This Project Grant award from the National Science Foundation's Biological Sciences (CFDA 47.074) program provides $151,677 to the University of Nebraska at Omaha (UNO) to develop novel natural language processing methods for ontology-based named entity recognition and robust semantic similarity metrics in the domain of biological literature. The goal is to advance the state-of-the-art in machine learning models for automated curation and knowledge discovery from scientific publications. Key...
The National Science Foundation (NSF) awarded a $329,183 Project Grant to the College of William & Mary under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant, awarded on October 1, 2023, will fund the development of a framework and methodology to enable researchers and software engineers to better interpret the behavior of AI-powered developer tools that leverage neural language models for source code. The project aims to generate global and local...
This federal Project Grant award, totaling $300,000.00, was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a collaborative research project that aims to develop new semantic metrics to measure code complexity and guide software developers in writing more comprehensible code. The key objectives are to: (1) validate the correlation between code verifiability and human comprehension, (2)...
This Project Grant from the National Science Foundation's Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), provides $147,518 to develop a software health monitoring and improvement framework. The framework will integrate and enhance recent advances in software issue detection and refactoring techniques to serve diverse scientific and engineering domains. It will detect and help fix software quality issues in...
This $100,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) program aims to develop a holistic benchmarking infrastructure for evaluating large language models used in software engineering. The key activities include: Conducting surveys and interviews with the software engineering and machine learning research communities to gather requirements and understand barriers in evaluating large language models for code....
This $550,000 award from the National Science Foundation's (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program supports the development of a hybrid, scalable data management system to improve access to scientific knowledge in data science. The project aims to create an intelligent, user-friendly interface that can extract, organize, and provide deep access to relevant concepts from peer-reviewed scientific literature. Key objectives include: Building a comprehensive knowledge...
The National Science Foundation awarded a $275,000 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering program (CFDA 47.070). The three-year award will support the development of program synthesis techniques to help software developers manage schema changes to databases. The project aims to simplify the schema modification process through automated techniques for migrating data between formats and updating code to reflect schema...
This $600,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to enable developers to perform fine-grained software testing, thereby increasing software quality. The key objectives are to: (1) develop a language and framework for expressing and using fine-grained tests; (2) automatically generate fine-grained tests from code or existing tests; (3) adapt fine-grained tests to software evolution and...
This National Science Foundation (NSF) $900,000 "COLLABORATIVE RESEARCH: SHF: MEDIUM: NATURAL LANGUAGE MODELS WITH EXECUTION DATA FOR SOFTWARE TESTING" Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), aims to develop natural language processing (NLP) models to simplify the development and maintenance of software tests. Key objectives include test generation, completion, update, and migration across programming languages, targeting...
This $256,710 National Science Foundation award under the Computer and Information Science and Engineering (CFDA 47.070) program supports a collaborative research project led by the University of Texas at Austin. The project investigates full-stack implementation methodologies for developing expressive programming systems that bridge the gap between high-level specifications and high-performance implementations of complex reasoning tasks at scale. Key focus areas include extending declarative...