Software issues contain units of work to fix, improve or create new threads
during the development and facilitate communication among the team members.
Assigning an issue to the most relevant team member and determining a category
of an issue is a tedious and challenging task. Wrong classifications cause
delays and rework in the project and trouble among the team members. This
thesis proposes a set of carefully curated linguistic features for shallow
machine learning methods and compares the performance of shallow and ensemble
methods with deep language models. Unlike the state-of-the-art, we assign
issues to four roles (designer, developer, tester, and leader) rather than to
specific individuals or teams to contribute to the generality of our solution.
We also consider the level of experience of the developers to reflect the
industrial practices in our solution formulation. We employ a classification
approach to categorize issues into distinct classes, namely bug, new feature,
improvement, and other. Additionally, we endeavor to further classify bugs
based on the specific type of modification required. We collect and annotate
five industrial data sets from one of the top three global television producers
to evaluate our proposal and compare it with deep language models. Our data
sets contain 5324 issues in total. We show that an ensemble classifier of
shallow techniques achieves 0.92 for issue assignment and 0.90 for issue
classification in accuracy which is statistically comparable to the
state-of-the-art deep language models. The contributions include the public
sharing of five annotated industrial issue data sets, the development of a
clear and comprehensive feature set, the introduction of a novel label set and
the validation of the efficacy of an ensemble classifier of shallow machine
learning techniques.

本论文提出了一种分类方法，使用一系列经过精心筛选的语言特征来识别错误的类型，并将错误分配给设计师、开发人员、测试人员和领导者这四个角色中的一个，表明了其更一般的解决方案。与深层语言模型相比，我们使用浅层机器学习模型和集成方法，并展示其在精度上的可比性。

软件问题的自动分配和分类

Automated Assignment and Classification of Software Issues

Determining the type of kidney stones allows urologists to prescribe a
treatment to avoid recurrence of renal lithiasis. An automated in-vivo
image-based classification method would be an important step towards an
immediate identification of the kidney stone type required as a first phase of
the diagnosis. In the literature it was shown on ex-vivo data (i.e., in very
controlled scene and image acquisition conditions) that an automated kidney
stone classification is indeed feasible. This pilot study compares the kidney
stone recognition performances of six shallow machine learning methods and
three deep-learning architectures which were tested with in-vivo images of the
four most frequent urinary calculi types acquired with an endoscope during
standard ureteroscopies. This contribution details the database construction
and the design of the tested kidney stones classifiers. Even if the best
results were obtained by the Inception v3 architecture (weighted precision,
recall and F1-score of 0.97, 0.98 and 0.97, respectively), it is also shown
that choosing an appropriate colour space and texture features allows a shallow
machine learning method to approach closely the performances of the most
promising deep-learning methods (the XGBoost classifier led to weighted
precision, recall and F1-score values of 0.96). This paper is the first one
that explores the most discriminant features to be extracted from images
acquired during ureteroscopies.

该研究使用 6 种浅层机器学习模型和 3 种深度学习模型对 4 种尿路结石类型的内窥镜图像进行识别，结果显示 Inception v3 模型表现最佳，但对颜色空间和纹理特征的适当选择使得浅层机器学习模型的表现可以接近最优深度学习模型的表现，研究其提出了从尿路镜检查中提取图像特征的方法。