research-article

More accurate recommendations for method-level changes

Authors:

Patrick Kreutzer,

Michael PhilippsenAuthors Info & Claims

ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

Pages 798 - 808

https://doi.org/10.1145/3106237.3106276

Published: 21 August 2017 Publication History

Abstract

During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations. Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations. In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples. Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments.

We present the Accurate REcommendation System (ARES) that achieves a higher accuracy than other tools because its algorithms take care of code movements when creating patterns and recommendations. On average, the recommendations by ARES have an accuracy of 96% with respect to code changes that developers have manually performed in commits of source code archives. At the same time ARES achieves precision and recall values that are on par with other tools.

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Cited By

Farah PSilva RVergilio S(2023)An Assessment of Machine Learning Algorithms and Models for Prediction of Change-Prone Java MethodsProceedings of the XXXVII Brazilian Symposium on Software Engineering10.1145/3613372.3613395(322-331)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1145/3613372.3613395
Dilhara MKetkar ADig D(2021)Understanding Software-2.0ACM Transactions on Software Engineering and Methodology10.1145/345347830:4(1-42)Online publication date: 23-Jul-2021
https://dl.acm.org/doi/10.1145/3453478
Mader PKuschke TJanke M(2021)Reactive Auto-Completion of Modeling ActivitiesIEEE Transactions on Software Engineering10.1109/TSE.2019.292488647:7(1431-1451)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TSE.2019.2924886
Show More Cited By

Index Terms

More accurate recommendations for method-level changes
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
2. Software and its engineering
  1. Software notations and tools
    1. Software maintenance tools

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Published In

cover image ACM Conferences

ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

August 2017

1073 pages

ISBN:9781450351058

DOI:10.1145/3106237

General Chairs:
Eric Bodden
Paderborn University, Germany / Fraunhofer IEM, Germany
,
Wilhelm Schäfer
Paderborn University, Germany
,
Program Chairs:
Arie van Deursen
Delft University of Technology, Netherlands
,
Andrea Zisman
Open University, UK

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 21 August 2017

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ESEC/FSE'17

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SIGSOFT

ESEC/FSE'17: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

September 4 - 8, 2017

Paderborn, Germany

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Overall Acceptance Rate 112 of 543 submissions, 21%

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Cited By

Farah PSilva RVergilio S(2023)An Assessment of Machine Learning Algorithms and Models for Prediction of Change-Prone Java MethodsProceedings of the XXXVII Brazilian Symposium on Software Engineering10.1145/3613372.3613395(322-331)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1145/3613372.3613395
Dilhara MKetkar ADig D(2021)Understanding Software-2.0ACM Transactions on Software Engineering and Methodology10.1145/345347830:4(1-42)Online publication date: 23-Jul-2021
https://dl.acm.org/doi/10.1145/3453478
Mader PKuschke TJanke M(2021)Reactive Auto-Completion of Modeling ActivitiesIEEE Transactions on Software Engineering10.1109/TSE.2019.292488647:7(1431-1451)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TSE.2019.2924886
Noda KYokoyama HKikuchi S(2021)Sirius: Static Program Repair with Dependence Graph-Based Systematic Edit Patterns2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME52107.2021.00045(437-447)Online publication date: Sep-2021
https://doi.org/10.1109/ICSME52107.2021.00045
Fazzini MXin QOrso ALo DMariani LMesbah A(2020)APIMigratorProceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems10.1145/3387905.3388608(77-80)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.1145/3387905.3388608
Li HXie RKong XWang LLi B(2020)An Analysis of Utility for API Recommendation: Do the Matched Results Have the Same Efforts?2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS51102.2020.00067(479-488)Online publication date: Dec-2020
https://doi.org/10.1109/QRS51102.2020.00067
Fazzini MXin QOrso AZhang DMøller A(2019)Automated API-usage update for Android appsProceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3293882.3330571(204-215)Online publication date: 10-Jul-2019
https://dl.acm.org/doi/10.1145/3293882.3330571
Silva RRoy CRahman MSchneider KPaixao Kde Almeida Maia MGuéhéneuc YKhomh FSarro F(2019)Recommending comprehensive solutions for programming tasks by mining crowd knowledgeProceedings of the 27th International Conference on Program Comprehension10.1109/ICPC.2019.00054(358-368)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICPC.2019.00054

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