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	Product assignment recommender
	Xie, Jialiang ; Zheng, Qimu ; Zhou, Minghui ; Mockus, Audris
	2014
英文摘要	Effectiveness of software development process depends on the accuracy of data in supporting tools. In particular, a customer issue assigned to a wrong product team takes much longer to resolve (negatively affecting user-perceived quality) and wastes developer effort. In Open Source Software (OSS) and in commercial projects values in issue-tracking systems (ITS) or Customer Relationship Management (CRM) sys-tems are often assigned by non-developers for whom the assignment task is difficult. We propose PAR (Product As-signment Recommender) to estimate the odds that a value in the ITS is incorrect. PAR learns from the past activities in ITS and performs prediction using a logistic regression model. Our demonstrations show how PAR helps develop-ers to focus on fixing real problems, and how it can be used to improve data accuracy in ITS by crowd-sourcing non-developers to verify and correct low-accuracy data. Copyright ? 2014 ACM.; EI; 0
语种	英语
DOI标识	10.1145/2591062.2591073
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/295585]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Xie, Jialiang,Zheng, Qimu,Zhou, Minghui,et al. Product assignment recommender. 2014-01-01.

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