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index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-180076082-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-180076082-1');
</script>
<title>Duplicate Bug Report Detection</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">
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<style>
/* Remove the navbar's default margin-bottom and rounded borders */
.navbar {
margin-bottom: 0;
border-radius: 0;
}
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.row.content {height: 1200px}
/* Set gray background color and 100% height */
.sidenav {
padding-top: 20px;
background-color: #f1f1f1;
height: 100%;
}
/* Set black background color, white text and some padding */
footer {
background-color: #555;
color: white;
padding: 15px;
}
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@media screen and (max-width: 767px) {
.sidenav {
height: auto;
padding: 15px;
}
.row.content {height:auto;}
}
</style>
</head>
<body>
<nav class="navbar navbar-inverse">
<div class="container-fluid">
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#myNavbar">
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<span class="icon-bar"></span>
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<li class="active"><a href="index.html">Home</a></li>
<li class="active"><a href="Artifacts.html">Artifacts</a></li>
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</ul>
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<div class="container-fluid text-center">
<div class="row content">
<div class="col-sm-2 sidenav">
<h3></h3>
</div>
<div class="col-sm-8 text-left">
<h1 class="text-center">Duplicate Bug Report Detection using an Attention-based Pre-trained Neural Language Model</h1>
<hr>
<p><strong>Context:</strong> Users and developers use bug tracking systems to report errors that occur during the development and testing of software. The manual identification of duplicates is a tedious task especially with software that have large bug repositories.
In this context, their automatic detection becomes a necessary task that can help prevent frequently fixing the same bug.</p>
<p><strong>Objective:</strong>
In this paper, we propose BERT-MLP, a novel pre-trained language model using Bidirectional Encoder Representations from Transformers (BERT) for duplicate bug report detection with the aim of improving the detection rate compared to existing works.</p>
<p><strong>Method:</strong>
Our approach considers only unstructured data. These are fed into the BERT model in order to learn the contextual relationships between words. The output is fed into a Multi-Layer Perceptron (MLP) classifier, which represents our base duplicate bug report detector.</p>
<p><strong>Results:</strong> Our approach was evaluated on three projects: Mozilla Firefox, Eclipse Platform and Thunderbird. It achieved an accuracy of 92.11%, 94.08% and 89.03% respectively for Mozilla, Eclipse and Thunderbird. A comparison with a Dual- Channel Convolutional Neural Network (DC-CNN) model and other pre-trained models, including RoBERTa and Sentence-Bert has been conducted. Results showed that BERT-MLP outper- formed, the second best performing model (DC-CNN) by 12% in accuracy for Eclipse and 9% for both Mozilla and Thunderbird, respectively.</p>
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<h3>Related Paper</h3>
<p>Eman Abdullah AlOmar, Anthony Peruma, Mohamed Wiem Mkaouer, Christian Newman, Ali Ouni, and Marouane Kessentini "How we refactor and how we document it? On the use of supervised machine learning algorithms to classify refactoring documentation", the Expert Systems with Applications (ESWA'2020). <a href="./Preprint/ESWA20_preprint.pdf">[preprint]</a></p>
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