A unified active learning framework for annotating graph data with application to software source code performance prediction
Most machine learning and data analytics applications, including performance engineering
in software systems, require a large number of annotations and labelled data, which might …
in software systems, require a large number of annotations and labelled data, which might …
Adding context to source code representations for deep learning
F Tian, C Treude - 2022 IEEE International Conference on …, 2022 - ieeexplore.ieee.org
Deep learning models have been successfully applied to a variety of software engineering
tasks, such as code classification, summarisation, and bug and vulnerability detection. In …
tasks, such as code classification, summarisation, and bug and vulnerability detection. In …
Tep-gnn: Accurate execution time prediction of functional tests using graph neural networks
Predicting the performance of production code prior to actual execution is known to be
highly challenging. In this paper, we propose a predictive model, dubbed TEP-GNN, which …
highly challenging. In this paper, we propose a predictive model, dubbed TEP-GNN, which …
Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?
Programming language understanding and representation (aka code representation
learning) has always been a hot and challenging task in software engineering. It aims to …
learning) has always been a hot and challenging task in software engineering. It aims to …
A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks
B Casey, J Santos, G Perry - arXiv preprint arXiv:2403.10646, 2024 - arxiv.org
Machine learning techniques for cybersecurity-related software engineering tasks are
becoming increasingly popular. The representation of source code is a key portion of the …
becoming increasingly popular. The representation of source code is a key portion of the …
Towards Automatic Support of Software Model Evolution with Large Language~ Models
Modeling structure and behavior of software systems plays a crucial role, in various areas of
software engineering. As with other software engineering artifacts, software models are …
software engineering. As with other software engineering artifacts, software models are …
Software defect prediction: future directions and challenges
Software defect prediction is one of the most popular research topics in software
engineering. The objective of defect prediction is to identify defective instances prior to the …
engineering. The objective of defect prediction is to identify defective instances prior to the …
Encoding Version History Context for Better Code Representation
With the exponential growth of AI tools that generate source code, understanding software
has become crucial. When developers comprehend a program, they may refer to additional …
has become crucial. When developers comprehend a program, they may refer to additional …
Batch Mode Deep Active Learning for Regression on Graph Data
Acquiring labelled data for machine learning tasks, for example, for software performance
prediction, remains a resource-intensive task. This study extends our previous work by …
prediction, remains a resource-intensive task. This study extends our previous work by …
Analysing the Behaviour of Tree-Based Neural Networks in Regression Tasks
The landscape of deep learning has vastly expanded the frontiers of source code analysis,
particularly through the utilization of structural representations such as Abstract Syntax Trees …
particularly through the utilization of structural representations such as Abstract Syntax Trees …