论文标题
Deepperf：一种基于深度学习的方法，用于改善软件性能
DeepPERF: A Deep Learning-Based Approach For Improving Software Performance
论文作者
论文摘要
改善软件性能是软件开发周期中重要但充满挑战的部分。如今，大多数绩效效率低下是由绩效专家确定和修补的。深度学习方法的最新进展和开源数据的广泛可用性为自动化绩效问题的识别和修补而创造了一个绝佳的机会。在本文中，我们提出了Deepperf，这是一种基于变压器的方法，用于建议用于C＃应用程序的性能改进。我们在英语和源代码语料库上预先介绍了Deepperf，然后进行了Finetuning的任务，以生成C＃应用程序的性能改进补丁。 Our evaluation shows that our model can generate the same performance improvement suggestion as the developer fix in ~53% of the cases, getting ~34% of them verbatim in our expert-verified dataset of performance changes made by C# developers.此外，我们使用基准和单元测试在GitHub上在50个开源C＃存储库上评估Deepperf，并发现我们的模型能够提出有效的性能改进，以改善CPU使用和内存分配。到目前为止，我们已经提交了19个带有28种不同性能优化的拉装要求，其中11个PR已获得项目所有者的批准。
Improving software performance is an important yet challenging part of the software development cycle. Today, the majority of performance inefficiencies are identified and patched by performance experts. Recent advancements in deep learning approaches and the wide-spread availability of open source data creates a great opportunity to automate the identification and patching of performance problems. In this paper, we present DeepPERF, a transformer-based approach to suggest performance improvements for C# applications. We pretrain DeepPERF on English and Source code corpora and followed by finetuning for the task of generating performance improvement patches for C# applications. Our evaluation shows that our model can generate the same performance improvement suggestion as the developer fix in ~53% of the cases, getting ~34% of them verbatim in our expert-verified dataset of performance changes made by C# developers. Additionally, we evaluate DeepPERF on 50 open source C# repositories on GitHub using both benchmark and unit tests and find that our model is able to suggest valid performance improvements that can improve both CPU usage and Memory allocations. So far we've submitted 19 pull-requests with 28 different performance optimizations and 11 of these PRs have been approved by the project owners.
