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ICSE 2021
Mon 17 May - Sat 5 June 2021

As software systems grow in complexity and the space of possible configurations increases exponentially, finding the near-optimal configuration of a software system becomes challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, collecting enough sample configurations can be very expensive since each such sample requires configuring, compiling, and executing the entire system using a complex test suite. When learning on new data is too expensive, it is possible to use \textit{Transfer Learning} to “transfer” old lessons to the new context. Traditional transfer learning has a number of challenges, specifically, (a) learning from excessive data takes excessive time, and (b) the performance of the models built via transfer can deteriorate as a result of learning from a poor source. To resolve these problems, we propose a novel transfer learning framework called BEETLE, which is a “bellwether”-based transfer learner that focuses on identifying and learning from the most relevant source from amongst the old data. This paper evaluates BEETLE with 57 different software configuration problems based on five software systems (a video encoder, an SAT solver, a SQL database, a high-performance C-compiler, and a streaming data analytics tool). In each of these cases, BEETLE found configurations that are as good as or better than those found by other state-of-the-art transfer learners while requiring only a fraction (17th) of the measurements needed by those other methods. Based on these results, we say that BEETLE is a new high-water mark in optimally configuring software.

Conference Day
Fri 28 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:05 - 16:05
4.3.4. Configuration of Software Systems: OptimizationJournal-First Papers / Technical Track at Blended Sessions Room 4 +12h
Chair(s): Sergio SeguraUniversidad de Sevilla
15:05
20m
Paper
Resource-Guided Configuration Space Reduction for Deep Learning ModelsTechnical Track
Technical Track
Yanjie GaoMicrosoft Research, Yonghao ZhuMicrosoft Research, Hongyu ZhangThe University of Newcastle, Haoxiang LinMicrosoft Research, Mao YangMicrosoft Research
Link to publication DOI Pre-print Media Attached
15:25
20m
Paper
ConfigMiner: Identifying the Appropriate Configuration Options for Config-related User Questions by Mining Online ForumsJournal-First
Journal-First Papers
Mohammed SayaghETS Montreal, University of Quebec, Ahmed E. HassanSchool of Computing, Queen's University
Link to publication DOI Pre-print
15:45
20m
Paper
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLEJournal-First
Journal-First Papers
Rahul KrishnaColumbia University, USA, Vivek NairFacebook, USA, Pooyan JamshidiUniversity of South Carolina, Tim MenziesNorth Carolina State University, USA
Link to publication DOI Pre-print Media Attached

Conference Day
Sat 29 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

03:05 - 04:05
4.3.4. Configuration of Software Systems: OptimizationJournal-First Papers / Technical Track at Blended Sessions Room 4
03:05
20m
Paper
Resource-Guided Configuration Space Reduction for Deep Learning ModelsTechnical Track
Technical Track
Yanjie GaoMicrosoft Research, Yonghao ZhuMicrosoft Research, Hongyu ZhangThe University of Newcastle, Haoxiang LinMicrosoft Research, Mao YangMicrosoft Research
Link to publication DOI Pre-print Media Attached
03:25
20m
Paper
ConfigMiner: Identifying the Appropriate Configuration Options for Config-related User Questions by Mining Online ForumsJournal-First
Journal-First Papers
Mohammed SayaghETS Montreal, University of Quebec, Ahmed E. HassanSchool of Computing, Queen's University
Link to publication DOI Pre-print
03:45
20m
Paper
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLEJournal-First
Journal-First Papers
Rahul KrishnaColumbia University, USA, Vivek NairFacebook, USA, Pooyan JamshidiUniversity of South Carolina, Tim MenziesNorth Carolina State University, USA
Link to publication DOI Pre-print Media Attached