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Mining student CVS repositories for performance indicators

Published: 17 May 2005 Publication History

Abstract

Over 200 CVS repositories representing the assignments of students in a second year undergraduate computer science course have been assembled. This unique data set represents many individuals working separately on identical projects, presenting the opportunity to evaluate the effects of the work habits captured by CVS on performance. This paper outlines our experiences mining and analyzing these repositories. We extracted various quantitative measures of student behaviour and code quality, and attempted to correlate these features with grades. Despite examining 166 features, we find that grade performance cannot be accurately predicted; certainly no predictors stronger than simple lines-of-code were found.

References

[1]
CVS. http://www.cvs.org/.
[2]
PMD: A style checker. http://pmd.sourceforge.net/.
[3]
ViewCVS. http://viewcvs.sourceforge.net/.
[4]
T. L. Graves, A. F. Karr, J. S. Marron, and H. Siy. Predicting fault incidence using software change history. In IEEE Transactions on Software Engineering, volume 26, July 2000.
[5]
Y. Liu, E. Stroulia, K. Wong, and D. German. Using CVS historical information to understand how students develop software. In Proc. International Workshop on Mining Software Repositories (MSR04), Edinburgh, 2004.
[6]
T. Zimmermann and P. Weibgerber. Preprocessing CVS data for fine-grained analysis. In Proc. International Workshop on Mining Software Repositories (MSR04), Edinburgh, 2004.

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  • (2024)On Predicting Exam Performance Using Version Control Systems’ FeaturesComputers10.3390/computers1306015013:6(150)Online publication date: 9-Jun-2024
  • (2024)Early Identification of Struggling Students in Large Computer Science Courses: A Replication Study2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00022(88-93)Online publication date: 2-Jul-2024
  • (2024)Does Starting Deep Learning Homework Earlier Improve Grades?Artificial Intelligence. ECAI 2023 International Workshops10.1007/978-3-031-50485-3_38(381-396)Online publication date: 25-Jan-2024
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Published In

cover image ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes  Volume 30, Issue 4
July 2005
1514 pages
ISSN:0163-5948
DOI:10.1145/1082983
Issue’s Table of Contents
  • cover image ACM Other conferences
    MSR '05: Proceedings of the 2005 international workshop on Mining software repositories
    May 2005
    109 pages
    ISBN:1595931236
    DOI:10.1145/1083142
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2005
Published in SIGSOFT Volume 30, Issue 4

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Cited By

View all
  • (2024)On Predicting Exam Performance Using Version Control Systems’ FeaturesComputers10.3390/computers1306015013:6(150)Online publication date: 9-Jun-2024
  • (2024)Early Identification of Struggling Students in Large Computer Science Courses: A Replication Study2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00022(88-93)Online publication date: 2-Jul-2024
  • (2024)Does Starting Deep Learning Homework Earlier Improve Grades?Artificial Intelligence. ECAI 2023 International Workshops10.1007/978-3-031-50485-3_38(381-396)Online publication date: 25-Jan-2024
  • (2022)Exploring the Differences in Students’ Behavioral Engagement With Quizzes and Its Impact on their Performance in a Flipped CS1 CourseProceedings of the 22nd Koli Calling International Conference on Computing Education Research10.1145/3564721.3564740(1-11)Online publication date: 17-Nov-2022
  • (2022)Evaluating Code Improvements in Software Quality Course ProjectsProceedings of the 26th International Conference on Evaluation and Assessment in Software Engineering10.1145/3530019.3530036(160-169)Online publication date: 13-Jun-2022
  • (2022)A Comparison of Immediate and Scheduled Feedback in Introductory Programming ProjectsProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499372(885-891)Online publication date: 22-Feb-2022
  • (2022)Early Identification of Student Struggles at the Topic Level Using Context-Agnostic FeaturesProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499298(147-153)Online publication date: 22-Feb-2022
  • (2021)Using process mining for Git log analysis of projects in a software development courseEducation and Information Technologies10.1007/s10639-021-10564-6Online publication date: 10-May-2021
  • (2021)Students Projects’ Source Code Changes Impact on Software Quality Through Static AnalysisQuality of Information and Communications Technology10.1007/978-3-030-85347-1_39(553-564)Online publication date: 25-Aug-2021
  • (2021)Measuring Students’ Source Code Quality in Software Development Projects Through Commit-Impact AnalysisInformation Technology and Systems10.1007/978-3-030-68418-1_11(100-109)Online publication date: 29-Jan-2021
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