Open-source development has revolutionized the software industry by promoting
collaboration, transparency, and community-driven innovation. Today, a vast
amount of various kinds of open-source software, which form networks of
repositories, is often hosted on GitHub - a popular software development
platform. To enhance the discoverability of the repository networks, i.e.,
groups of similar repositories, GitHub introduced repository topics in 2017
that enable users to more easily explore relevant projects by type, technology,
and more. It is thus crucial to accurately assign topics for each GitHub
repository. Current methods for automatic topic recommendation rely heavily on
TF-IDF for encoding textual data, presenting challenges in understanding
semantic nuances. This paper addresses the limitations of existing techniques
by proposing Legion, a novel approach that leverages Pre-trained Language
Models (PTMs) for recommending topics for GitHub repositories. The key novelty
of Legion is three-fold. First, Legion leverages the extensive capabilities of
PTMs in language understanding to capture contextual information and semantic
meaning in GitHub repositories. Second, Legion overcomes the challenge of
long-tailed distribution, which results in a bias toward popular topics in
PTMs, by proposing a Distribution-Balanced Loss (DB Loss) to better train the
PTMs. Third, Legion employs a filter to eliminate vague recommendations,
thereby improving the precision of PTMs. Our empirical evaluation on a
benchmark dataset of real-world GitHub repositories shows that Legion can
improve vanilla PTMs by up to 26% on recommending GitHubs topics. Legion also
can suggest GitHub topics more precisely and effectively than the
state-of-the-art baseline with an average improvement of 20% and 5% in terms of
Precision and F1-score, respectively.

开源开发背书了软件行业，“Legion” 是一个使用预训练语言模型为 GitHub 仓库推荐主题的新方法，通过利用 PTM 的广泛能力来捕捉 GitHub 仓库的上下文信息和语义含义，通过提出分布平衡损失来解决 PTM 中偏向热门主题的挑战，并通过过滤器消除模糊的建议，从而改进了 PTM 的精度。在实际基准数据集上的实证评估表明，“Legion” 在推荐 GitHub 主题方面比原始 PTM 提高了 26%，在精确度和 F1 得分方面平均提高了 20% 和 5%。

LEGION: 利用预训练语言模型进行 GitHub 主题推荐的分布平衡损失

LEGION: Harnessing Pre-trained Language Models for GitHub Topic  Recommendations with Distribution-Balance Loss

While many organizations have shifted to working remotely during the COVID-19
pandemic, how the remote workforce and the remote teams are influenced by and
would respond to this and future shocks remain largely unknown. Software
developers have relied on remote collaborations long before the pandemic,
working in virtual teams (GitHub repositories). The dynamics of these
repositories through the pandemic provide a unique opportunity to understand
how remote teams react under shock. This work presents a systematic analysis.
We measure the overall effect of the early pandemic on public GitHub
repositories by comparing their sizes and productivity with the counterfactual
outcomes forecasted as if there were no pandemic. We find that the productivity
level and the number of active members of these teams vary significantly during
different periods of the pandemic. We then conduct a finer-grained
investigation and study the heterogeneous effects of the shock on individual
teams. We find that the resilience of a team is highly correlated to certain
properties of the team before the pandemic. Through a bootstrapped regression
analysis, we reveal which types of teams are robust or fragile to the shock.

本篇论文通过对早期疫情时期远程软件开发团队在 GitHub 上的仓库进行系统分析，发现了疫情对这些团队的生产力和活跃成员数量产生的显著影响。并进一步研究了事件对于各个团队的异质性影响，发现在疫情前团队的某些特点与其韧性高度相关，并通过靴带回归分析揭示出哪些团队对于如此的冲击更具韧性和稳健性。

早期 COVID-19 大流行期间的 GitHub 代码库团队韧性实证分析

Team Resilience under Shock: An Empirical Analysis of GitHub  Repositories during Early COVID-19 Pandemic

Detecting and fixing bugs are two of the most important yet frustrating parts
of the software development cycle. Existing bug detection tools are based
mainly on static analyzers, which rely on mathematical logic and symbolic
reasoning about the program execution to detect common types of bugs. Fixing
bugs is typically left out to the developer. In this work we introduce
DeepDebug: a data-driven program repair approach which learns to detect and fix
bugs in Java methods mined from real-world GitHub repositories. We frame
bug-patching as a sequence-to-sequence learning task consisting of two steps:
(i) denoising pretraining, and (ii) supervised finetuning on the target
translation task. We show that pretraining on source code programs improves the
number of patches found by 33% as compared to supervised training from scratch,
while domain-adaptive pretraining from natural language to code further
improves the accuracy by another 32%. We refine the standard accuracy
evaluation metric into non-deletion and deletion-only fixes, and show that our
best model generates 75% more non-deletion fixes than the previous state of the
art. In contrast to prior work, we attain our best results when generating raw
code, as opposed to working with abstracted code that tends to only benefit
smaller capacity models. Finally, we observe a subtle improvement from adding
syntax embeddings along with the standard positional embeddings, as well as
with adding an auxiliary task to predict each token's syntactic class. Despite
focusing on Java, our approach is language agnostic, requiring only a
general-purpose parser such as tree-sitter.

DeepDebug 是数据驱动的程序修复方法，其可以从 GitHub 代码库中挖掘的实际 Java 方法中学习检测和修复错误的方法。通过将错误修补构建为序列到序列的学习任务，它实现了两个步骤，即去噪预训练和有监督的微调。本方法显示出比以前的工作更好的异常检测能力，并且可以使用通用解析器进行语言无关的方法。