Generative AI is changing the way developers interact with software systems,
providing services that can produce and deliver new content, crafted to satisfy
the actual needs of developers. For instance, developers can ask for new code
directly from within their IDEs by writing natural language prompts, and
integrated services based on generative AI, such as Copilot, immediately
respond to prompts by providing ready-to-use code snippets. Formulating the
prompt appropriately, and incorporating the useful information while avoiding
any information overload, can be an important factor in obtaining the right
piece of code. The task of designing good prompts is known as prompt
engineering. In this paper, we systematically investigate the influence of
eight prompt features on the style and the content of prompts, on the level of
correctness, complexity, size, and similarity to the developers' code of the
generated code. We specifically consider the task of using Copilot with 124,800
prompts obtained by systematically combining the eight considered prompt
features to generate the implementation of 200 Java methods. Results show how
some prompt features, such as the presence of examples and the summary of the
purpose of the method, can significantly influence the quality of the result.

研究调查了 8 个提示特征对生成代码的风格、内容、正确性、复杂性、大小和与开发人员代码的相似性的影响，并考虑了在使用 Copilot 生成 200 个 Java 方法的 124,800 个提示的任务结果表明，某些提示特征，如示例的存在和方法目的的总结，可以显著影响结果的质量。

分析提示在自动生成方法中的影响：一项基于 Copilot 的实证研究

Analyzing Prompt Influence on Automated Method Generation: An Empirical  Study with Copilot

Automatic source code analysis in key areas of software engineering, such as
code security, can benefit from Machine Learning (ML). However, many standard
ML approaches require a numeric representation of data and cannot be applied
directly to source code. Thus, to enable ML, we need to embed source code into
numeric feature vectors while maintaining the semantics of the code as much as
possible. code2vec is a recently released embedding approach that uses the
proxy task of method name prediction to map Java methods to feature vectors.
However, experimentation with code2vec shows that it learns to rely on variable
names for prediction, causing it to be easily fooled by typos or adversarial
attacks. Moreover, it is only able to embed individual Java methods and cannot
embed an entire collection of methods such as those present in a typical Java
class, making it difficult to perform predictions at the class level (e.g., for
the identification of malicious Java classes). Both shortcomings are addressed
in the research presented in this paper. We investigate the effect of
obfuscating variable names during the training of a code2vec model to force it
to rely on the structure of the code rather than specific names and consider a
simple approach to creating class-level embeddings by aggregating sets of
method embeddings. Our results, obtained on a challenging new collection of
source-code classification problems, indicate that obfuscating variable names
produces an embedding model that is both impervious to variable naming and more
accurately reflects code semantics. The datasets, models, and code are shared
for further ML research on source code.

本论文研究使用机器学习进行源码分析的问题，提出解决标准机器学习方法不能直接用于源代码的问题。作者通过使用 code2vec 嵌入方法来解决问题，但是实验结果表明，变量名对预测结果有较大影响。因此，作者提出了对变量名进行混淆的方法，进一步提高了嵌入模型的准确性，并探索了一种方法将类别层面的信息整合到嵌入模型中。研究结果在源代码分类方面表现出了较好的性能。