited by 15 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Not only limited to bug or severity, but also researchers have proposed different classification models from other perspectives. Du et al [25] developed an automatic classification framework based on word2vec in 2017, which classified bug reports into different fault trigger categories from four granularities, including Bug/Non-Bug, BOH/MAN, ARB/NAM, and NAM/ARB. In 2014, Tan et al [26] believed that semantic, security and concurrency problems are strongly related to software systems.…”

Section: Related Workmentioning

confidence: 99%

Automatic Classification of Bug Reports Based on Multiple Text Information and Reports’ Intention

Meng

Wang

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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 of bug reports. The innovation is that when categorizing bug reports, in addition to using the text information of the report, the intention of the report (i.e. "suggestion" or "explanation") is also considered, thereby improving the performance of the classification. First, we collect bug reports from four ecosystems (Apache, Eclipse, Gentoo, Mozilla) and manually annotate them to construct an experimental data set. Then, we use Natural Language Processing technology to preprocess the data. On this basis, BERT and TF-IDF are used to extract the features of the intention and the multiple text information. Finally, the features are used to train the classifiers. The experimental result on five classifiers (including K-Nearest Neighbor, Naive Bayes, Logistic Regression, Support Vector Machine and Random Forest) show that our proposed method achieves better performance and its F-Measure achieves from 87.3% to 95.5% .

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic Classification of Bug Reports Based on Multiple Text Information and Reports’ Intention

Meng

Wang

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…To resolve these problems, artificial intelligence techniques are now actively being studied, and have shown better classification accuracy than traditional (non-artificial intelligence based) methods [25][26][27][28][29][30][31][32][33][34][35][36]. Such techniques can be a key to solving most of the current problems regarding this issue.…”

mentioning

confidence: 99%

“…To reduce the effort required in this regard, studies have proposed the application of state-of-the-art automation methods for bug report classification [25][26][27][28][29]. In particular, latent Dirichlet allocation (LDA)-based classification methods are common because they are suitable to bug reports that contain text-based data.…”

mentioning

confidence: 99%

Improving bug report triage performance using artificial intelligence based document generation model

Lee

Seo

2020

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

Along with the fourth industrial revolution, artificial intelligence, big data, Internet of Things, and cloud computing are emerging as cutting-edge technologies globally. In particular, artificial intelligence has unlimited potential to further improve the quality of human life and can solve several difficult engineering problems [1-12]. Moreover, this technology provides basic ideas to derive successful solutions to numerous problems encountered in the software development field.

show abstract

“…There are many studies use of machine learning techniques for software faults prediction, the related researches examine more machine learning mechanism on several faults datasets, for examples, depending on Mandlebugs dataset, [27] and [28] are predicate fault software by machine learning techniques, where Carrozza et al used Mandelbugs location in the software of complex systems in their work and fault tolerance mechanisms. They analyze Mandelbugs and discuss a method based on a set of software complexity metrics for Mandelbug prediction.…”

mentioning

confidence: 99%

FIBR-OSS: fault injection model for bug reports in open-source software

Alazawi

Al-Salam²

2020

IJEECS

View full text Add to dashboard Cite

<span>For assessment of system dependability, fault injection techniques are used to expedite the presence of an error or failure in the system, which helps evaluate fault tolerance and system failure prediction. Defects classification and prediction is the principal significant advance in the trustworthiness evaluation of complex software systems such as open-source software since it can quickly be affected by the reliability of those systems, improves performance, and lessening the product cost. In this context, a new prototype of the fault injection model is presented, FIBR-OSS (Fault Injection for Bug Reports in Open-Source Software). FIBR-OSS can support developers to evaluate the system performance during phase's development for its dependability attributes such as reliability and system dependability means such as fault prediction or forecasting. FIBR-OSS is used for fault speed-up to test the system's failure prediction performance. Some machine learning techniques are implemented on bug reports produced existing by the bug tracking system as datasets for failure prediction techniques, some of those machine learning techniques are used in our approach.</span>

show abstract

The Automatic Classification of Fault Trigger Based Bug Report

Cited by 15 publications

References 17 publications

Automatic Classification of Bug Reports Based on Multiple Text Information and Reports’ Intention

Automatic Classification of Bug Reports Based on Multiple Text Information and Reports’ Intention

Improving bug report triage performance using artificial intelligence based document generation model

FIBR-OSS: fault injection model for bug reports in open-source software

Contact Info

Product

Resources

About