Flaky tests can pass or fail non-deterministically, without alterations to a
software system. Such tests are frequently encountered by developers and hinder
the credibility of test suites. State-of-the-art research incorporates machine
learning solutions into flaky test detection and achieves reasonably good
accuracy. Moreover, the majority of automated flaky test repair solutions are
designed for specific types of flaky tests. This research work proposes a novel
categorization framework, called FlaKat, which uses machine-learning
classifiers for fast and accurate prediction of the category of a given flaky
test that reflects its root cause. Sampling techniques are applied to address
the imbalance between flaky test categories in the International Dataset of
Flaky Test (IDoFT). A new evaluation metric, called Flakiness Detection
Capacity (FDC), is proposed for measuring the accuracy of classifiers from the
perspective of information theory and provides proof for its effectiveness. The
final FDC results are also in agreement with F1 score regarding which
classifier yields the best flakiness classification.

该研究提出了一个名为 FlaKat 的新分类框架，利用机器学习分类器快速准确地预测给定的易崩溃测试的类别，反映其根本原因，并提出了一种新的评估指标 FDC，用于从信息论的角度衡量分类器的准确性。最终的 FDC 结果与 F1 得分一致，确定了能够产生最佳易崩溃分类的分类器。

FlaKat: 一个基于机器学习的一致性测试分类框架

FlaKat: A Machine Learning-Based Categorization Framework for Flaky  Tests

Due to rapid advancements in the development of Large Language Models (LLMs),
programming these models with prompts has recently gained significant
attention. However, the sheer number of available prompt engineering techniques
creates an overwhelming landscape for practitioners looking to utilize these
tools. For the most efficient and effective use of LLMs, it is important to
compile a comprehensive list of prompting techniques and establish a
standardized, interdisciplinary categorization framework. In this survey, we
examine some of the most well-known prompting techniques from both academic and
practical viewpoints and classify them into seven distinct categories. We
present an overview of each category, aiming to clarify their unique
contributions and showcase their practical applications in real-world examples
in order to equip fellow practitioners with a structured framework for
understanding and categorizing prompting techniques tailored to their specific
domains. We believe that this approach will help simplify the complex landscape
of prompt engineering and enable more effective utilization of LLMs in various
applications. By providing practitioners with a systematic approach to prompt
categorization, we aim to assist in navigating the intricacies of effective
prompt design for conversational pre-trained LLMs and inspire new possibilities
in their respective fields.

采用系统性方法对大型语言模型的提示技术进行分类，通过举例说明实际应用，为从业人员提供一个结构化的理解和分类提示技术的框架，以便更有效地利用大型语言模型的各种应用领域。