@conference {zmzkh-icsm-2013,
	title = {How does Context affect the Distribution of Software Maintainability Metrics?},
	booktitle = {Proceedings of the 29th IEEE International Conference on Software Maintainability},
	series = {ICSM {\textquoteright}13},
	year = {2013},
	note = {"FLOSSMole [25] is another data source, from
where we download descriptions (i.e., application domain)
of SourceForge software systems. Furthermore, we download
latest application domain information4
and monthly download
data5 of studied software systems directly from SourceForge."},
	abstract = {Software metrics have many uses, e.g., defect prediction, effort estimation, and benchmarking an organization against peers and industry standards. In all these cases, metrics may depend on the context, such as the programming language. Here we aim to investigate if the distributions of commonly used metrics do, in fact, vary with six context factors: application domain, programming language, age, lifespan, the number of changes, and the number of downloads. For this preliminary study we select 320 nontrivial software systems from SourceForge. These software systems are randomly sampled from nine popular application domains of SourceForge. We calculate 39 metrics commonly used to assess software maintainability for each software system and use Kruskal Wallis test and Mann-Whitney U test to determine if there are significant differences among the distributions with respect to each of the six context factors. We use Cliff{\textquoteright}s delta to measure the magnitude of the differences and find that all six context factors affect the distribution of 20 metrics and the programming language factor affects 35 metrics. We also briefly discuss how each context factor may affect the distribution of metric values.We expect our results to help software benchmarking and other software engineering methods that rely on these commonly used metrics to be tailored to a particular context.},
	keywords = {benchmark, context, contextual factor, flossmole, large scale, metrics, mining software repositories, sampling, software maintainability, sourceforge, static metrics},
	attachments = {https://flosshub.org/sites/flosshub.org/files/icsm2013_contextstudy.pdf},
	author = {Zhang, Feng and Audris Mockus and Ying Zou and Foutse Khomh and Hassan, Ahmed E.}
}
@article {1396,
	title = {Studying the impact of social interactions on software quality},
	journal = {Empirical Software Engineering},
	year = {2012},
	note = {bug databases},
	abstract = {Correcting software defects accounts for a significant amount of resources in a software project. To make best use of testing efforts, researchers have studied statistical models to predict in which parts of a software system future defects are likely to occur. By studying the mathematical relations between predictor variables used in these models, researchers can form an increased understanding of the important connections between development activities and software quality. Predictor variables used in past top-performing models are largely based on source code-oriented metrics, such as lines of code or number of changes. However, source code is the end product of numerous interlaced and collaborative activities carried out by developers. Traces of such activities can be found in the various repositories used to manage development efforts. In this paper, we develop statistical models to study the impact of social interactions in a software project on software quality. These models use predictor variables based on social information mined from the issue tracking and version control repositories of two large open-source software projects. The results of our case studies demonstrate the impact of metrics from four different dimensions of social interaction on post-release defects. Our findings show that statistical models based on social information have a similar degree of explanatory power as traditional models. Furthermore, our results demonstrate that social information does not substitute, but rather augments traditional source code-based metrics used in defect prediction models.},
	keywords = {bug tracker, eclipse, Firefox, Human Factors, measurement, metrics, software evolution, Software quality assurance},
	issn = {1573-7616},
	doi = {10.1007/s10664-012-9205-0},
	author = {Bettenburg, Nicolas and Hassan, Ahmed E.}
}
@article {1681,
	title = {Using Pig as a data preparation language for large-scale mining software repositories studies: An experience report},
	journal = {Journal of Systems and Software},
	volume = {85},
	year = {2012},
	note = {"For example, FLOSSMole (Howison et al., 2006) is a public relational database that contains data extracted from a large number of software repositories. Many researchers use FLOSSMole as a platform. For example, Herraiz et al. (2008) used data in FLOSSMole (Howison et al., 2006) to perform analysis to illustrate that most of the software projects are governed by short term goals rather than long term goals."},
	month = {10/2012},
	pages = {2195 - 2204},
	abstract = {The Mining Software Repositories (MSR) field analyzes software repository data to uncover knowledge and assist development of ever growing, complex systems. However, existing approaches and platforms for MSR analysis face many challenges when performing large-scale MSR studies. Such approaches and platforms rarely scale easily out of the box. Instead, they often require custom scaling tricks and designs that are costly to maintain and that are not reusable for other types of analysis. We believe that the web community has faced many of these software engineering scaling challenges before, as web analyses have to cope with the enormous growth of web data. In this paper, we report on our experience in using a web-scale platform (i.e., Pig) as a data preparation language to aid large-scale MSR studies. Through three case studies, we carefully validate the use of this web platform to prepare (i.e., Extract, Transform, and Load, ETL) data for further analysis. Despite several limitations, we still encourage MSR researchers to leverage Pig in their large-scale studies because of Pig{\textquoteright}s scalability and flexibility. Our experience report will help other researchers who want to scale their analyses.},
	keywords = {flossmole cited},
	issn = {01641212},
	doi = {10.1016/j.jss.2011.07.034},
	url = {http://www.sciencedirect.com/science/article/pii/S0164121211002007},
	author = {Weiyi Shang and Adams, Bram and Hassan, Ahmed E.}
}
@conference {Jiang:2006:EEC:1137983.1138030,
	title = {Examining the evolution of code comments in PostgreSQL},
	booktitle = {Proceedings of the 2006 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}06},
	year = {2006},
	pages = {179{\textendash}180},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {It is common, especially in large software systems, for developers to change code without updating its associated comments due to their unfamiliarity with the code or due to time constraints. This is a potential problem since outdated comments may confuse or mislead developers who perform future development. Using data recovered from CVS, we study the evolution of code comments in the PostgreSQL project. Our study reveals that over time the percentage of commented functions remains constant except for early fluctuation due to the commenting style of a particular active developer.},
	keywords = {code comments, comments, cvs, evolution, functions, maintenance, mining challenge, msr challenge, postgresql, software evolution, software maintenance, source code},
	isbn = {1-59593-397-2},
	doi = {http://doi.acm.org/10.1145/1137983.1138030},
	url = {http://doi.acm.org/10.1145/1137983.1138030},
	attachments = {https://flosshub.org/sites/flosshub.org/files/179ExaminingTheEvolution.pdf},
	author = {Zhen Ming Jiang and Hassan, Ahmed E.}
}
