Active learning for inference and regeneration of computer programs that store and retrieve data
As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that many of the computations implement. We present a new approach for obtaining sof...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM Press
2020
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| Online Access: | https://hdl.handle.net/1721.1/125749 |
| _version_ | 1826188949839675392 |
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| author | Rinard, Martin C Shen, Jiasi Mangalick, Varun |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Rinard, Martin C Shen, Jiasi Mangalick, Varun |
| author_sort | Rinard, Martin C |
| collection | MIT |
| description | As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that many of the computations implement. We present a new approach for obtaining software that executes on modern computing platforms with complex programming interfaces. Our approach starts with a simple seed program, written in the language of the developer's choice, that implements the desired core functionality. It then systematically generates inputs and observes the resulting outputs to learn the core functionality. It finally automatically regenerates new code that implements the learned core functionality on the target computing platform. This regenerated code contains boilerplate code for the complex programming interfaces that the target computing platform presents. By providing a productive new mechanism for capturing and encapsulating knowledge about how to use modern complex interfaces, this new approach promises to greatly reduce the developer effort required to obtain secure, robust software that executes on modern computing platforms. |
| first_indexed | 2024-09-23T08:07:11Z |
| format | Article |
| id | mit-1721.1/125749 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:07:11Z |
| publishDate | 2020 |
| publisher | ACM Press |
| record_format | dspace |
| spelling | mit-1721.1/1257492022-09-23T11:01:39Z Active learning for inference and regeneration of computer programs that store and retrieve data Rinard, Martin C Shen, Jiasi Mangalick, Varun Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that many of the computations implement. We present a new approach for obtaining software that executes on modern computing platforms with complex programming interfaces. Our approach starts with a simple seed program, written in the language of the developer's choice, that implements the desired core functionality. It then systematically generates inputs and observes the resulting outputs to learn the core functionality. It finally automatically regenerates new code that implements the learned core functionality on the target computing platform. This regenerated code contains boilerplate code for the complex programming interfaces that the target computing platform presents. By providing a productive new mechanism for capturing and encapsulating knowledge about how to use modern complex interfaces, this new approach promises to greatly reduce the developer effort required to obtain secure, robust software that executes on modern computing platforms. DARPA (Grant FA8650-15-C-7564) 2020-06-09T20:07:25Z 2020-06-09T20:07:25Z 2018-10 2019-07-02T16:31:33Z Article http://purl.org/eprint/type/ConferencePaper 9781450360319 https://hdl.handle.net/1721.1/125749 Rinard, Martin C., Jiasi Shen, and Varun Mangalick. "Active learning for inference and regeneration of computer programs that store and retrieve data." ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, October 2018, Boston, MA, USA (ACM), 2018. en http://dx.doi.org/10.1145/3276954.3276959 Proceedings of the 2018 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ACM Press MIT web domain |
| spellingShingle | Rinard, Martin C Shen, Jiasi Mangalick, Varun Active learning for inference and regeneration of computer programs that store and retrieve data |
| title | Active learning for inference and regeneration of computer programs that store and retrieve data |
| title_full | Active learning for inference and regeneration of computer programs that store and retrieve data |
| title_fullStr | Active learning for inference and regeneration of computer programs that store and retrieve data |
| title_full_unstemmed | Active learning for inference and regeneration of computer programs that store and retrieve data |
| title_short | Active learning for inference and regeneration of computer programs that store and retrieve data |
| title_sort | active learning for inference and regeneration of computer programs that store and retrieve data |
| url | https://hdl.handle.net/1721.1/125749 |
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