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ICSE 2020
Wed 24 June - Thu 16 July 2020
Wed 8 Jul 2020 15:16 - 15:24 at Goguryeo - A8-Machine Learning and Models Chair(s): Liliana Pasquale

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 Jul
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15:00 - 16:00
A8-Machine Learning and ModelsJournal First / Technical Papers at Goguryeo
Chair(s): Liliana PasqualeUniversity College Dublin & Lero
15:00
8m
Talk
Improving Vulnerability Inspection Efficiency Using Active LearningJ1
Journal First
Zhe YuNORTH CAROLINA STATE UNIVERSITY, Chris TheisenMicrosoft, Laurie WilliamsNorth Carolina State University, Tim MenziesNorth Carolina State University
15:08
8m
Talk
How Bugs Are Born: A Model to Identify How Bugs Are Introduced in Software ComponentsJ1
Journal First
Gema Rodríguez-PérezUniversity of Waterloo, Canada, Gregorio RoblesUniversidad Rey Juan Carlos, Alexander SerebrenikEindhoven University of Technology, Andy ZaidmanTU Delft, Daniel M. GermanUniversity of Victoria, Jesus M. Gonzalez-BarahonaUniversidad Rey Juan Carlos
DOI Pre-print
15:16
8m
Talk
How to “DODGE” Complex Software AnalyticsJ1
Journal First
Amritanshu AgrawalWayfair, Wei FuLanding AI, Di ChenNorth Carolina State University, USA, Xipeng ShenNorth Carolina State University, Tim MenziesNorth Carolina State University
15:24
12m
Talk
Importance-Driven Deep Learning System TestingTechnical
Technical Papers
Simos GerasimouUniversity of York, UK, Hasan Ferit EniserMPI-SWS, Alper SenBogazici University, Turkey, Alper ÇakanBogazici University, Turkey
15:36
12m
Talk
Quickly Generating Diverse Valid Test Inputs with Reinforcement LearningArtifact ReusableTechnicalArtifact Available
Technical Papers
Sameer ReddyUniversity of California, Berkeley, Caroline LemieuxUniversity of California, Berkeley, Rohan PadhyeCarnegie Mellon University, Koushik SenUniversity of California, Berkeley
15:48
8m
Talk
Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software EngineeringJ1
Journal First
Gopi Krishnan RajbahadurQueen's University, Shaowei WangMississippi State University, Yasutaka KameiKyushu University, Ahmed E. HassanQueen's University