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Graph neural network-based mutation-aware regression test ordering using code dependency graphs and execution traces.

S Sowmyadevi1, Anna Alphy1

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR campus, Ghaziabad, Utter Pradesh 201204, India.

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|January 19, 2026
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Summary
This summary is machine-generated.

This study introduces a mutation-aware test prioritization system using Graph Neural Networks (GNNs) to improve regression testing. The GNN-based approach significantly enhances fault detection and mutation coverage, outperforming existing methods.

Keywords:
APFDFault DetectionGraph neural networksMutation testingRegression testingSoftware quality assuranceTest prioritization

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Regression testing is crucial for software quality but faces challenges in efficiently prioritizing tests.
  • Existing test prioritization methods often struggle to balance fault detection, execution cost, and coverage.
  • Mutation testing, while effective, can be computationally expensive and requires sophisticated prioritization strategies.

Purpose of the Study:

  • To develop a novel mutation-aware test prioritization system using Graph Neural Networks (GNNs).
  • To enhance regression testing by integrating static program structure, dynamic execution traces, and mutation coverage.
  • To achieve a superior balance between fault detection, execution cost, and mutation coverage in test prioritization.

Main Methods:

  • Constructing a hybrid graph representation by combining Program Dependence Graphs and Call Graphs with runtime characteristics.
  • Employing GNN variations (GCN, GAT, GraphSAGE) to embed higher-order dependencies within test cases.
  • Utilizing a multi-objective optimization function to rank test cases based on fault detection, execution cost, and mutation coverage.

Main Results:

  • Achieved an average APFD (Average Percentage of Faults Detected) of 88.9%, significantly outperforming traditional baselines (74.5%) and ML baselines (82.7%).
  • Attained a mutation score of 84.6% with a minimal execution overhead of 16.1 seconds.
  • Statistical significance (Wilcoxon signed-rank test, p < 0.05) confirmed the robustness of the performance gains.

Conclusions:

  • The proposed GNN-based mutation-aware test prioritization system offers a scalable, accurate, and mutation-driven paradigm for modern regression testing.
  • Integrating execution traces and mutation characteristics is vital for effective test prioritization, as indicated by ablation studies.
  • GNN embeddings provide interpretable prioritization by clustering fault-related test cases.