标题： 使用深度学习解释性能曲线 显示英文标题

标题： Interpreting Performance Profiles with Deep Learning

摘要： 性能分析工具（也称为分析器）在理解运行时程序性能方面起着重要作用，例如热点、瓶颈和低效之处。 虽然分析器已被证明是有用的，但它们给软件工程师带来了额外的负担。 软件工程师作为用户，负责解释复杂的性能数据并识别程序源代码中的可操作优化。 然而，对于用户来说，将低效之处与程序语义联系起来可能具有挑战性，特别是如果用户不是代码的作者，这限制了分析器的适用性。 在本论文中，我们探索了一种新的方向，即结合性能分析和程序语义，并采用深度学习方法。 核心思想是提取代码摘要以获取语义信息（在一定层次上），并将其集成到分析器中，从而更好地理解程序低效之处以进行可操作的优化。 具体来说，我们将由Async Profiler（最先进的Java分析器）生成的分析结果与微调的基于CodeBERT的代码摘要相结合。 我们在图形用户界面中展示了任何选定调用路径的代码摘要。 我们的系统可以有效协助许多Java基准测试的分析。 显示英文摘要

摘要： Profiling tools (also known as profilers) play an important role in understanding program performance at runtime, such as hotspots, bottlenecks, and inefficiencies. While profilers have been proven to be useful, they give extra burden to software engineers. Software engineers, as the users, are responsible to interpret the complex performance data and identify actionable optimization in program source code. However, it can be challenging for users to associate inefficiencies with the program semantics, especially if the users are not the authors of the code, which limits the applicability of profilers. In this thesis, we explore a new direction to combine performance profiles and program semantics with a deep learning approach. The key idea is to glean code summary for semantic information (at a certain level) and integrate it into a profiler, which can better understand program inefficiencies for actionable optimization. To be concrete, we combine profiles generated by Async Profiler (the state-of-the-art Java profiler) with code summarization from a fine-tuned CodeBERT-based model. We demonstrate the code summaries of any selected call path in a graphic user interface. Our system can effectively assist analysis on many Java benchmarks.