Understanding bug fixes in Ant: An observational study
Maisqual Ant Dataset introduces the Apache Ant weekly dataset, featuring 636 extracts of the project over 12 years at different levels of artifacts- application, files, and functions. By associating community and process related information to code extracts, this dataset unveils interesting perspect...
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sg-smu-ink.sis_research-70132021-06-24T03:35:55Z Understanding bug fixes in Ant: An observational study SAHA, Shilpi MAJUMDER, Subhashis JAIN, Samip DATTA, Subhajit Maisqual Ant Dataset introduces the Apache Ant weekly dataset, featuring 636 extracts of the project over 12 years at different levels of artifacts- application, files, and functions. By associating community and process related information to code extracts, this dataset unveils interesting perspectives on the evolution of one of the great success stories of open source. In this paper, we analyze development dataset extracted on Monday of every week since the beginning of the project until end of July, 2012. By examining the correlation between number of developers, number of bugs fixed and number of commits done, we find evidence that developers maintain a reasonably good level of performance in the term of bug fixing. Using point plotting, we find that the number of source lines of code (SLOC) is impacted by the version release in the development period and maintenance period. This effect is also influenced by the number of bugs fixed and the number of commits done during the version release. 2014-11-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/6010 info:doi/10.1109/IC3I.2014.7019731 https://doi.org/10.1109/IC3I.2014.7019731 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software Engineering Software Metrics Source code metrics Source Lines of Code Software Engineering |
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Software Engineering Software Metrics Source code metrics Source Lines of Code Software Engineering SAHA, Shilpi MAJUMDER, Subhashis JAIN, Samip DATTA, Subhajit Understanding bug fixes in Ant: An observational study |
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Maisqual Ant Dataset introduces the Apache Ant weekly dataset, featuring 636 extracts of the project over 12 years at different levels of artifacts- application, files, and functions. By associating community and process related information to code extracts, this dataset unveils interesting perspectives on the evolution of one of the great success stories of open source. In this paper, we analyze development dataset extracted on Monday of every week since the beginning of the project until end of July, 2012. By examining the correlation between number of developers, number of bugs fixed and number of commits done, we find evidence that developers maintain a reasonably good level of performance in the term of bug fixing. Using point plotting, we find that the number of source lines of code (SLOC) is impacted by the version release in the development period and maintenance period. This effect is also influenced by the number of bugs fixed and the number of commits done during the version release. |
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SAHA, Shilpi MAJUMDER, Subhashis JAIN, Samip DATTA, Subhajit |
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SAHA, Shilpi MAJUMDER, Subhashis JAIN, Samip DATTA, Subhajit |
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SAHA, Shilpi |
title |
Understanding bug fixes in Ant: An observational study |
title_short |
Understanding bug fixes in Ant: An observational study |
title_full |
Understanding bug fixes in Ant: An observational study |
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Understanding bug fixes in Ant: An observational study |
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Understanding bug fixes in Ant: An observational study |
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understanding bug fixes in ant: an observational study |
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Institutional Knowledge at Singapore Management University |
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2014 |
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https://ink.library.smu.edu.sg/sis_research/6010 https://doi.org/10.1109/IC3I.2014.7019731 |
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