ShellFusion: answer generation for shell programming tasks via knowledge fusion.
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| Title: | ShellFusion: answer generation for shell programming tasks via knowledge fusion. |
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| Authors: | Zhang, Neng1 zhangn279@mail.sysu.edu.cn, Liu, Chao2 liu.chao@cqu.edu.cn, Xia, Xin3 xin.xia@acm.org, Treude, Christoph4 christoph.treude@unimelb.edu.au, Zou, Ying5 ying.zou@queensu.ca, Lo, David6 davidlo@smu.edu.sg, Zheng, Zibin1 zhzibin@mail.sysu.edu.cn |
| Source: | ICSE: International Conference on Software Engineering. 2022, p1970-1981. 12p. |
| Subjects: | Computer network management, UNIX device drivers (Computer programs), Natural language processing, Artificial intelligence, QUERY (Information retrieval system) |
| Abstract: | Shell commands are widely used for accomplishing tasks, such as network management and file manipulation, in Unix and Linux platforms. There are a large number of shell commands available. For example, 50,000+ commands are documented in the Ubuntu Manual Pages (MPs). Quite often, programmers feel frustrated when searching and orchestrating appropriate shell commands to accomplish specific tasks. To address the challenge, the shell programming community calls for easy-to-use tutorials for shell commands. However, existing tutorials (e.g., TLDR) only cover a limited number of frequently used commands for shell beginners and provide limited support for users to search for commands by a task. We propose an approach, i.e., ShellFusion, to automatically generate comprehensive answers (including relevant shell commands, scripts, and explanations) for shell programming tasks. Our approach integrates knowledge mined from Q&A posts in Stack Exchange, Ubuntu MPs, and TLDR tutorials. For a query that describes a shell programming task, ShellFusion recommends a list of relevant shell commands. Specifically ShellFusion retrieves the top-n Q&A posts with questions similar to the query and detects shell commands with options (e.g., 1s -t) from the accepted answers of the retrieved posts. Next, ShellFusion filters out irrelevant commands with descriptions in MP and TLDR that share little semantics with the query, and further ranks the candidate commands based on their similarities with the query and the retrieved posts. To help users understand how to achieve the task using a recommended command, ShellFusion generates a comprehensive answer for each command by synthesizing knowledge from Q&A posts, MPs, and TLDR. Our evaluation of 434 shell programming tasks shows that ShellFusion significantly outperforms Magnum (the state-of-the-art natural language-to-Bash command approach) by at least 179.6% in terms of MRR@K and MAP@K. A user study conducted with 20 shell programmers further shows that ShellFusion can help users address programming tasks more efficiently and accurately compared with Magnum and DeepAns (a recent answer recommendation baseline). [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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| Header | DbId: egs DbLabel: Engineering Source An: 185195783 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ShellFusion: answer generation for shell programming tasks via knowledge fusion. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Neng%22">Zhang, Neng</searchLink><relatesTo>1</relatesTo><i> zhangn279@mail.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Chao%22">Liu, Chao</searchLink><relatesTo>2</relatesTo><i> liu.chao@cqu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xia%2C+Xin%22">Xia, Xin</searchLink><relatesTo>3</relatesTo><i> xin.xia@acm.org</i><br /><searchLink fieldCode="AR" term="%22Treude%2C+Christoph%22">Treude, Christoph</searchLink><relatesTo>4</relatesTo><i> christoph.treude@unimelb.edu.au</i><br /><searchLink fieldCode="AR" term="%22Zou%2C+Ying%22">Zou, Ying</searchLink><relatesTo>5</relatesTo><i> ying.zou@queensu.ca</i><br /><searchLink fieldCode="AR" term="%22Lo%2C+David%22">Lo, David</searchLink><relatesTo>6</relatesTo><i> davidlo@smu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Zibin%22">Zheng, Zibin</searchLink><relatesTo>1</relatesTo><i> zhzibin@mail.sysu.edu.cn</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, p1970-1981. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+network+management%22">Computer network management</searchLink><br /><searchLink fieldCode="DE" term="%22UNIX+device+drivers+%28Computer+programs%29%22">UNIX device drivers (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22QUERY+%28Information+retrieval+system%29%22">QUERY (Information retrieval system)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Shell commands are widely used for accomplishing tasks, such as network management and file manipulation, in Unix and Linux platforms. There are a large number of shell commands available. For example, 50,000+ commands are documented in the Ubuntu Manual Pages (MPs). Quite often, programmers feel frustrated when searching and orchestrating appropriate shell commands to accomplish specific tasks. To address the challenge, the shell programming community calls for easy-to-use tutorials for shell commands. However, existing tutorials (e.g., TLDR) only cover a limited number of frequently used commands for shell beginners and provide limited support for users to search for commands by a task. We propose an approach, i.e., ShellFusion, to automatically generate comprehensive answers (including relevant shell commands, scripts, and explanations) for shell programming tasks. Our approach integrates knowledge mined from Q&A posts in Stack Exchange, Ubuntu MPs, and TLDR tutorials. For a query that describes a shell programming task, ShellFusion recommends a list of relevant shell commands. Specifically ShellFusion retrieves the top-n Q&A posts with questions similar to the query and detects shell commands with options (e.g., 1s -t) from the accepted answers of the retrieved posts. Next, ShellFusion filters out irrelevant commands with descriptions in MP and TLDR that share little semantics with the query, and further ranks the candidate commands based on their similarities with the query and the retrieved posts. To help users understand how to achieve the task using a recommended command, ShellFusion generates a comprehensive answer for each command by synthesizing knowledge from Q&A posts, MPs, and TLDR. Our evaluation of 434 shell programming tasks shows that ShellFusion significantly outperforms Magnum (the state-of-the-art natural language-to-Bash command approach) by at least 179.6% in terms of MRR@K and MAP@K. A user study conducted with 20 shell programmers further shows that ShellFusion can help users address programming tasks more efficiently and accurately compared with Magnum and DeepAns (a recent answer recommendation baseline). [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: 10.1145/3510003.3510131 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1970 Subjects: – SubjectFull: Computer network management Type: general – SubjectFull: UNIX device drivers (Computer programs) Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: QUERY (Information retrieval system) Type: general Titles: – TitleFull: ShellFusion: answer generation for shell programming tasks via knowledge fusion. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Neng – PersonEntity: Name: NameFull: Liu, Chao – PersonEntity: Name: NameFull: Xia, Xin – PersonEntity: Name: NameFull: Treude, Christoph – PersonEntity: Name: NameFull: Zou, Ying – PersonEntity: Name: NameFull: Lo, David – PersonEntity: Name: NameFull: Zheng, Zibin 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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