mlsanityData to 7 Oct 2026

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

arXiv 2304.13032 ↗First posted 6 Apr 2023cs.SE cs.AI cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 33 of its 68 references that are in the map. First posted on arXiv 6 Apr 2023.

Builds on

33 of 68 references.

References · 33

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21 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2024Deep Configuration Performance Learning: A Systematic Survey and Taxonomycited by 8
  2. 2023PACE: A Program Analysis Framework for Continuous Performance Predictioncited by 3
  3. 2023Reducing Computational Costs in Sentiment Analysis: Tensorized Recurrent Networks vs. Recurrent Networkscited by 0
  4. 2024Analysing the Behaviour of Tree-Based Neural Networks in Regression Taskscited by 0