@article {moonen:2020:adaptive:JSS,
	title = {On Adaptive Change Recommendation},
	journal = {Journal of Systems and Software},
	volume = {164},
	year = {2020},
	month = {02/2020},
	publisher = {Elsevier},
	abstract = {As the complexity of a software system grows, it becomes harder for developers to be aware of all the dependencies between its artifacts (e.g., files or methods). Change impact analysis helps to overcome this challenge, by recommending relevant source-code artifacts related to a developer{\textquoteright}s current changes. Association rule mining has shown promise in determining change impact by uncovering relevant patterns in the system{\textquoteright}s change history.State-of-the-art change impact mining typically uses a change history of tens of thousands of transactions. For efficiency,\&nbsp;targeted\&nbsp;association rule mining constrains the transactions used to those potentially\&nbsp;relevant\&nbsp;to answering a particular\&nbsp;query. However, it still considers\&nbsp;all\&nbsp;the relevant transactions in the history.This paper presents\&nbsp;Atari, a new\&nbsp;adaptive\&nbsp;approach that further constrains targeted association rule mining by considering a\&nbsp;dynamic selection\&nbsp;of the relevant transactions. Our investigation of\&nbsp;adaptive change impact mining\&nbsp;empirically studies fourteen algorithm variants. We show that adaptive algorithms are viable, can be just as applicable as the start-of-the-art complete-history algorithms, and even outperform them for certain queries. However, more important than this direct comparison, our investigation motivates and lays the groundwork for the future study of adaptive techniques, and their application to challenges such as\&nbsp;on-the-fly\&nbsp;impact analysis at the GitHub-scale.},
	keywords = {association rule mining, change impact analysis, change recommendation, evolutionary coupling},
	doi = {https://doi.org/10.1016/j.jss.2020.110550},
	url = {https://www.sciencedirect.com/science/article/pii/S0164121220300327},
	author = {Moonen, Leon and Binkley, David and Pugh, Sydney}
}
@inproceedings {pugh:2018:adaptive,
	title = {The Case for Adaptive Change Recommendation},
	journal = {18th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM)},
	year = {2018},
	month = {09/2018},
	pages = {129-138},
	publisher = {IEEE},
	abstract = {As the complexity of software systems grows, it becomes increasingly\&nbsp;difficult\&nbsp;for developers to be aware of all the dependencies that exist between artifacts (e.g., files or methods) of a system. Change impact analysis\&nbsp;helps\&nbsp;to overcome this\&nbsp;problem, as it\&nbsp;recommends\&nbsp;to a developer relevant source-code artifacts related to her changes. Association rule mining has shown\&nbsp;promise\&nbsp;in determining change impact by uncovering relevant patterns in the system{\textquoteright}s change history.State-of-the-art change impact mining algorithms typically make use of a change history of tens of thousands of transactions. For efficiency, targeted association rule mining is used because it focuses on only those transactions relevant to answering a particular query. However, even targeted algorithms must consider the complete set of relevant transactions in the history.This paper presents , a new adaptive approach to targeted association rule mining that considers a dynamic selection of relevant transactions. It can be viewed as further\&nbsp;constrained\&nbsp;version of targeted association rule mining, in which as few as a single transaction might be considered when determining change impact. Our investigation of adaptive change impact mining empirically studies seven algorithm variants. We show that adaptive algorithms are viable, can be just as applicable as the start-of-the-art complete-history algorithms, and even outperform them for\&nbsp;certain\&nbsp;queries. However, more\&nbsp;important\&nbsp;than the direct comparison, our investigation lays\&nbsp;important\&nbsp;groundwork to\&nbsp;support\&nbsp;the application of adaptive techniques to\&nbsp;problems\&nbsp;such as, impact analysis at the GitHub-scale.},
	doi = {10.1109/SCAM.2018.00022},
	author = {Pugh, Sydney and Binkley, Dave and Moonen, Leon}
}
