Data-Driven Extract Method Recommendations: A Study at ING
Thu 26 Aug 2021 04:20 - 04:30 - Analytics & Software Evolution—Libraries and APIs 1 Chair(s): Massimiliano Di Penta
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 AugDisplayed 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 10mPaper | 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 10mPaper | Heuristic and Neural Network based Prediction of Project-Specific API Member Access Journal First | ||
16:20 10mPaper | 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 30mLive Q&A | Q&A (Analytics & Software Evolution—Libraries and APIs 1) Research Papers |
Thu 26 AugDisplayed 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 10mPaper | 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 10mPaper | Heuristic and Neural Network based Prediction of Project-Specific API Member Access Journal First | ||
04:20 10mPaper | 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 30mLive Q&A | Q&A (Analytics & Software Evolution—Libraries and APIs 1) Research Papers |