Machine learning applied to software engineering tasks can be improved by hyperparameter optimization, i.e., automatic tools that find good settings for a learner’s control parameters. We show that such hyperparameter optimization can be unnecessarily slow, particularly when the optimizers waste time exploring “redundant tunings”, i.e., pairs of tunings which lead to indistinguishable results. By ignoring redundant tunings, DODGE, a tuning tool, runs orders of magnitude faster, while also generating learners with more accurate predictions than seen in prior state-of-the-art approaches.
This paper was TSE-2019-01-0041.R2, accepted for publication to IEEE TSE, Sept 25, 2019.The paper is on-line at IEEE Xplore at https://ieeexplore.ieee.org/document/8854183. A pre-print is also available at https://arxiv.org/pdf/1902.01838.pdf
Wed 8 JulDisplayed time zone: (UTC) Coordinated Universal Time change
15:00 - 16:00 | A8-Machine Learning and ModelsJournal First / Technical Papers at Goguryeo Chair(s): Liliana Pasquale University College Dublin & Lero | ||
15:00 8mTalk | Improving Vulnerability Inspection Efficiency Using Active LearningJ1 Journal First Zhe Yu NORTH CAROLINA STATE UNIVERSITY, Chris Theisen Microsoft, Laurie Williams North Carolina State University, Tim Menzies North Carolina State University | ||
15:08 8mTalk | How Bugs Are Born: A Model to Identify How Bugs Are Introduced in Software ComponentsJ1 Journal First Gema Rodríguez-Pérez University of Waterloo, Canada, Gregorio Robles Universidad Rey Juan Carlos, Alexander Serebrenik Eindhoven University of Technology, Andy Zaidman TU Delft, Daniel M. German University of Victoria, Jesus M. Gonzalez-Barahona Universidad Rey Juan Carlos DOI Pre-print | ||
15:16 8mTalk | How to “DODGE” Complex Software AnalyticsJ1 Journal First Amritanshu Agrawal Wayfair, Wei Fu Landing AI, Di Chen North Carolina State University, USA, Xipeng Shen North Carolina State University, Tim Menzies North Carolina State University | ||
15:24 12mTalk | Importance-Driven Deep Learning System TestingTechnical Technical Papers Simos Gerasimou University of York, UK, Hasan Ferit Eniser MPI-SWS, Alper Sen Bogazici University, Turkey, Alper Çakan Bogazici University, Turkey | ||
15:36 12mTalk | Quickly Generating Diverse Valid Test Inputs with Reinforcement Learning Technical Papers Sameer Reddy University of California, Berkeley, Caroline Lemieux University of California, Berkeley, Rohan Padhye Carnegie Mellon University, Koushik Sen University of California, Berkeley | ||
15:48 8mTalk | Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software EngineeringJ1 Journal First Gopi Krishnan Rajbahadur Queen's University, Shaowei Wang Mississippi State University, Yasutaka Kamei Kyushu University, Ahmed E. Hassan Queen's University | ||