@conference {953,
	title = {When process data quality affects the number of bugs: Correlations in software engineering datasets},
	booktitle = {2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)},
	year = {2010},
	pages = {62 - 71},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Cape Town, South Africa},
	abstract = {Software engineering process information extracted from version control systems and bug tracking databases are widely used in empirical software engineering. In prior work, we showed that these data are plagued by quality deficiencies, which vary in its characteristics across projects. In addition, we showed that those deficiencies in the form of bias do impact the results of studies in empirical software engineering. While these findings affect software engineering researchers the impact on practitioners has not yet been substantiated. In this paper we, therefore, explore (i) if the process data quality and characteristics have an influence on the bug fixing process and (ii) if the process quality as measured by the process data has an influence on the product (i.e., software) quality. Specifically, we analyze six Open Source as well as two Closed Source projects and show that process data quality and characteristics have an impact on the bug fixing process: the high rate of empty commit messages in Eclipse, for example, correlates with the bug report quality. We also show that the product quality - measured by number of bugs reported - is affected by process data quality measures. These findings have the potential to prompt practitioners to increase the quality of their software process and its associated data quality.},
	keywords = {apache, bug reports, eclipse, gnome, log files, mozilla, netbeans, openoffice.org, version control},
	isbn = {978-1-4244-6802-7},
	doi = {10.1109/MSR.2010.5463286},
	attachments = {https://flosshub.org/sites/flosshub.org/files/62bachmann-msr10.pdf},
	author = {Bachmann, Adrian and Bernstein, Abraham}
}
@conference {Knab:2006:PDD:1137983.1138012,
	title = {Predicting defect densities in source code files with decision tree learners},
	booktitle = {Proceedings of the 2006 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}06},
	year = {2006},
	pages = {119{\textendash}125},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {With the advent of open source software repositories the data available for defect prediction in source files increased tremendously. Although traditional statistics turned out to derive reasonable results the sheer amount of data and the problem context of defect prediction demand sophisticated analysis such as provided by current data mining and machine learning techniques.In this work we focus on defect density prediction and present an approach that applies a decision tree learner on evolution data extracted from the Mozilla open source web browser project. The evolution data includes different source code, modification, and defect measures computed from seven recent Mozilla releases. Among the modification measures we also take into account the change coupling, a measure for the number of change-dependencies between source files. The main reason for choosing decision tree learners, instead of for example neural nets, was the goal of finding underlying rules which can be easily interpreted by humans. To find these rules, we set up a number of experiments to test common hypotheses regarding defects in software entities. Our experiments showed, that a simple tree learner can produce good results with various sets of input data.},
	keywords = {change analysis, data mining, decision tree learner, defect density, defect prediction, mozilla, prediction, release history, scm, source code, version control},
	isbn = {1-59593-397-2},
	doi = {http://doi.acm.org/10.1145/1137983.1138012},
	url = {http://doi.acm.org/10.1145/1137983.1138012},
	attachments = {https://flosshub.org/sites/flosshub.org/files/119Predicting.pdf},
	author = {Knab, Patrick and Pinzger, Martin and Bernstein, Abraham}
}
