demonstration

LINKSTER: enabling efficient manual inspection and annotation of mined data

Authors:

Christian Bird,

Adrian Bachmann,

Foyzur Rahman,

Abraham BernsteinAuthors Info & Claims

FSE '10: Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering

Pages 369 - 370

https://doi.org/10.1145/1882291.1882352

Published: 07 November 2010 Publication History

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Abstract

While many uses of mined software engineering data are automatic in nature, some techniques and studies either require, or can be improved, by manual methods. Unfortunately, manually inspecting, analyzing, and annotating mined data can be difficult and tedious, especially when information from multiple sources must be integrated. Oddly, while there are numerous tools and frameworks for automatically mining and analyzing data, there is a dearth of tools which facilitate manual methods. To fill this void, we have developed LINKSTER, a tool which integrates data from bug databases, source code repositories, and mailing list archives to allow manual inspection and annotation. LINKSTER has already been used successfully by an OSS project lead to obtain data for one empirical study.

References

[1]

J. Śliwerski, T. Zimmermann, and A. Zeller, "When do changes induce fixes?" in Proc. of the international workshop on Mining software repositories, 2005.

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[2]

C. Bird, A. Gourley, and P. Devanbu, "Detecting Patch Submission and Acceptance in OSS Projects," in Proc. of the International Workshop on Mining Software Repositories, 2007.

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[3]

M. Cataldo, P. Wagstrom, J. Herbsleb, and K. Carley, "Identification of coordination requirements: implications for the Design of collaboration and awareness tools," Proc. of the 20th conference on Computer supported cooperative work, 2006.

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[4]

A. Bachmann, C. Bird, F. Rahman, P. Devanbu, and A. Bernstein, "The Missing Links: Bugs and Bug-fix Commits," in Proc. of the 16th Symposium on Foundations of Software Engineering, 2010.

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Cited By

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Jian WJun YGuohui LJianhong GLijun Q(2024)Research on Scenario-Based Dynamic Inspection Methodology Using Expression Engine2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)10.23919/ITUK62727.2024.10772839(1-8)Online publication date: 21-Oct-2024
https://doi.org/10.23919/ITUK62727.2024.10772839
Marty BGaudin RPiperno TRouquette DSchwob CMezeix L(2024)Methodology to classify high voltage transmission poles using CNN approach from satellite images for safety public regulation application: Study case of rural area in ThailandSystems and Soft Computing10.1016/j.sasc.2024.200080(200080)Online publication date: Mar-2024
https://doi.org/10.1016/j.sasc.2024.200080
Zakarea Alshara Salman HShatnawi ASeriai A(2023)ML-Augmented Automation for Recovering Links Between Pull-Requests and Issues on GitHubIEEE Access10.1109/ACCESS.2023.323639211(5596-5608)Online publication date: 2023
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Index Terms

LINKSTER: enabling efficient manual inspection and annotation of mined data
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
        Software product lines

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Information

Published In

FSE '10: Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering

November 2010

302 pages

ISBN:9781605587912

DOI:10.1145/1882291

General Chair:
Gruia-Catalin Roman
Washington University in St. Louis, USA
,
Program Chair:
André van der Hoek
University of California, Irvine, USA

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Association for Computing Machinery

New York, NY, United States

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Published: 07 November 2010

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SIGSOFT

SIGSOFT/FSE'10: 18th ACM SIGSOFT Symposium on the Foundations of Software Engineering

November 7 - 11, 2010

New Mexico, Santa Fe, USA

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Overall Acceptance Rate 17 of 128 submissions, 13%

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Jian WJun YGuohui LJianhong GLijun Q(2024)Research on Scenario-Based Dynamic Inspection Methodology Using Expression Engine2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)10.23919/ITUK62727.2024.10772839(1-8)Online publication date: 21-Oct-2024
https://doi.org/10.23919/ITUK62727.2024.10772839
Marty BGaudin RPiperno TRouquette DSchwob CMezeix L(2024)Methodology to classify high voltage transmission poles using CNN approach from satellite images for safety public regulation application: Study case of rural area in ThailandSystems and Soft Computing10.1016/j.sasc.2024.200080(200080)Online publication date: Mar-2024
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Zakarea Alshara Salman HShatnawi ASeriai A(2023)ML-Augmented Automation for Recovering Links Between Pull-Requests and Issues on GitHubIEEE Access10.1109/ACCESS.2023.323639211(5596-5608)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3236392
Alshara ZShatnawi AEyal-Salman HSeriai AShatnawi M(2023)PI-Link: A Ground-Truth Dataset of Links Between Pull-Requests and Issues in GitHubIEEE Access10.1109/ACCESS.2022.323298211(697-710)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3232982
Lan JGong LZhang JZhang H(2023)BTLink : automatic link recovery between issues and commits based on pre-trained BERT modelEmpirical Software Engineering10.1007/s10664-023-10342-728:4Online publication date: 12-Jul-2023
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Li CZhao YYang Y(2022)An Empirical Study of the Bug Link Rate2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS57517.2022.00028(177-188)Online publication date: Dec-2022
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Kondo MKashiwa YKamei YMizuno O(2022)An empirical study of issue-link algorithms: which issue-link algorithms should we use?Empirical Software Engineering10.1007/s10664-022-10120-x27:6Online publication date: 1-Nov-2022
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Xie RChen LYe WLi ZHu TDu DZhang S(2019)DeepLink: A Code Knowledge Graph Based Deep Learning Approach for Issue-Commit Link Recovery2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER.2019.8667969(434-444)Online publication date: Feb-2019
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Liu XHuang LLi CNg V(2018)Linking Source Code to Untangled Change Intents2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2018.00047(393-403)Online publication date: Sep-2018
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An Analytical Study on Frequent Itemset Mining Algorithms

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