Clustering navigation sequences to create contexts for guiding code navigation = 코드에서 방문할 위치를 안내하기 위한 문맥 생성: 방문 경로 군집화

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To guide programmer code navigation, previous approaches such as TeamTracks recommend pieces of code to visit by mining the associations between pieces of code in programmer interaction histories. However, these result in low recommendation accuracy. To create more accurate recommendations, we propose NavClus an approach that clusters navigation sequences from programmer interaction histories. NavClus automatically forms collections of code that are relevant to the tasks performed by programmers, and then retrieves the collections best matched to a programmer`s current navigation path. This makes it possible to recommend the collections of code that are relevant to the programmer`s given task. We compare NavClus` recommendation accuracy with TeamTracks` by simulating recommendations using 4,397 interaction histories. The comparative experiment shows that the recommendation accuracy ofNavClus is twice as high as that of TeamTracks. We also conduct a diary study to investigate the effectiveness of a graphical code recommender that incorporates the NavClus approach. Our study reveals that developers using NavClus tend to spend less time viewing the code base and more time modifying the software, indicating less time is needed to understand software before modifying it.
Advisors
Kang, Sung-Wonresearcher강성원
Description
한국과학기술원 : 전산학과,
Publisher
한국과학기술원
Issue Date
2013
Identifier
566045/325007  / 020105371
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전산학과, 2013.8, [ vi, 75 p. ]

Keywords

code navigation; 추천 시스템; 문맥 인식; 데이타 스트림 마이닝; 데이타 군집화 기법; 코드 네비게이션; data clustering techniques; data stream mining; context aware; recommendation systems

URI
http://hdl.handle.net/10203/197808
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=566045&flag=dissertation
Appears in Collection
CS-Theses_Ph.D.(박사논문)
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