Toward Neurosymbolic Program Comprehension
Recent advancements in Large Language Models (LLMs) have paved the way for Large Code Models (LCMs), enabling automation in complex software engineering tasks, such as code generation, software testing, and program comprehension, among others. Tools like GitHub Copilot and ChatGPT have shown substantial benefits in supporting developers across various practices. However, the ambition to scale these models to trillion-parameter sizes, exemplified by GPT-4, poses significant challenges that limit the usage of Artificial Intelligence (AI)-based systems powered by large Deep Learning (DL) models. These include rising computational demands for training and deployment and issues related to trustworthiness, bias, and interpretability. Such factors can make managing these models impractical for many organizations, while their "black-box'' nature undermines key aspects, including transparency and accountability. In this paper, we question the prevailing assumption that increasing model parameters is always the optimal path forward, provided there is sufficient new data to learn additional patterns. In particular, we advocate for a Neurosymbolic research direction that combines the strengths of existing DL techniques (e.g., LLMs) with traditional symbolic methods–renowned for their reliability, speed, and determinism. To this end, we outline the core features and present preliminary results for our envisioned approach, aimed at establishing the first NeuroSymbolic Program Comprehension (NsPC) framework to aid in identifying defective code components.
Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | Empirical Findings, Future Visions, Recommendations Replications and Negative Results (RENE) / Early Research Achievements (ERA) / Tool Demonstration / Research Track at 205 Chair(s): Mark Hills Appalachian State University, Coen De Roover Vrije Universiteit Brussel, Gema Rodríguez-Pérez Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus | ||
11:00 10mTalk | Terminal Lucidity: Envisioning the Future of the Terminal Research Track Michael MacInnis Carleton University, Olga Baysal Carleton University, Michele Lanza Software Institute - USI, Lugano Pre-print | ||
11:10 6mTalk | Exploring Code Comprehension in Scientific Programming: Preliminary Insights from Research Scientists Early Research Achievements (ERA) Alyssia Chen University of Hawaii at Manoa, Carol Wong University of Hawaii at Manoa, Bonita Sharif University of Nebraska-Lincoln, USA, Anthony Peruma University of Hawai‘i at Mānoa Pre-print | ||
11:16 10mTalk | Method Names in Jupyter Notebooks: An Exploratory Study Research Track Carol Wong University of Hawaii at Manoa, Gunnar Larsen University of Hawaii at Manoa, Rocky Huang University of Hawaii at Manoa, Bonita Sharif University of Nebraska-Lincoln, USA, Anthony Peruma University of Hawai‘i at Mānoa | ||
11:26 6mTalk | SCALAR: A Part-of-speech Tagger for Identifiers Tool Demonstration Christian Newman , Brandon Scholten Kent State University, Sophia Testa Kent State University, Joshua Behler Kent State University, Syreen Banabilah Kent State University, Michael L. Collard The University of Akron, Michael J. Decker Bowling Green State University, Mohamed Wiem Mkaouer University of Michigan - Flint, Marcos Zampieri George mason University, Eman Abdullah AlOmar Stevens Institute of Technology, USA, Reem Alsuhaibani Prince Sultan University, Anthony Peruma University of Hawai‘i at Mānoa, Jonathan I. Maletic Kent State University | ||
11:32 6mTalk | How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow Early Research Achievements (ERA) Federica Pepe University of Sannio, Claudia Farkas York University, Maleknaz Nayebi York University, Giulio Antoniol Ecole Polytechnique de Montreal, Massimiliano Di Penta University of Sannio, Italy | ||
11:38 4mTalk | Toward Neurosymbolic Program Comprehension Early Research Achievements (ERA) Alejandro Velasco William & Mary, Aya Garryyeva William and Mary, David Nader Palacio William & Mary, Antonio Mastropaolo William and Mary, USA, Denys Poshyvanyk William & Mary Pre-print | ||
11:42 10mTalk | Combining Static Analysis Techniques for Program Comprehension Using Slicito Tool Demonstration Pre-print File Attached | ||
11:52 6mTalk | Mining Code Change Patterns in Ada Projects Replications and Negative Results (RENE) | ||
11:58 6mTalk | Telling Software Evolution Stories With Sonification Early Research Achievements (ERA) | ||
12:04 10mTalk | Attributed Multiplex Learning for Analogical Third-Party Library Recommendation and Retrieval Research Track Baihui Sang State Key Laboratory for Novel Software Technology, Nanjing University, Liang Wang Nanjing University, Jierui Zhang Nanjing University, Xianping Tao Nanjing University | ||
12:14 6mTalk | LLM2FedLLM - A Tool for Simulating Federated LLMs for Software Engineering Tasks Tool Demonstration Jahnavi Kumar Indian Institute of Technology Tirupati, India, Siddhartha Gandu Indian Institute of Technology Tirupati, Sridhar Chimalakonda Indian Institute of Technology Tirupati | ||
12:20 10mLive Q&A | Session's Discussion: "Empirical Findings, Future Visions, Recommendations" Research Track | ||