Flaky tests are problematic because they non-deterministically pass or fail
for the same software version under test, causing confusion and wasting
developer time. While machine learning models have been used to predict
flakiness and its root causes, there is less work on providing support to fix
the problem. To address this gap, we propose a framework that automatically
generates labeled datasets for 13 fix categories and train models to predict
the fix category of a flaky test by analyzing the test code only. Though it is
unrealistic at this stage to accurately predict the fix itself, the categories
provide precise guidance about what part of the test code to look at. Our
approach is based on language models, namely CodeBERT and UniXcoder, whose
output is fine-tuned with a Feed Forward Neural Network (FNN) or a Siamese
Network-based Few Shot Learning (FSL). Our experimental results show that
UniXcoder outperforms CodeBERT, in correctly predicting most of the categories
of fixes a developer should apply. Furthermore, FSL does not appear to have any
significant effect. Given the high accuracy obtained for most fix categories,
our proposed framework has the potential to help developers to fix flaky tests
quickly and accurately.To aid future research, we make our automated labeling
tool, dataset, prediction models, and experimental infrastructure publicly
available.

论文提出一种基于机器学习的框架，通过对测试代码进行分析并使用语言模型 CodeBERT 和 UniXcoder，训练模型以预测问题的修复类别，提供快速准确的修复支持。实验结果表明，UniXcoder 可以比 CodeBERT 更准确地预测开发者应该应用的大部分修复类别。

使用语言模型对脆弱测试修复类别进行黑盒预测

Black-Box Prediction of Flaky Test Fix Categories Using Language Models

In this paper, we define a neuro-symbolic approach to address the task of
finding semantically similar clones for the codes of the legacy programming
language COBOL, without training data. We define a meta-model that is
instantiated to have an Intermediate Representation (IR) in the form of
Abstract Syntax Trees (ASTs) common across codes in C and COBOL. We linearize
the IRs using Structure Based Traversal (SBT) to create sequential inputs. We
further fine-tune UnixCoder, the best-performing model for zero-shot
cross-programming language code search, for the Code Cloning task with the SBT
IRs of C code-pairs, available in the CodeNet dataset. This allows us to learn
latent representations for the IRs of the C codes, which are transferable to
the IRs of the COBOL codes. With this fine-tuned UnixCoder, we get a
performance improvement of 12.85 MAP@2 over the pre-trained UniXCoder model, in
a zero-shot setting, on the COBOL test split synthesized from the CodeNet
dataset. This demonstrates the efficacy of our meta-model based approach to
facilitate cross-programming language transfer.

本文提出了神经符号方法，通过中间表示（IR）和 UnixCoder 优化，实现了 COBOL 代码克隆任务，取得了 12.85 MAP@2 的表现提升。

基于神经符号和跨语言中间表示的零 - shot 代码克隆

Neuro-symbolic Zero-Shot Code Cloning with Cross-Language Intermediate  Representation

Pre-trained models for programming languages have recently demonstrated great
success on code intelligence. To support both code-related understanding and
generation tasks, recent works attempt to pre-train unified encoder-decoder
models. However, such encoder-decoder framework is sub-optimal for
auto-regressive tasks, especially code completion that requires a decoder-only
manner for efficient inference. In this paper, we present UniXcoder, a unified
cross-modal pre-trained model for programming language. The model utilizes mask
attention matrices with prefix adapters to control the behavior of the model
and leverages cross-modal contents like AST and code comment to enhance code
representation. To encode AST that is represented as a tree in parallel, we
propose a one-to-one mapping method to transform AST in a sequence structure
that retains all structural information from the tree. Furthermore, we propose
to utilize multi-modal contents to learn representation of code fragment with
contrastive learning, and then align representations among programming
languages using a cross-modal generation task. We evaluate UniXcoder on five
code-related tasks over nine datasets. To further evaluate the performance of
code fragment representation, we also construct a dataset for a new task,
called zero-shot code-to-code search. Results show that our model achieves
state-of-the-art performance on most tasks and analysis reveals that comment
and AST can both enhance UniXcoder.

本文提出了一种统一的跨模态预训练模型 UniXcoder，使用前缀适配器控制模型行为并使用语法树和代码注释等交叉模态内容来增强代码表示。同时，利用多模态内容进行对比学习以学习代码片段代表，并使用跨模态生成任务在编程语言之间进行表示的对齐。该模型在五个代码相关任务上取得了最先进的结果，而注释和 AST 可以增强该模型。