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01598 am a22002173u 4500 |
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137593 |
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|a Cambronero, José P.
|e author
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|a Dang, Thurston H. Y.
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|a Vasilakis, Nikos
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|a Shen, Jiasi
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|a Wu, Jerry
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|a Rinard, Martin C.
|e author
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|a Active learning for software engineering
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|b ACM,
|c 2021-11-05T19:16:58Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/137593
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|a © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Software applications have grown increasingly complex to deliver the features desired by users. Software modularity has been used as a way to mitigate the costs of developing such complex software. Active learning-based program inference provides an elegant framework that exploits this modularity to tackle development correctness, performance and cost in large applications. Inferred programs can be used for many purposes, including generation of secure code, code re-use through automatic encapsulation, adaptation to new platforms or languages, and optimization. We show through detailed examples how our approach can infer three modules in a representative application. Finally, we outline the broader paradigm and open research questions.
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|a Article
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|t 10.1145/3359591.3359732
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|t Onward! 2019 - Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, co-located with SPLASH 2019
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