Automatic program repair (APR) is crucial to reduce manual debugging efforts
for developers and improve software reliability. While conventional
search-based techniques typically rely on heuristic rules or a redundancy
assumption to mine fix patterns, recent years have witnessed the surge of deep
learning (DL) based approaches to automate the program repair process in a
data-driven manner. However, their performance is often limited by a fixed set
of parameters to model the highly complex search space of APR. To ease such
burden on the parametric models, in this work, we propose a novel
Retrieval-Augmented Patch Generation framework (RAP-Gen) by explicitly
leveraging relevant fix patterns retrieved from a codebase of previous bug-fix
pairs. Specifically, we build a hybrid patch retriever to account for both
lexical and semantic matching based on the raw source code in a
language-agnostic manner, which does not rely on any code-specific features. In
addition, we adapt a code-aware language model CodeT5 as our foundation model
to facilitate both patch retrieval and generation tasks in a unified manner. We
adopt a stage-wise approach where the patch retriever first retrieves a
relevant external bug-fix pair to augment the buggy input for the CodeT5 patch
generator, which synthesizes a ranked list of repair patch candidates. Notably,
RAP-Gen is a generic APR framework that can flexibly integrate different patch
retrievers and generators to repair various types of bugs. We thoroughly
evaluate RAP-Gen on three benchmarks in two programming languages, including
the TFix benchmark in JavaScript, and Code Refinement and Defects4J benchmarks
in Java, where the bug localization information may or may not be provided.
Experimental results show that RAP-Gen significantly outperforms previous
state-of-the-art approaches on all benchmarks, e.g., repairing 15 more bugs on
818 Defects4J bugs.

自动程序修复是减少开发人员手动调试努力并提高软件可靠性的关键。本文提出了一种新颖的检索增强型修补生成框架（RAP-Gen），通过明确利用从以前的错误修复对的代码库中检索出的相关修复模式来简化参数模型的负担，进而自动化程序修复过程。实验结果表明，RAP-Gen 在所有基准测试中显著优于先前的最先进方法，例如在 818 个 Defects4J 错误上修复了 15 个更多的错误。

RAP-Gen：用 CodeT5 进行检索增强的修补补丁生成自动程序修复

RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic  Program Repair

Health experts assert that hope plays a crucial role in enhancing
individuals' physical and mental well-being, facilitating their recovery, and
promoting restoration. Hope speech refers to comments, posts and other social
media messages that offer support, reassurance, suggestions, inspiration, and
insight. The detection of hope speech involves the analysis of such textual
content, with the aim of identifying messages that invoke positive emotions in
people. Our study aims to find computationally efficient yet
comparable/superior methods for hope speech detection. We also make our
codebase public at this https URL

研究旨在寻找计算效率更高、可比较 / 优于现有方法的希望言论检测方法，并公开我们的代码库。

超越负面性：关于希望言论检测的重新分析和后续实验

Beyond Negativity: Re-Analysis and Follow-Up Experiments on Hope Speech  Detection

Politically sensitive topics are still a challenge for open-domain chatbots.
However, dealing with politically sensitive content in a responsible,
non-partisan, and safe behavior way is integral for these chatbots. Currently,
the main approach to handling political sensitivity is by simply changing such
a topic when it is detected. This is safe but evasive and results in a chatbot
that is less engaging. In this work, as a first step towards a politically safe
chatbot, we propose a group of metrics for assessing their political prudence.
We then conduct political prudence analysis of various chatbots and discuss
their behavior from multiple angles through our automatic metric and human
evaluation metrics. The testsets and codebase are released to promote research
in this area.

本研究提出了一种评估聊天机器人政治谨慎性的度量体系，并通过自动和人工评估指标进行政治谨慎性分析，释放了测试集和代码，以促进该领域的研究。