Effective Bug Processing and Tracking System

Angel, T. S. Shiny; Kumar, G. Senthil; Sehgal, Vivek; Nayak, Gargi

doi:10.1166/jctn.2018.7506

Cited by 10 publications

(6 citation statements)

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“…The information is also target at finer granularity by the pre-defined filters which are included separately for every dimension of input word embedding's as described in Figure 3. Suppose for every dimension k of word embedding's, where and = gives the output vector for every k dimension where i th entry (for i ) is expressed as (2) The utilization of word embedding's in both ways helps in extracting richer sentence modeling. Furthermore, by updating the word embedding in training phase capture more distinct information.…”

Section: A Cnn Based Feature Extraction Layermentioning

confidence: 99%

“…Further, if the new bug report is not duplicate then it is added to the master bug report list else, it is considered as a duplicate bug report. However, the filtering process of duplicate bug report requires extensive amount of manual effort, time and complete knowledge of bugs [2]. Previously study showed that near about 220 bug reports are submitted in BST per day in which 25-30% reports are duplicates [3].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A Cnn Based Feature Extraction Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…It is a fundamental characteristic that gives general advantages to the industry, which requires earnestness in its resolution with respect to resources. It proves that 80% of bug reports goes through severity reassignment, even after the bug report has been sent to a developer [4]. Additionally, it demonstrates that bug reports with severity reassignment have fundamentally longer resolution times than those with no reassigned severity.…”

Section: Introductionmentioning

confidence: 97%

“…The developer (triager) uses this information to resolve the costly bug, which is a time-consuming process. The triager has to choose and identify the issue from a large number of communicated software bugs [2,3,4]. Different bugs have different impacts upon the quality, based on the functionality of software.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

Kukkar

Mohana

Nayyar

et al. 2019

Sensors

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The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.

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“…For this reason, software organizations utilize bug tracking system (BTS) primarily to handle bug reporting, and bug fixing processes. These large software organizations receive numerous bug reports submissions per day through the BTS provided to the users (Angel, Kumar, Sehgal and Nayak, 2018). A bug report contains the description, summary, reporter, and sometimes stack trace of the buggy code, and many other information.…”

Section: Introductionmentioning

confidence: 99%

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

Ahmad

Kholief

et al. 2023

Preprint

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Many software projects utilize Bug Tracking System (BTS) to manage and process bug reports. Over the years, the number of bug report submissions has increased exponentially with some projects receiving as many as about a hundred submissions daily. Bug report processing (BRP) consists of five key processes: duplicate detection, severity prediction, fix-time prediction, bug triage, and bug localization. Previously, traditional machine learning (ML) algorithms -based models were proposed to automate BRP tasks. However, in recent years deep BRP (DBRP) models were proposed to exploit the ever-increasing bug repositories for automatic extraction of semantic and contextual features of bug reports. Although, some papers reviewed related literature from many perspectives of software maintenance, no existing work comprehensively reviewed the state of DBRP. In this paper, we review the state of DBRP models in the five key BRP tasks. For this, we collect papers from four international databases published between 2015 and 2021. We evaluate the papers with regard to the deep neural networks, text representation models, and bug report features utilized. Finally, we present findings and prospects for future research works in DBRP. Our review analyzes how word embedding, CNNs, LSTMs, attention mechanism are used to represent bug report and source code, model performance and challenges such as data imbalance, feature utilization and complexity.

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Effective Bug Processing and Tracking System

Cited by 10 publications

References 0 publications

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

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

A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

Deep Bug Reports Processing (DBRP): A Systematic Literature Review

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