15 citations · 25 across the 20 of their papers we have counts for
7 papers · 1 filter
CodeSAM: Source Code Representation Learning by Infusing Self-Attention with Multi-Code-View Graphs
Alex Mathai, Kranthi Sedamaki, Debeshee Das +4
Machine Learning (ML) for software engineering (SE) has gained prominence due to its ability to significantly enhance the performance of various SE applications. This progress is l…
Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights
Rahul Krishna, Rangeet Pan, Raju Pavuluri +3
Large Language Models for Code (or code LLMs) are increasingly gaining popularity and capabilities, offering a wide array of functionalities such as code completion, code generatio…
ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries
Kishan Maharaj, Vitobha Munigala, Srikanth G. Tamilselvam +5
Recent advancements in large language models (LLMs) have significantly enhanced their ability to understand both natural language and code, driving their use in tasks like natural…
Enabling Communication via APIs for Mainframe Applications
Vini Kanvar, Srikanth Tamilselvam, Ashwin Dhinesh Kumar +1
Mainframe systems continue to support critical applications across industries such as banking, retail, and healthcare. Exposing their functionality through Application Programming…
DocCGen: Document-based Controlled Code Generation
Sameer Pimparkhede, Mehant Kammakomati, Srikanth Tamilselvam +3
Recent developments show that Large Language Models (LLMs) produce state-of-the-art performance on natural language (NL) to code generation for resource-rich general-purpose langua…
ConCodeEval: Evaluating Large Language Models for Code Constraints in Domain-Specific Languages
Mehant Kammakomati, Sameer Pimparkhede, Srikanth Tamilselvam +2
Recent work shows Large Language Models (LLMs) struggle to understand natural language constraints for various text generation tasks in zero- and few-shot settings. While, in the c…
Read between the lines -- Functionality Extraction From READMEs
Prince Kumar, Srikanth Tamilselvam, Dinesh Garg
While text summarization is a well-known NLP task, in this paper, we introduce a novel and useful variant of it called functionality extraction from Git README files. Though this t…