@conference {938,
	title = {Using association rules to study the co-evolution of production \& test code},
	booktitle = {2009 6th IEEE International Working Conference on Mining Software Repositories (MSR)2009 6th IEEE International Working Conference on Mining Software Repositories},
	year = {2009},
	pages = {151 - 154},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Vancouver, BC, Canada},
	abstract = {Unit tests are generally acknowledged as an important aid to produce high quality code, as they provide quick feedback to developers on the correctness of their code. In order to achieve high quality, well-maintained tests are needed. Ideally, tests co-evolve with the production code to test changes as soon as possible. In this paper, we explore an approach based on association rule mining to determine whether production and test code co-evolve synchronously. Through two case studies, one with an open source and another one with an industrial software system, we show that our association rule mining approach allows one to assess the co-evolution of product and test code in a software project and, moreover, to uncover the distribution of programmer effort over pure coding, pure testing, or a more test-driven-like practice.},
	keywords = {association rules, checkstyle, source code, unit test},
	isbn = {978-1-4244-3493-0},
	doi = {10.1109/MSR.2009.5069493},
	attachments = {https://flosshub.org/sites/flosshub.org/files/151UsingAssociation.pdf},
	author = {Lubsen, Zeeger and Zaidman, Andy and Pinzger, Martin}
}
@conference {DBLP:conf/pakdd/ChawlaAD03,
	title = {Mining Open Source Software (OSS) Data Using Association Rules Network},
	booktitle = {Lecture Notes in Computer Science },
	volume = {2637},
	year = {2003},
	pages = {461-466},
	abstract = {The Open Source Software(OSS) movement has attracted considerable attention in the last few years. In this paper we report our results of mining data acquired from SourceForge.net, the largest open source software hosting website. In the process we introduce Association Rules Network(ARN), a (hyper)graphical model to represent a special class of association rules. Using ARNs we discover important relationships between the attributes of successful OSS projects. We verify and validate these relationships using Factor Analysis, a classical statistical technique related to Singular Value Decomposition(SVD).},
	keywords = {arn, association rules, factor analysis, project success, sourceforge, svd},
	author = {Sanjay Chawla and Bavani Arunasalam and Joseph G. Davis}
}
