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Most machine learning and data analytics applications, including performanceengineering in software systems, require a large number of annotations andlabelled data, which might not be available in advance. Acquiring annotationsoften requires significant time, effort, and computational resources, making itchallenging. We develop a unified active learning framework specializing insoftware performance prediction to address this task. We begin by parsing thesource code to an Abstract Syntax Tree (AST) and augmenting it with data andcontrol flow edges. Then, we convert the tree representation of the source codeto a Flow Augmented-AST graph (FA-AST) representation. Based on the graphrepresentation, we construct various graph embeddings (unsupervised andsupervised) into a latent space. Given such an embedding, the framework becomestask agnostic since active learning can be performed using any regressionmethod and query strategy suited for regression. Within this framework, weinvestigate the impact of using different levels of information for active andpassive learning, e.g., partially available labels and unlabeled test data. Ourapproach aims to improve the investment in AI models for different softwareperformance predictions (execution time) based on the structure of the sourcecode. Our real-world experiments reveal that respectable performance can beachieved by querying labels for only a small subset of all the data.

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