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ESEC/FSE 2021
Thu 19 - Sat 28 August 2021 Clowdr Platform

The sound identification of refactoring opportunities is still an open problem in software engineering. Recent studies have shown the effectiveness of machine learning models in recommending methods that should undergo different refactoring operations. In this work, we experiment with such approaches to identify methods that should undergo an Extract Method refactoring, in the context of ING, a large financial organization. More specifically, we (i) compare the code metrics distributions, which are used as features by the models, between open-source and ING systems, (ii) measure the accuracy of different machine learning models in recommending Extract Method refactorings, (iii) compare the recommendations given by the models with the opinions of ING experts. Our results show that the feature distributions of ING systems and open-source systems are somewhat different, that machine learning models can recommend Extract Method refactorings with high accuracy, and that experts tend to agree with most of the recommendations of the model.

Wed 25 Aug

Displayed time zone: Athens change

16:00 - 17:00
Analytics & Software Evolution—Libraries and APIs 1Research Papers / Industry Papers / Journal First +12h
Chair(s): Yi Li Nanyang Technological University, Davide Di Ruscio University of L'Aquila
16:00
10m
Paper
Embedding App-Library Graph for Neural Third Party Library Recommendation
Research Papers
Bo Li Swinburne University of Technology, Qiang He Swinburne University of Technology, Feifei Chen Deakin University, Xin Xia Huawei Technologies, Li Li Monash University, John Grundy Monash University, Yun Yang Swinburne University of Technology
DOI
16:10
10m
Paper
Heuristic and Neural Network based Prediction of Project-Specific API Member Access
Journal First
Lin Jiang Beijing Institute of Technology, Hui Liu Beijing Institute of Technology, He Jiang Beijing Institute of Technology, Lu Zhang Peking University, Hong Mei Beijing Institute of Technology
16:20
10m
Paper
Data-Driven Extract Method Recommendations: A Study at ING
Industry Papers
David van der Leij Delft University of Technology; ING, Jasper Binda ING, Robbert van Dalen ING, Pieter Vallen ING, Yaping Luo ING; Eindhoven University of Technology, Maurício Aniche Delft University of Technology
DOI Pre-print
16:30
30m
Live Q&A
Q&A (Analytics & Software Evolution—Libraries and APIs 1)
Research Papers

Thu 26 Aug

Displayed time zone: Athens change

04:00 - 05:00
Analytics & Software Evolution—Libraries and APIs 1Journal First / Research Papers / Industry Papers
Chair(s): Massimiliano Di Penta University of Sannio
04:00
10m
Paper
Embedding App-Library Graph for Neural Third Party Library Recommendation
Research Papers
Bo Li Swinburne University of Technology, Qiang He Swinburne University of Technology, Feifei Chen Deakin University, Xin Xia Huawei Technologies, Li Li Monash University, John Grundy Monash University, Yun Yang Swinburne University of Technology
DOI
04:10
10m
Paper
Heuristic and Neural Network based Prediction of Project-Specific API Member Access
Journal First
Lin Jiang Beijing Institute of Technology, Hui Liu Beijing Institute of Technology, He Jiang Beijing Institute of Technology, Lu Zhang Peking University, Hong Mei Beijing Institute of Technology
04:20
10m
Paper
Data-Driven Extract Method Recommendations: A Study at ING
Industry Papers
David van der Leij Delft University of Technology; ING, Jasper Binda ING, Robbert van Dalen ING, Pieter Vallen ING, Yaping Luo ING; Eindhoven University of Technology, Maurício Aniche Delft University of Technology
DOI Pre-print
04:30
30m
Live Q&A
Q&A (Analytics & Software Evolution—Libraries and APIs 1)
Research Papers