@proceedings {1725,
	title = {Mining StackOverflow to Filter out Off-topic IRC Discussion},
	year = {2015},
	month = {05/2015},
	pages = {4 pages},
	abstract = {Internet Relay Chat (IRC) is a commonly used tool by OpenSource developers. Developers use IRC channels to discuss programming related problems, but much of the discussion is irrelevant and off-topic. Essentially if we treat IRC discussions like email messages, and apply spam filtering, we can try to filter out the spam (the off-topic discussions) from the ham (the programming discussions). Yet we need labelled data that unfortunately takes time to curate.
To avoid costly curration in order to filter out off-topic discussions, we need positive and negative data-sources. On- line discussion forums, such as StackOverflow, are very effective for solving programming problems. By engaging in open-data, StackOverflow data becomes a powerful source of labelled text regarding programming. This work shows that we can train classifiers using StackOverflow posts as positive examples of on-topic programming discussion. YouTube video comments, notorious for their lack of quality, serve as training set of off- topic discussion. By exploiting these datasets, accurate classifiers can be built, tested and evaluated that require very little effort for end-users to deploy and exploit.},
	keywords = {irc, topics},
	url = {http://webdocs.cs.ualberta.ca/~hindle1/2015/shaiful-mining_so.pdf},
	attachments = {https://flosshub.org/sites/flosshub.org/files/shaiful-mining_so.pdf},
	author = {Shaiful Alam Chowdhury and Hindle, Abram}
}
@article {1400,
	title = {Analyzing and mining a code search engine usage log},
	journal = {Empirical Software Engineering},
	volume = {17},
	year = {2012},
	month = {8/2012},
	pages = {424 - 466},
	abstract = {This paper presents an analysis of a year long usage log of Koders, the first commercially available Internet-Scale code search engine (http://www.koders.com). The usage log comprises about ten million activities from more than three million users. Analysis of the usage data shows that despite of attracting a large number of visitors, Koders has a very sparse usage and that it lacks regular usage from many of its users. When compared to Web search, search behavior in Koders showed many similar patterns. A topic modeling analysis of the usage data shows what topics users of Koders are looking for. Observations on the prevalence of these topics among the users, and observations on how search and download activities vary across topics, lead to the conclusion that users who find code search engines usable are those who already know to a high level of specificity what to look for. This paper also presents a general categorization of these topics that provides insights on the different ways code search engine users express their queries. It identifies various forms of queries in Koders{\textquoteright}s log and the kinds of results addressed by the queries. It also provides several suggestions for improvements in code search engines based on the analysis of usage, topics, and query forms. The work presented in this paper is the first of its kind that reveals several insights on the usage of an Internet-Scale code search engine.},
	keywords = {code search, koders, search, search engine, topics},
	issn = {1573-7616},
	doi = {10.1007/s10664-010-9144-6},
	author = {Bajracharya, Sushil Krishna and Lopes, Cristina Videira}
}
@article {1395,
	title = {Automated topic naming: supporting cross-project analysis of software maintenance activities},
	journal = {Empirical Software Engineering},
	year = {2012},
	abstract = {Software repositories provide a deluge of software artifacts to analyze. Researchers have attempted to summarize, categorize, and relate these artifacts by using semi-unsupervised machine-learning algorithms, such as Latent Dirichlet Allocation (LDA). LDA is used for concept and topic analysis to suggest candidate word-lists or topics that describe and relate software artifacts. However, these word-lists and topics are difficult to interpret in the absence of meaningful summary labels. Current attempts to interpret topics assume manual labelling and do not use domain-specific knowledge to improve, contextualize, or describe results for the developers. We propose a solution: automated labelled topic extraction. Topics are extracted using LDA from commit-log comments recovered from source control systems. These topics are given labels from a generalizable cross-project taxonomy, consisting of non-functional requirements. Our approach was evaluated with experiments and case studies on three large-scale Relational Database Management System (RDBMS) projects: MySQL, PostgreSQL and MaxDB. The case studies show that labelled topic extraction can produce appropriate, context-sensitive labels that are relevant to these projects, and provide fresh insight into their evolving software development activities. },
	keywords = {LDA, maxdb, mysql, postgresql, topics},
	issn = {1573-7616},
	doi = {10.1007/s10664-012-9209-9},
	author = {Hindle, Abram and Ernst, Neil A. and Godfrey, Michael W. and Mylopoulos, John}
}
@conference {1015,
	title = {Mining Eclipse Developer Contributions via Author-Topic Models},
	booktitle = {Fourth International Workshop on Mining Software Repositories (MSR{\textquoteright}07:ICSE Workshops 2007)},
	year = {2007},
	pages = {30 - 30},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Minneapolis, MN, USA},
	abstract = {We present the results of applying statistical author-topic models to a subset of the Eclipse 3.0 source code consisting of 2,119 source files and 700,000 lines of code from 59 developers. This technique provides an intuitive and automated framework with which to mine developer contributions and competencies from a given code base while simultaneously extracting software function in the form of topics. In addition to serving as a convenient summary for program function and developer activities, our study shows that topic models provide a meaningful, effective, and statistical basis for developer similarity analysis.},
	keywords = {contributions, developers, eclipse, expertise, mining challenge, msr challenge, source code, topics},
	isbn = {0-7695-2950-X},
	doi = {10.1109/MSR.2007.20},
	attachments = {https://flosshub.org/sites/flosshub.org/files/28300030.pdf},
	author = {Linstead, Erik and Rigor, Paul and Bajracharya, Sushil and Lopes, Cristina and Baldi, Pierre}
}
