Active learning for software engineering
Name
onward-2019.pdf
Description
Accepted version
Size
773.48 KB
Format
Adobe PDF
Checksum (MD5)
4373e02781e116b951a37d28d19e8e77
Author(s)
Cambronero, José P.
Dang, Thurston H. Y.
Vasilakis, Nikos
Shen, Jiasi
Wu, Jerry
Rinard, Martin C.
Date Issued
October 23, 2019
Journal
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
Publisher
ACM
Citation
Cambronero, José P., Dang, Thurston H. Y., Vasilakis, Nikos, Shen, Jiasi, Wu, Jerry et al. 2019. "Active learning for software engineering." 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.
Version
Author's final manuscript
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3359591.3359732
