15 citations · 25 across the 20 of their papers we have counts for
20 papers
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…
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…
Prompting with Pseudo-Code Instructions
Mayank Mishra, Prince Kumar, Riyaz Bhat +3
Prompting with natural language instructions has recently emerged as a popular method of harnessing the capabilities of large language models. Given the inherent ambiguity present…
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…
Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective Genetic Optimization Guided By Deep Networks
Alex Mathai, Shreya Khare, Srikanth Tamilselvam +1
We propose a novel genetic-algorithm technique that generates black-box adversarial examples which successfully fool neural network based text classifiers. We perform a genetic sea…
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…
Benchmarking Popular Classification Models' Robustness to Random and Targeted Corruptions
Utkarsh Desai, Srikanth Tamilselvam, Jassimran Kaur +2
Text classification models, especially neural networks based models, have reached very high accuracy on many popular benchmark datasets. Yet, such models when deployed in real worl…
Coverage Testing of Deep Learning Models using Dataset Characterization
Senthil Mani, Anush Sankaran, Srikanth Tamilselvam +1
Deep Neural Networks (DNNs), with its promising performance, are being increasingly used in safety critical applications such as autonomous driving, cancer detection, and secure au…
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
Ameya Prabhu, Riddhiman Dasgupta, Anush Sankaran +2
For an unknown (new) classification dataset, choosing an appropriate deep learning architecture is often a recursive, time-taking, and laborious process. In this research, we propo…
Compliance Change Tracking in Business Process Services
Srikanth G Tamilselvam, Ankush Gupta, Arvind Agarwal
Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining a…
A Visual Programming Paradigm for Abstract Deep Learning Model Development
Srikanth Tamilselvam, Naveen Panwar, Shreya Khare +3
Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumpti…
"Let me convince you to buy my product ... ": A Case Study of an Automated Persuasive System for Fashion Products
Vitobha Munigala, Srikanth Tamilselvam, Anush Sankaran
Persuasivenes is a creative art aimed at making people believe in certain set of beliefs. Many a times, such creativity is about adapting richness of one domain into another to str…