@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 {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}
}
