@article {rolfsnes:2018:aggregating,
	title = {Aggregating Association Rules to Improve Change Recommendation},
	journal = {Journal of Empirical Software Engineering (EMSE)},
	volume = {23},
	year = {2018},
	month = {04/2018},
	pages = {987-1035},
	publisher = { Springer},
	abstract = {As the complexity of software systems grows, it becomes increasinglydifficult for developers to be aware of all the dependencies thatexist between artifacts (e.g., files or methods) of a system. \&nbsp;Changerecommendation has been proposed as a technique to overcome thisproblem, as it suggests to a developer relevant source-code artifactsrelated to her changes. \&nbsp;Association rule mining has shown promiseto deriving such recommendations by uncovering relevant patternsin the system{\textquoteright}s change history. \&nbsp;The strength of the mined associationrules is captured using a variety of interestingness measures.However, state-of-the-art recommendation engines typically use onlythe rule with the highest interestingness value when more than onerule applies. \&nbsp;In contrast, we argue that when multiple rules apply,this indicates collective evidence, and aggregating those rules(and their evidence) will lead to more accurate change recommendation.To investigate this hypothesis we conduct a large empirical studyof 15 open source software systems and two systems from our industrypartners. \&nbsp;We evaluate association rule aggregation using fourvariants of the change history for each system studied, enablingus to compare two different levels of granularity in two differentscenarios. \&nbsp;Furthermore, we study 40 interestingness measures usingthe rules produced by two different mining algorithms. \&nbsp;The resultsshow that (1) between 13\% and 90\% of change recommendations can beimproved by rule aggregation, (2) rule aggregation almost alwaysimproves change recommendation for both algorithms and all measures,and (3) fine-grained histories benefit more from rule aggregation.\&nbsp;},
	keywords = {change impact analysis, change recommendations, evolutionary coupling, interestingness aggregator, rule aggregation, targeted association rule mining},
	issn = {1382-3256},
	doi = {10.1007/s10664-017-9560-y},
	url = {https://doi.org/10.1007/s10664-017-9560-y},
	author = {Thomas Gramstad {Rolfsnes} and Moonen, Leon and Di Alesio, Stefano and Behjati, Razieh and Binkley, David}
}
