专题演讲嘉宾 :郭健美

华东理工大学副教授

郭健美,博士、副教授。主要研究方向在软件工程、人工智能和大数据分析。2017 年入选上海市浦江人才计划(A类)。研究获得 2015 年第 30 届国际自动化软件工程大会 ACM SIGSOFT“杰出论文奖”、2016 年第 20 届国际系统与软件产品线大会“最佳论文奖”、2017 年第 30 届加拿大人工智能大会“最佳论文奖”。

演讲:Data-Efficient Performance Learning for Configurable Systems

Many software systems today are configurable, offering customization of functionality byfeature selection. Understanding how performance varies in terms of featureselection is key for selecting appropriate configurations that meet a set ofgiven requirements. Due to a huge configuration space and the possibly highcost of performance measurement, it is usually not feasible to explore theentire configuration space of a configurable system exhaustively. It is thus amajor challenge to accurately predict performance based on a small sample ofmeasured system variants. To address this challenge, we propose adata-efficient learning approach, called DECART, that combines severaltechniques of machine learning and statistics for performance prediction ofconfigurable systems. DECART builds, validates, and determines a predictionmodel based on an available sample of measured system variants. Empiricalresults on 10 real-world configurable systems demonstrate the effectiveness andpracticality of DECART. In particular, DECART achieves a prediction accuracy of90% or higher based on a small sample, whose size is linear in the number offeatures. In addition, we propose a sample quality metric and introduce aquantitative analysis of the quality of a sample for performance prediction.

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