Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Cohort Studies for Mining Software Repositories
SAARIMÄKI, Nyyti; Vegas, Sira; Lenarduzzi, Valentina et al.
2024In Proceedings - 2024 IEEE/ACM 21st International Conference on Mining Software Repositories, MSR 2024
Peer reviewed
 

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Keywords :
Cohort Study; Empirical Software Engineering; MSR; Cohort studies; Mining software; Software repositories; Computer Science Applications; Software; Safety, Risk, Reliability and Quality
Abstract :
[en] Mining Software Repositories studies have become increasingly popular over the years. However, a notable limitation is that they report correlational relationships rather than establishing causation. In contrast, certain disciplines (e.g. epidemiology) have developed specific methods to address this limitation. The goal of this tutorial is to introduce participants to one such method: cohort studies. By the end of the tutorial, participants will be familiar with the steps and techniques involved in designing and analyzing cohort studies.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SVV - Software Verification and Validation
Disciplines :
Computer science
Author, co-author :
SAARIMÄKI, Nyyti  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV
Vegas, Sira ;  Universidad Politécnica de Madrid, Spain
Lenarduzzi, Valentina ;  University of Oulu, Finland
Taibi, Davide ;  University of Oulu, Finland
Robredo, Mikel ;  University of Oulu, Finland
External co-authors :
yes
Language :
English
Title :
Cohort Studies for Mining Software Repositories
Publication date :
02 July 2024
Event name :
Proceedings of the 21st International Conference on Mining Software Repositories
Event place :
Lisbon, Prt
Event date :
15-04-2024 => 16-04-2024
Main work title :
Proceedings - 2024 IEEE/ACM 21st International Conference on Mining Software Repositories, MSR 2024
Publisher :
Institute of Electrical and Electronics Engineers Inc.
ISBN/EAN :
9798400705878
Pages :
569-570
Peer reviewed :
Peer reviewed
FnR Project :
FNR17373407 - LOGODOR - Automated Log Smell Detection And Removal, 2022 (01/09/2023-31/08/2026) - Domenico Bianculli
Funders :
Association for Computing Machinery (ACM)
IEEE Computer Society
IEEE Technical Council on Software Engineering (TCSE)
Special Interest Group on Software Engineering (ACM SIGSOFT)
Funding number :
FNR17373407
Funding text :
Grant PID2022-137846NB-I00 funded by MCIN/AEI/10.13039/501100011033, by ERDF A way of making Europe. This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant referenceC22/IS/17373407/LOGODOR. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement, the authors have applied a Creative Commons Attribution 4.0 International (CC BY4.0) license to any Author Accepted Manuscript version arising from this submission.
Commentary :
This work was a tutorial presented at MSR 2024
Available on ORBilu :
since 19 January 2026

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