| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | June 15, 2022 |
| Latest Amendment Date: | August 19, 2024 |
| Award Number: | 2211428 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Andrian Marcus
amarcus@nsf.gov (703)292-0000 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | June 15, 2022 |
| End Date: | May 31, 2026 (Estimated) |
| Total Intended Award Amount: | $864,000.00 |
| Total Awarded Amount to Date: | $992,000.00 |
| Funds Obligated to Date: |
FY 2023 = $128,000.00 FY 2024 = $218,062.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
940 GRACE HALL NOTRE DAME IN US 46556-5708 (574)631-7432 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
940 Grace Hall NOTRE DAME IN US 46556-5708 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
CISE Education and Workforce, Software & Hardware Foundation |
| Primary Program Source: |
01002324DB NSF RESEARCH & RELATED ACTIVIT 01002425DB NSF RESEARCH & RELATED ACTIVIT 01002526DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The research objective of this project is to design novel artificial intelligence-based models of software that learn from and are informed by human behavior. The frontier of many areas of Software Engineering (SE) research involves applications of AI-based models to SE tasks. Many tasks in SE research rely on the same basic underpinning technologies, often a neural representation of source code that is trained to find features in code, which are then used for various tasks e.g., to predict words for a document or areas of code likely to contain a bug. While the first applications of recurrent neural network-based encoder-decoder models were a paradigm shift over the manually-crafted heuristics and rules that the neural models replaced, subsequent changes have yielded less improvement despite increased sophistication.
The vision of this project is to achieve a breakthrough in more human-like neural models of source code. Its aim is to advance a broad spectrum of SE research tasks that rely on neural models, by improving the neural models of code that underpin many downstream tasks. The research plan is three-fold: First, the project will characterize human behavior during different SE tasks via eye-tracking and IDE-based experiments. Second, the project will design models that predict or even mimic human behavior. Third, the project will use those models to augment and improve neural representations of source code, and evaluate these new representations in a variety of SE tasks.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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