Mining student CVS repositories for performance indicators

Mierle, Keir; Laven, Kevin; Roweis, Sam T.; Wilson, Greg

doi:10.1145/1083142.1083150

Cited by 31 publications

(16 citation statements)

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“…These observations are likely to be correct for most student projects, and so may have important implications for such studies [7]; however in industrial projects the developers are likely to be more familiar with CVS and consequently less likely to exhibit the same behavior as the students. This may or may not be the case with open source projects as it may be hard to determine the experience of the lead developers, particularly so if the repository is many years old.…”

Section: Discussionmentioning

confidence: 89%

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Correctness of data mined from CVS

Thomson

Holcombe

2008

Proceedings of the 2008 International Working Conference on Mining Software Repositories

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Source code repositories managed by the popular CVS are frequently mined by researchers to validate various hypotheses about how the development of a software product progressed. This paper presents a study where the development process of 17 student teams was followed. It will show that in an environment where the teams were permitted to manage their own CVS repositories that several errors emerged that could lead to the misinterpretation of a hypothesis. These were classified into three types: type one errors which relate to the non-use of the system; type two errors that emerged from the direct manipulation of the repository; and type three errors from the limitation of CVS not to record file name changes.

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Section: Discussionmentioning

confidence: 89%

“…Student projects are particularly vulnerable to errors made by the students because they don't understand how the version control system work, especially at the start of the course [6,7].…”

Section: Introductionmentioning

confidence: 99%

Correctness of data mined from CVS

Thomson

Holcombe

2008

Proceedings of the 2008 International Working Conference on Mining Software Repositories

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“…The high number of related comments was expected since SCMs are one of the most mined repositories in the field. The comments range from tips on how to extract data from a specific SCM system such as CVS (e.g., using "sliding window" and the commit time for grouping CVS commits [18], [41]) and GIT (e.g., observing that "implicit" branches can create ordering confusion [12]) to general points that can be applied to all SCMs.…”

Section: A Source Codementioning

confidence: 99%

The MSR Cookbook: Mining a decade of research

Hemmati

Nadi

Baysal

et al. 2013

2013 10th Working Conference on Mining Software Repositories (MSR)

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Abstract-The Mining Software Repositories (MSR) research community has grown significantly since the first MSR workshop was held in 2004. As the community continues to broaden its scope and deepens its expertise, it is worthwhile to reflect on the best practices that our community has developed over the past decade of research. We identify these best practices by surveying past MSR conferences and workshops. To that end, we review all 117 full papers published in the MSR proceedings between 2004 and 2012. We extract 268 comments from these papers, and categorize them using a grounded theory methodology. From this evaluation, four high-level themes were identified: data acquisition and preparation, synthesis, analysis, and sharing/replication. Within each theme we identify several common recommendations, and also examine how these recommendations have evolved over the past decade. In an effort to make this survey a living artifact, we also provide a public forum that contains the extracted recommendations in the hopes that the MSR community can engage in a continuing discussion on our evolving best practices.

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“…Examples include the mining of CVS meta-data [4] or of data from an automated grading and testing system [6] to identify performance indicators, the use of CVS meta-data to analyse student group work [5], the effectiveness of the Personal Software Process pedagogy in student programming [7] and the analysis of patterns of student CVS commit events [8].…”

Section: Related Workmentioning

confidence: 99%

Using the SCORE software package to analyse novice computer graphics programming

Wittmann

Bower

Kavakli

2011

Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education

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This paper presents the SCORE (Student Coding Observation and Recording Engine) software package designed to capture and analyse student coding processes. The package consists of an Eclipse [1] plug-in to gather observational data while students code a programming task, and an analysis tool that allows researchers to visualise, categorise and annotate changes in code. Because the SCORE package supports code text level analysis it enables more in-depth understanding of student programming and problem solving approaches than meta-data or program output analysis tools. SCORE also provides features to assist the analysis of Computer Graphics programs. An example analysis of a student's Computer Graphics assignments demonstrates how SCORE was used to reveal the dominant role of general programming issues in the early assignment, whereas spatial programming issues persisted throughout both assignments.

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Mining student CVS repositories for performance indicators

Cited by 31 publications

References 0 publications

Correctness of data mined from CVS

Correctness of data mined from CVS

The MSR Cookbook: Mining a decade of research

Using the SCORE software package to analyse novice computer graphics programming

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