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。