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Integrating Declarative Static Analysis With Neural Models of Code- [electronic resource]
Integrating Declarative Static Analysis With Neural Models of Code- [electronic resource]
상세정보
- 자료유형
- 학위논문
- Control Number
- 0016931383
- International Standard Book Number
- 9798379754686
- Dewey Decimal Classification Number
- 004
- Main Entry-Personal Name
- Pashakhanloo, Pardis.
- Publication, Distribution, etc. (Imprint
- [S.l.] : University of Pennsylvania., 2023
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- Physical Description
- 1 online resource(132 p.)
- General Note
- Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
- General Note
- Advisor: Naik, Mayur.
- Dissertation Note
- Thesis (Ph.D.)--University of Pennsylvania, 2023.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Summary, Etc.
- 요약In recent years, deep learning techniques have made remarkable strides in solving a variety of program understanding challenges. The successful application of these techniques to a given task depends heavily on how the source code is represented by the deep neural network. Designing a suitable representation for a newly created task involves many challenges. It is necessary, among other things, to understand the implementation of other functions or modules in a project that may be spread out across a large lexical area. In addition, determining which components and features to include in order to enrich the representation is a challenge. In this dissertation, the challenges of code representation are addressed by proposing to systematically represent programs as relational databases, introducing a graph walk mechanism to remove unrelated context from large relational graphs, and describing a language for specifying tasks and program analysis queries to tailor neural code-reasoning models. A detailed analysis shows the presented techniques are superior to state-of-the-art in a variety of aspects, such as performance, robustness, and interpretability.
- Subject Added Entry-Topical Term
- Computer science.
- Subject Added Entry-Topical Term
- Information science.
- Index Term-Uncontrolled
- Bug finding
- Index Term-Uncontrolled
- Deep learning
- Index Term-Uncontrolled
- Program analysis
- Index Term-Uncontrolled
- Software security
- Added Entry-Corporate Name
- University of Pennsylvania Computer and Information Science
- Host Item Entry
- Dissertations Abstracts International. 84-12A.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:642119
MARC
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■007cr#unu||||||||
■020 ▼a9798379754686
■035 ▼a(MiAaPQ)AAI30310556
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aPashakhanloo, Pardis.
■24510▼aIntegrating Declarative Static Analysis With Neural Models of Code▼h[electronic resource]
■260 ▼a[S.l.]▼bUniversity of Pennsylvania. ▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a1 online resource(132 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: A.
■500 ▼aAdvisor: Naik, Mayur.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aIn recent years, deep learning techniques have made remarkable strides in solving a variety of program understanding challenges. The successful application of these techniques to a given task depends heavily on how the source code is represented by the deep neural network. Designing a suitable representation for a newly created task involves many challenges. It is necessary, among other things, to understand the implementation of other functions or modules in a project that may be spread out across a large lexical area. In addition, determining which components and features to include in order to enrich the representation is a challenge. In this dissertation, the challenges of code representation are addressed by proposing to systematically represent programs as relational databases, introducing a graph walk mechanism to remove unrelated context from large relational graphs, and describing a language for specifying tasks and program analysis queries to tailor neural code-reasoning models. A detailed analysis shows the presented techniques are superior to state-of-the-art in a variety of aspects, such as performance, robustness, and interpretability.
■590 ▼aSchool code: 0175.
■650 4▼aComputer science.
■650 4▼aInformation science.
■653 ▼aBug finding
■653 ▼aDeep learning
■653 ▼aProgram analysis
■653 ▼aSoftware security
■690 ▼a0984
■690 ▼a0800
■690 ▼a0723
■71020▼aUniversity of Pennsylvania▼bComputer and Information Science.
■7730 ▼tDissertations Abstracts International▼g84-12A.
■773 ▼tDissertation Abstract International
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931383▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
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