@conference {1005,
	title = {Comparing Approaches to Mining Source Code for Call-Usage Patterns},
	booktitle = {Fourth International Workshop on Mining Software Repositories (MSR{\textquoteright}07:ICSE Workshops 2007)},
	year = {2007},
	pages = {20 - 20},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Minneapolis, MN, USA},
	abstract = {Two approaches for mining function-call usage patterns from source code are compared. The first approach, itemset mining, has recently been applied to this problem. The other approach, sequential-pattern mining, has not been previously applied to this problem. Here, a call-usage pattern is a composition of function calls that occur in a function definition. Both approaches look for frequently occurring patterns that represent standard usage of functions and identify possible errors. Itemset mining produces unordered patterns, i.e., sets of function calls, whereas, sequential-pattern mining produces partially ordered patterns, i.e., sequences of function calls. The trade-off between the additional ordering context given by sequential-pattern mining and the efficiency of itemset mining is investigated. The two approaches are applied to the Linux kernel v2.6.14 and results show that mining ordered patterns is worth the additional cost.},
	keywords = {function calls, functions, kernel, linux, sequence, sequencing, sequential-pattern mining},
	isbn = {0-7695-2950-X},
	doi = {10.1109/MSR.2007.3},
	attachments = {https://flosshub.org/sites/flosshub.org/files/28300020.pdf},
	author = {Kagdi, Huzefa and Collard, Michael L. and Maletic, Jonathan I.}
}
@conference {Kagdi:2006:MSC:1137983.1137996,
	title = {Mining sequences of changed-files from version histories},
	booktitle = {Proceedings of the 2006 international workshop on Mining software repositories},
	series = {MSR {\textquoteright}06},
	year = {2006},
	pages = {47{\textendash}53},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Modern source-control systems, such as Subversion, preserve change-sets of files as atomic commits. However, the specific ordering information in which files were changed is typically not found in these source-code repositories. In this paper, a set of heuristics for grouping change-sets (i.e., log-entries) found in source-code repositories is presented. Given such groups of change-sets, sequences of files that frequently change together are uncovered. This approach not only gives the (unordered) sets of files but supplements them with (partial temporal) ordering information. The technique is demonstrated on a subset of KDE source-code repository. The results show that the approach is able to find sequences of changed-files.},
	keywords = {change, change history, change management, change sequences, heuristics, kde, mining software repositories, scm, sequences, source code},
	isbn = {1-59593-397-2},
	doi = {http://doi.acm.org/10.1145/1137983.1137996},
	url = {http://doi.acm.org/10.1145/1137983.1137996},
	attachments = {https://flosshub.org/sites/flosshub.org/files/47MiningSequences.pdf},
	author = {Kagdi, Huzefa and Yusuf, Shehnaaz and Maletic, Jonathan I.}
}
