15 citations · 25 across the 20 of their papers we have counts for
11 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…
Handling Communication via APIs for Microservices
Vini Kanvar, Ridhi Jain, Srikanth Tamilselvam
Enterprises in their journey to the cloud, want to decompose their monolith applications into microservices to maximize cloud benefits. Current research focuses a lot on how to par…
COMEX: A Tool for Generating Customized Source Code Representations
Debeshee Das, Noble Saji Mathews, Alex Mathai +4
Learning effective representations of source code is critical for any Machine Learning for Software Engineering (ML4SE) system. Inspired by natural language processing, large langu…
Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network
Alex Mathai, Sambaran Bandyopadhyay, Utkarsh Desai +1
Monolithic software encapsulates all functional capabilities into a single deployable unit. But managing it becomes harder as the demand for new functionalities grow. Microservice…
Graph Neural Network to Dilute Outliers for Refactoring Monolith Application
Utkarsh Desai, Sambaran Bandyopadhyay, Srikanth Tamilselvam
Microservices are becoming the defacto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can ha…
Evaluation of Siamese Networks for Semantic Code Search
Raunak Sinha, Utkarsh Desai, Srikanth Tamilselvam +1
With the increase in the number of open repositories and discussion forums, the use of natural language for semantic code search has become increasingly common. The accuracy of the…