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Full-Text Articles in Physical Sciences and Mathematics
Concern Localization Using Information Retrieval: An Empirical Study On Linux Kernel, Shaowei Wang, David Lo, Zhenchang Xing, Lingxiao Jiang
Concern Localization Using Information Retrieval: An Empirical Study On Linux Kernel, Shaowei Wang, David Lo, Zhenchang Xing, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Many software maintenance activities need to find code units (functions, files, etc.) that implement a certain concern (features, bugs, etc.). To facilitate such activities, many approaches have been proposed to automatically link code units with concerns described in natural languages, which are termed as concern localization and often employ Information Retrieval (IR) techniques. There has not been a study that evaluates and compares the effectiveness of latest IR techniques on a large dataset. This study fills this gap by investigating ten IR techniques, some of which are new and have not been used for concern localization, on a Linux kernel …
Parallel Learning To Rank For Information Retrieval, Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw
Parallel Learning To Rank For Information Retrieval, Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency.