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Found 5 papers, 1 papers with code
Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice
Large Language Models (LLMs) are frequently discussed in academia and the general public as support tools for virtually any use case that relies on the production of text, including software engineering.
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction
Then, we convert the tree representation of the source code to a Flow Augmented-AST graph (FA-AST) representation.
Machine Learning Systems are Bloated and Vulnerable
Today's software is bloated with both code and features that are not used by most users.
TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks
TEP-GNN uses FA-ASTs, or flow-augmented ASTs, as a graph-based code representation approach, and predicts test execution times using a powerful graph neural network (GNN) deep learning model.
The State of Research on Function-as-a-Service Performance Evaluation: A Multivocal Literature Review
Function-as-a-Service (FaaS) is one form of the serverless cloud computing paradigm and is defined through FaaS platforms (e. g., AWS Lambda) executing event-triggered code snippets (i. e., functions).
Performance Distributed, Parallel, and Cluster Computing
