Bugtrac – A New Improved Bug Tracking System

Gopal, Madhwaraj Kango; Govindaraj, M; Chandra, Paramita; Shetty, Prathiksha; Raj, Sunny

doi:10.1109/delcon54057.2022.9753390

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…However, the large quantity of software fault data recorded on the Bugzilla is not utilized by the developers and users. The problem of reliability/quality for open-source products will be resolved if the large quantity database of Bugzilla is used efficiently (Kaur, and Jindal, 2017;Gopal et al, 2022, Tuna et al, 2022.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software

Tamura

Yamada

2023

Int. j. math. eng. manag. sci.

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Recently, many open-source products have been used under the situations of general software development, because the cost saving and standardization. Therefore, many open-source products are gathering attention from many software development companies. Then, the reliability/quality of open-source products becomes very important factor for the software development. This paper focuses on the reliability/quality evaluation of open-source products. In particular, the large quantity fault data sets recorded on Bugzilla of open-source products is used in many open-source development projects. Then, the large amount of data sets of software faults is recorded on the Bugzilla. This paper proposes the reliability/quality evaluation approach based on the deep machine learning by using the large quantity fault data on the Bugzilla. Moreover, the large quantity fault data sets are analyzed by the deep machine learning based on the fine-tuning.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software

Tamura

Yamada

2023

Int. j. math. eng. manag. sci.

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BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

Wang

Tong

et al. 2022

Complex Intell. Syst.

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Since defects in software may cause product fault and financial loss, it is essential to conduct software defect prediction (SDP) to identify the potentially defective modules, especially in the early stage of the software development lifecycle. Recently, cross-version defect prediction (CVDP) began to draw increasing research interests, employing the labeled defect data of the prior version within the same project to predict defects in the current version. As software development is a dynamic process, the data distribution (such as defects) during version change may get changed. Recent studies utilize machine learning (ML) techniques to detect software defects. However, due to the close dependencies between the updated and unchanged code, prior ML-based methods fail to model the long and deep dependencies, causing a high false positive. Furthermore, traditional defect detection is performed on the entire project, and the detection efficiency is relatively low, especially on large-scale software projects. To this end, we propose BugPre, a CVDP approach to address these two issues. BugPre is a novel framework that only conducts efficient defect prediction on changed modules in the current version. BugPre utilizes variable propagation tree-based associated analysis method to obtain the changed modules in the current version. Besides, BugPre constructs graph leveraging code context dependences and uses a graph convolutional neural network to learn representative characteristics of code, thereby improving defect prediction capability when version changes occur. Through extensive experiments on open-source Apache projects, the experimental results indicate that our BugPre outperforms three state-of-the-art defect detection approaches, and the F1-score has increased by higher than 16%.

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