A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction

• URL: http://arxiv.org/abs/2304.13032v2
• Date: Wed, 20 Sep 2023 11:18:08 GMT
• Title: A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction
• Authors: Peter Samoaa, Linus Aronsson, Antonio Longa, Philipp Leitner, Morteza Haghir Chehreghani
• Abstract summary: We develop a unified active learning framework specializing in software performance prediction. We investigate the impact of using different levels of information for active and passive learning. Our approach aims to improve the investment in AI models for different software performance predictions.
• Score: 4.572330678291241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most machine learning and data analytics applications, including performance engineering in software systems, require a large number of annotations and labelled data, which might not be available in advance. Acquiring annotations often requires significant time, effort, and computational resources, making it challenging. We develop a unified active learning framework specializing in software performance prediction to address this task. We begin by parsing the source code to an Abstract Syntax Tree (AST) and augmenting it with data and control flow edges. Then, we convert the tree representation of the source code to a Flow Augmented-AST graph (FA-AST) representation. Based on the graph representation, we construct various graph embeddings (unsupervised and supervised) into a latent space. Given such an embedding, the framework becomes task agnostic since active learning can be performed using any regression method and query strategy suited for regression. Within this framework, we investigate the impact of using different levels of information for active and passive learning, e.g., partially available labels and unlabeled test data. Our approach aims to improve the investment in AI models for different software performance predictions (execution time) based on the structure of the source code. Our real-world experiments reveal that respectable performance can be achieved by querying labels for only a small subset of all the data.

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