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Found 8 papers, 1 papers with code
COMET: Generating Commit Messages using Delta Graph Context Representation
We propose Comet (Context-Aware Commit Message Generation), a novel approach that captures context of code changes using a graph-based representation and leverages a transformer-based model to generate high-quality commit messages.
CONCORD: Towards a DSL for Configurable Graph Code Representation
CONCORD will help researchers a) create and experiment with customizable graph-based code representations for different software engineering tasks involving DL, b) reduce the engineering work to generate graph representations, c) address the issue of scalability in GNN models, and d) enhance the reproducibility of experiments in research through a standardized approach to code representation and analysis.
Naturalness of Attention: Revisiting Attention in Code Language Models
Language models for code such as CodeBERT offer the capability to learn advanced source code representation, but their opacity poses barriers to understanding of captured properties.
Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
This work will facilitate further advances in DL energy measurement and the development of energy-aware practices for DL systems.
Towards Real-Time Analysis of Broadcast Badminton Videos
In this work, we propose an end-to-end framework for player movement analysis for badminton matches on live broadcast match videos.
Quantifying Outlierness of Funds from their Categories using Supervised Similarity
Mutual fund categorization has become a standard tool for the investment management industry and is extensively used by allocators for portfolio construction and manager selection, as well as by fund managers for peer analysis and competitive positioning.
DACOS-A Manually Annotated Dataset of Code Smells
Researchers apply machine-learning techniques for code smell detection to counter the subjectivity of many code smells.
A Survey on Machine Learning Techniques for Source Code Analysis
This paper aims to summarize the current knowledge in applied machine learning for source code analysis.
