Can We Detect Bug Report Duplication with Unfinished Bug Reports?

Akihiro, Tsuruda; Manabe, Yuki; Aritsugi, Masayoshi

doi:10.1109/apsec.2015.33

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…The author in [39] also used the features and textual similarity to help assist in the duplicate bug report detection while writing the bug report so that user also does not continue writing the bug report. In their approach they have used some fixed amount of word to calculate the features.…”

Section: B Natural Language Processing Basedmentioning

confidence: 99%

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A Systematic Study of Duplicate Bug Report Detection

Gupta¹,

Gupta²

2021

IJACSA

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Defects are an integral part of any software project. They can arise at any time, at any phase of the software development or the maintenance phase. In open source projects, open bug repositories are used to maintain the bug reports. When a new bug report arrives, a person called "Triager" analyzes the bug report and assign it to some responsible developer. But before assigning, has to look if it is duplicate or not. Duplicate Bug Report is one of the big problems in the maintenance of bug repositories. Lack of knowledge and vocabulary skills of reporters sometimes increases the effort required for this purpose. Bug Tracking Systems are usually used to maintain the bug reports and are the most consulted resource during the maintenance process. Because of the Uncoordinated nature of the submission of bug reports to the tracking system, many times the same bug report is reported by many users. Duplicate Bug Reports lead to the waste of resources and the economy. It creates problems for triagers and requires a lot of analysis and validation. Lot of work has been done in the field of duplicate bug report detection. In this paper, we present the researches systematically done in this field by classifying the works into three categories and listing down the methods being used for the classified researches. The paper considers the papers till January 2020 for the analysis purpose. The paper mentions the strengths, limitations, data set, and the major approach used by the popular papers of the research in this field. The paper also lists the challenges and future directions in this field of research.

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Section: B Natural Language Processing Basedmentioning

confidence: 99%

“…For the binary classification, supervised learning they used the following methods for K-NN(K Nearest Neighbor, Linear SVM, RBF, SVM, Decision Tree, Random Forest and Naive Bayes.). The author in [39] used LibSVM after finding the features to find whether the bug report is duplicate or not.…”

Section: Machine Learning Basedmentioning

confidence: 99%

A Systematic Study of Duplicate Bug Report Detection

Gupta¹,

Gupta²

2021

IJACSA

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“…The semantic relationship between the content of the bug report is identified and this relationship is used to determine the relevant (important) features for detecting the duplicate bug report. In literature, the various machine learning and feature extraction techniques are used to extract the relevant features for developing an automatic duplicate bug report detection and classification system [10,11,12]. In spite of the substantial volume of work, the accuracy of existing duplicate detection system is comparatively low because of the following reasons: - Ignore the semantic relationship between the bug report content.…”

Section: A Motivationmentioning

confidence: 99%

Duplicate Bug Report Detection and Classification System Based on Deep Learning Technique

et al. 2020

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Duplicate bug report detection is a process of finding a duplicate bug report in the bug tracking system. This process is essential to avoid unnecessary work and rediscovery. In typical bug tracking systems, more than thousands of duplicate bug reports are reported every day. In turn, human cost, effort and time are increased. This makes it an important problem in the software management process. The solution is to automate the duplicate bug report detection system for reducing the manual effort, thus the productivity of triager's and developer's is increased. It also speeds up the process of software management as a result software maintenance cost is also reduced. However, existing systems are not quite accurate yet, in spite of these systems used various machine learning approaches. In this work, an automatic bug report detection and classification model is proposed using deep learning technique. The proposed system has three modules i.e. Preprocessing, Deep Learning Model and Duplicate Bug report Detection and Classification. Further, the proposed model used Convolutional Neural Network based deep learning model to extract relevant feature. These relevant features are used to determine the similar features of bug reports. Hence, the bug reports similarity is computers through these similar features. The performance of the proposed system is evaluated on six publicly available datasets using six performance metrics. It is noticed that the proposed system outperforms the existing systems by achieving an accuracy rate in the range of 85% to 99 % and recall@k rate in between 79%-94%. Moreover, the effectiveness of the proposed system is also measured on the cross training datasets of same and different domain. The proposed system achieves a good high accuracy rate for same domain data sets and low accuracy rate for different domain datasets.

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“…Existing defects or bugs are main issue during the development process and were caused by mistakes and failures made by people, as well as program codes or error in the technical designs. On the other hand, a number of bugs indicate the poor quality of the software [12] since the bug caused the failure of software production to have an impact [13] on the experience of users who use it [14]. The bug process provides an opportunity for developers to improve software performance [15] or quality.…”

Section: Introductionmentioning

confidence: 99%

The Performance of Fuzzy Logic How to Fixed The Risk Level of Bug System as Hard-working Quality Software

Pamuji¹

2019

JSC

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The quality of software production is considered important when testing which is involves several IT Staff such as IT development, operation, end-user. One of the issue was having today is a bug processing where it almost all platforms too difficult to avoid from the bugs and even might be full of the risks. In the main of Our focus is on how to measure and attempt to reduce the number were indicated as bugs from low up to critical levels. Furthermore, we were propose with a method already known as a fuzzy logic approach to measure the severity of the presence of bugs during the testing process. there are 20 thousand even more bugs have been reported and be supposed removed with the fuzzy logic approach with various levels. As The end result is that we have found a gradual 20% reduction in various criteria in the testing process as experimentally. Therefore, fuzzy logic is considered as effective enough to be able to improve existing methods and support to reduce bugs significantly.

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Can We Detect Bug Report Duplication with Unfinished Bug Reports?

Cited by 10 publications

References 13 publications

A Systematic Study of Duplicate Bug Report Detection

A Systematic Study of Duplicate Bug Report Detection

Duplicate Bug Report Detection and Classification System Based on Deep Learning Technique

The Performance of Fuzzy Logic How to Fixed The Risk Level of Bug System as Hard-working Quality Software

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