The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In this paper, we apply MP to the automation of software performance regression testing. A performance regression is a software performance degradation caused by a code change. We present AutoPerf - a novel approach to automate regression testing that utilizes three core techniques: (i) zero-positive learning, (ii) autoencoders, and (iii) hardware telemetry. We demonstrate AutoPerf's generality and efficacy against 3 types of performance regressions across 10 real performance bugs in 7 benchmark and open-source programs. On average, AutoPerf exhibits 4% profiling overhead and accurately diagnoses more performance bugs than prior state-of-the-art approaches. Thus far, AutoPerf has produced no false negatives.

中文翻译：

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一种用于诊断软件性能回归的零正学习方法

机器编程 (MP) 领域，软件开发的自动化，正在取得显着的研究进展。这部分是由于机器学习中出现了广泛的新技术。在本文中，我们将 MP 应用于软件性能回归测试的自动化。性能回归是由代码更改引起的软件性能下降。我们提出 AutoPerf - 一种利用三种核心技术实现自动化回归测试的新方法：(i) 零正学习，(ii) 自动编码器，以及 (iii) 硬件遥测。我们在 7 个基准测试和开源程序中的 10 个实际性能错误中针对 3 种类型的性能回归证明了 AutoPerf 的通用性和有效性。一般，AutoPerf 展示了 4% 的分析开销，并且比之前的最先进方法准确地诊断出更多的性能错误。到目前为止，AutoPerf 没有产生假阴性。