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On the Applicability of Language Models to Block-Based Programs.

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Title: On the Applicability of Language Models to Block-Based Programs.
Authors: Griebl, Elisabeth1 elisabeth.griebl@uni-passau.de, Fein, Benedikt1 benedikt.fein@uni-passau.de, Obermüller, Florian1 florian.obermueller@uni-passau.de, Fraser, Gordon1 gordon.fraser@uni-passau.de, Just, René2 rjust@cs.washington.edu
Source: ICSE: International Conference on Software Engineering. 2023, p2374-2386. 13p.
Subjects: Language models, End-user computing software, Source code, Natural language processing, Deep learning
Abstract: Block-based programming languages like SCRATCH are increasingly popular for programming education and end-user programming. Recent program analyses build on the insight that source code can be modelled using techniques from natural language processing. Many of the regularities of source code that support this approach are due to the syntactic overhead imposed by textual programming languages. This syntactic overhead, however, is precisely what block-based languages remove in order to simplify programming. Consequently, it is unclear how well this modelling approach performs on block-based programming languages. In this paper, we investigate the applicability of language models for the popular block-based programming language SCRATCH. We model SCRATCH programs using n-gram models, the most essential type of language model, and transformers, a popular deep learning model. Evaluation on the example tasks of code completion and bug finding confirm that blocks inhibit predictability, but the use of language models is nevertheless feasible. Our findings serve as foundation for improving tooling and analyses for block-based languages. [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.)
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  Data: On the Applicability of Language Models to Block-Based Programs.
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  Data: <searchLink fieldCode="AR" term="%22Griebl%2C+Elisabeth%22">Griebl, Elisabeth</searchLink><relatesTo>1</relatesTo><i> elisabeth.griebl@uni-passau.de</i><br /><searchLink fieldCode="AR" term="%22Fein%2C+Benedikt%22">Fein, Benedikt</searchLink><relatesTo>1</relatesTo><i> benedikt.fein@uni-passau.de</i><br /><searchLink fieldCode="AR" term="%22Obermüller%2C+Florian%22">Obermüller, Florian</searchLink><relatesTo>1</relatesTo><i> florian.obermueller@uni-passau.de</i><br /><searchLink fieldCode="AR" term="%22Fraser%2C+Gordon%22">Fraser, Gordon</searchLink><relatesTo>1</relatesTo><i> gordon.fraser@uni-passau.de</i><br /><searchLink fieldCode="AR" term="%22Just%2C+René%22">Just, René</searchLink><relatesTo>2</relatesTo><i> rjust@cs.washington.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2023, p2374-2386. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22End-user+computing+software%22">End-user computing software</searchLink><br /><searchLink fieldCode="DE" term="%22Source+code%22">Source code</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: Block-based programming languages like SCRATCH are increasingly popular for programming education and end-user programming. Recent program analyses build on the insight that source code can be modelled using techniques from natural language processing. Many of the regularities of source code that support this approach are due to the syntactic overhead imposed by textual programming languages. This syntactic overhead, however, is precisely what block-based languages remove in order to simplify programming. Consequently, it is unclear how well this modelling approach performs on block-based programming languages. In this paper, we investigate the applicability of language models for the popular block-based programming language SCRATCH. We model SCRATCH programs using n-gram models, the most essential type of language model, and transformers, a popular deep learning model. Evaluation on the example tasks of code completion and bug finding confirm that blocks inhibit predictability, but the use of language models is nevertheless feasible. Our findings serve as foundation for improving tooling and analyses for block-based languages. [ABSTRACT FROM AUTHOR]
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  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:
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      – Type: doi
        Value: 10.1109/ICSE48619.2023.00199
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      – Code: eng
        Text: English
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        PageCount: 13
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      – SubjectFull: Language models
        Type: general
      – SubjectFull: End-user computing software
        Type: general
      – SubjectFull: Source code
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Deep learning
        Type: general
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      – TitleFull: On the Applicability of Language Models to Block-Based Programs.
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            – D: 01
              M: 05
              Text: 2023
              Type: published
              Y: 2023
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