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Collective Program Analysis
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Collective Program Analysis. |
| 개인저자 | Upadhyaya, Ganesha. |
| 단체저자명 | Iowa State University. Computer Science. |
| 발행사항 | [S.l.]: Iowa State University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 120 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438076242 |
| 학위논문주기 | Thesis (Ph.D.)--Iowa State University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Hridesh Rajan. |
| 요약 | Encouraged by the success of data-driven software engineering (SE) techniques that have found numerous applications e.g. in defect prediction, specification inference, etc, the demand for mining and analyzing source code repositories at scale ha |
| 요약 | First, we describe the general concept of collective program analysis. Given a mining task that is required to be run on thousands of artifacts, the artifacts with similar interactions are clustered together, such that the mining task is require |
| 요약 | Given a mining task and an artifact producing an interaction pattern graph soundly and efficiently can be very challenging. We propose a pre-analysis and program compaction technique to achieve this. Given a source code mining task and thousands |
| 요약 | Upon producing interaction pattern graphs of thousands of artifacts, they have to be clustered and the mining task results have to be reused between similar artifacts to achieve acceleration. In the final part of this thesis, we fully describes |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
