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Found 13 papers, 6 papers with code
Read between the lines -- Functionality Extraction From READMEs
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.
COMEX: A Tool for Generating Customized Source Code Representations
To overcome this barrier, we propose our tool COMEX - a framework that allows researchers and developers to create and combine multiple code-views which can be used by machine learning (ML) models for various SE tasks.
Prompting with Pseudo-Code Instructions
Prompting with natural language instructions has recently emerged as a popular method of harnessing the capabilities of large language models.
Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network
But the challenges associated with the separation of functional modules, slows down the migration of a monolithic code into microservices.
Graph Neural Network to Dilute Outliers for Refactoring Monolith Application
Therefore, this problem of refactoring can be viewed as a graph based clustering task.
Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective Genetic Optimization Guided By Deep Networks
On an average, we achieve an attack success rate of 65. 67% for SST and 36. 45% for IMDB across the three models showing an improvement of 49. 48% and 101% respectively.
Evaluation of Siamese Networks for Semantic Code Search
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.
Benchmarking Popular Classification Models' Robustness to Random and Targeted Corruptions
This emphasizes the need for a model agnostic test dataset, which consists of various corruptions that are natural to appear in the wild.
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
Further, we predict the performance accuracy of the recommended architecture on the given unknown dataset, without the need for training the model.
Coverage Testing of Deep Learning Models using Dataset Characterization
Further, we conduct various experiments to demonstrate the effectiveness of systematic test case generation system for evaluating deep learning models.
A Visual Programming Paradigm for Abstract Deep Learning Model Development
Deep learning is one of the fastest growing technologies in computer science with a plethora of applications.
Graph Based Sentiment Aggregation using ConceptNet Ontology
The sentiment aggregation problem accounts for analyzing the sentiment of a user towards various aspects/features of a product, and meaningfully assimilating the pragmatic significance of these features/aspects from an opinionated text.
"Let me convince you to buy my product ... ": A Case Study of an Automated Persuasive System for Fashion Products
Persuasivenes is a creative art aimed at making people believe in certain set of beliefs.
