SnR: constraint-based type inference for incomplete Java code snippets.
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| Title: | SnR: constraint-based type inference for incomplete Java code snippets. |
|---|---|
| Authors: | Dong, Yiwen1 y225dong@uwaterloo.ca, Gu, Tianxiao2 tianxiao.gu@gmail.com, Tian, Yongqiang1 yongqiang.tian@uwaterloo.ca, Sun, Chengnian1 cnsun@uwaterloo.ca |
| Source: | ICSE: International Conference on Software Engineering. 2022, p1982-1993. 12p. |
| Subjects: | Java programming language, Datalog (Computer program language), Programming languages, Computer interfaces, Libraries |
| Abstract: | Code snippets are prevalent on websites such as Stack Overflow and are effective in demonstrating API usages concisely. However they are usually difficult to be used directly because most code snippets not only are syntactically incomplete but also lack dependency information, and thus do not compile. For example, Java snippets usually do not have import statements or required library names; only 6.88% of Java snippets on Stack Overflow include import statements necessary for compilation. This paper proposes SnR, a precise, efficient, constraint-based technique to automatically infer the exact types used in code snippets and the libraries containing the inferred types, to compile and therefore reuse the code snippets. Initially, SnR builds a knowledge base of APIs, i.e., various facts about the available APIs, from a corpus of Java libraries. Given a code snippet with missing import statements, SnR automatically extracts typing constraints from the snippet, solves the constraints against the knowledge base, and returns a set of APIs that satisfies the constraints to be imported into the snippet. We have evaluated SnR on a benchmark of 267 code snippets from Stack Overflow. SnR significantly outperforms the state-of-the-art tool Coster. SnR correctly infers 91.0% of the import statements, which makes 73.8% of the snippets compile, compared to 36.0% of the import statements and 9.0% of the snippets by Coster. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: SnR: constraint-based type inference for incomplete Java code snippets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dong%2C+Yiwen%22">Dong, Yiwen</searchLink><relatesTo>1</relatesTo><i> y225dong@uwaterloo.ca</i><br /><searchLink fieldCode="AR" term="%22Gu%2C+Tianxiao%22">Gu, Tianxiao</searchLink><relatesTo>2</relatesTo><i> tianxiao.gu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Tian%2C+Yongqiang%22">Tian, Yongqiang</searchLink><relatesTo>1</relatesTo><i> yongqiang.tian@uwaterloo.ca</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Chengnian%22">Sun, Chengnian</searchLink><relatesTo>1</relatesTo><i> cnsun@uwaterloo.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2022, p1982-1993. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Java+programming+language%22">Java programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Datalog+%28Computer+program+language%29%22">Datalog (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+languages%22">Programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+interfaces%22">Computer interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Libraries%22">Libraries</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Code snippets are prevalent on websites such as Stack Overflow and are effective in demonstrating API usages concisely. However they are usually difficult to be used directly because most code snippets not only are syntactically incomplete but also lack dependency information, and thus do not compile. For example, Java snippets usually do not have import statements or required library names; only 6.88% of Java snippets on Stack Overflow include import statements necessary for compilation. This paper proposes SnR, a precise, efficient, constraint-based technique to automatically infer the exact types used in code snippets and the libraries containing the inferred types, to compile and therefore reuse the code snippets. Initially, SnR builds a knowledge base of APIs, i.e., various facts about the available APIs, from a corpus of Java libraries. Given a code snippet with missing import statements, SnR automatically extracts typing constraints from the snippet, solves the constraints against the knowledge base, and returns a set of APIs that satisfies the constraints to be imported into the snippet. We have evaluated SnR on a benchmark of 267 code snippets from Stack Overflow. SnR significantly outperforms the state-of-the-art tool Coster. SnR correctly infers 91.0% of the import statements, which makes 73.8% of the snippets compile, compared to 36.0% of the import statements and 9.0% of the snippets by Coster. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1145/3510003.3510061 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1982 Subjects: – SubjectFull: Java programming language Type: general – SubjectFull: Datalog (Computer program language) Type: general – SubjectFull: Programming languages Type: general – SubjectFull: Computer interfaces Type: general – SubjectFull: Libraries Type: general Titles: – TitleFull: SnR: constraint-based type inference for incomplete Java code snippets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dong, Yiwen – PersonEntity: Name: NameFull: Gu, Tianxiao – PersonEntity: Name: NameFull: Tian, Yongqiang – PersonEntity: Name: NameFull: Sun, Chengnian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2022 Type: published Y: 2022 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
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