An Automated approach for Bug Categorization using Fuzzy Logic

Abstract

Various automated techniques built to benefit software developers, bug triagers, stakeholders and users in open source systems, utilize information placed in issue tracking systems. The success of these techniques depends largely on the quality of information present in the issue reports. Assigning correct label to issue reports is one of the quality concerns. Previous empirical studies conducted on the issue reports show that most issues are either mislabeled or are not labeled at all. Thus, in order to enhance quality of issue reports, there is a strong need to propose an automated and accurate bug labeling approach. A label can be a bug, feature enhancement or other request.In this paper, we propose an automated approach to label an issue either as bug or other request based on fuzzy set theory. Experiments are conducted on issue repository of three open source software systems: HTTPClient, Jackrabbit and Lucene. We have achieved an accuracy of 87%, 83.5% and 90.8% and FMeasure score of 0.83, 0.79 and 0.84 respectively. This is a considerable improvement as compared to the earlier reported work on these three datasets using topic modeling approach.

Classifying bug reports to bugs and other requests: an approach using topic modelling and fuzzy set theory

“…The proposed approach used topic-based data to train a classification model by leveraging the fuzzy set theory. The first baseline approach used word-based data to train a classification model using the fuzzy set theory [5]. The second baseline used topic-based data to train three models using several machine learning techniques [3].…”

Classifying bug reports to bugs and other requests: an approach using topic modelling and fuzzy set theory

“…The proposed approach used topic-based data to train a classification model by leveraging the fuzzy set theory. The first baseline approach used word-based data to train a classification model using the fuzzy set theory [5]. The second baseline used topic-based data to train three models using several machine learning techniques [3].…”

Detecting non-natural language artifacts for de-noising bug reports

“…The application of NLP and IR approaches on textual bug reports and issue tickets is an active research field. These methods are applied to solve a wide variety of tasks, for example, classification (Zhou et al 2021;Thung et al 2012;Chawla and Singh 2015), automated assignment (Mani et al 2019;Devaiya et al 2021), and fault localization (Zhou et al 2012;Saha et al 2013;Ye et al 2016). Some approaches require non-natural artifacts to be removed or treated separately, for example, language identification (Jauhiainen et al 2017), bug type classification (Hirsch and Hofer 2022a), and personality analysis (Calefato et al 2019).…”

Analysis and comparison of deep learning models for user stories identification

“…The authors in [6] manually classified a dataset and applied machine learning algorithms for bug classification. In [7], an automated approach is proposed to label an issue either as a bug or other request based on fuzzy set theory. The labeling of bug reports is done in three phases.…”

Classifying bug reports to bugs and other requests: an approach using topic modelling and fuzzy set theory

“…The proposed approach used topic-based data to train a classification model by leveraging the fuzzy set theory. The first baseline approach used word-based data to train a classification model using the fuzzy set theory [5]. The second baseline used topic-based data to train three models using several machine learning techniques [3].…”

Detecting non-natural language artifacts for de-noising bug reports

“…The application of NLP and IR approaches on textual bug reports and issue tickets is an active research field. These methods are applied to solve a wide variety of tasks, for example, classification (Zhou et al 2021;Thung et al 2012;Chawla and Singh 2015), automated assignment (Mani et al 2019;Devaiya et al 2021), and fault localization (Zhou et al 2012;Saha et al 2013;Ye et al 2016). Some approaches require non-natural artifacts to be removed or treated separately, for example, language identification (Jauhiainen et al 2017), bug type classification (Hirsch and Hofer 2022a), and personality analysis (Calefato et al 2019).…”

Analysis and comparison of deep learning models for user stories identification

“…The authors in [6] manually classified a dataset and applied machine learning algorithms for bug classification. In [7], an automated approach is proposed to label an issue either as a bug or other request based on fuzzy set theory. The labeling of bug reports is done in three phases.…”

Classifying bug reports to bugs and other requests: an approach using topic modelling and fuzzy set theory

“…The proposed approach used topic-based data to train a classification model by leveraging the fuzzy set theory. The first baseline approach used word-based data to train a classification model using the fuzzy set theory [5]. The second baseline used topic-based data to train three models using several machine learning techniques [3].…”

Detecting non-natural language artifacts for de-noising bug reports

“…The application of NLP and IR approaches on textual bug reports and issue tickets is an active research field. These methods are applied to solve a wide variety of tasks, for example, classification (Zhou et al 2021;Thung et al 2012;Chawla and Singh 2015), automated assignment (Mani et al 2019;Devaiya et al 2021), and fault localization (Zhou et al 2012;Saha et al 2013;Ye et al 2016). Some approaches require non-natural artifacts to be removed or treated separately, for example, language identification (Jauhiainen et al 2017), bug type classification (Hirsch and Hofer 2022a), and personality analysis (Calefato et al 2019).…”

Analysis and comparison of deep learning models for user stories identification

“…The authors in [6] manually classified a dataset and applied machine learning algorithms for bug classification. In [7], an automated approach is proposed to label an issue either as a bug or other request based on fuzzy set theory. The labeling of bug reports is done in three phases.…”