Journal of Trends in Computer Science and Smart Technology
IRO Journals
Volume 8 — Issue 1 — March 2026

Graph Neural Network and Reinforcement Learning Framework for Test Case Prioritization, Selection, and Reduction

https://doi.org/10.36548/jtcsst.2026.1.007
Pages: 130-154
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Sowmyadevi S.
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India
Shashi Mehrotra
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India
March 2026
Volume 8 — Issue 1
Journal of Trends in Computer Science and Smart Technology
PDF

How to Cite

S., Sowmyadevi, and Shashi Mehrotra. 2026. “Graph Neural Network and Reinforcement Learning Framework for Test Case Prioritization, Selection, and Reduction”. Journal of Trends in Computer Science and Smart Technology 8 (1): 130-54. https://doi.org/10.36548/jtcsst.2026.1.007.
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Keywords

— Regression Testing
— Test Case Prioritization
— Test Suite Reduction
— Test Case Selection
— Graph Neural Networks
— Reinforcement Learning
— Search-Based Software Engineering
Published: 19-03-2026

Abstract

The process of regression testing is generally done under critical time and resource constraints. In the existing approaches, Test Case Prioritization (TCP), Test Case Selection (TCS), and Test Suite Reduction (TSR) have been treated as different optimization problems. This is based on simple heuristics that do not use valuable information that can be extracted from the program under test. Moreover, they do not allow for making internally consistent budget-conscious decisions. This paper introduces a unified approach that combines various variants of Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for the solutions of TCP, TCS, and TSR. In our proposal, the representation of regression artifacts such as test cases, code entities, and faults is used as nodes in the typed graph, while the edges are used to show the relations between these artifacts. The relation-aware GNN is used for generating test case embeddings, reflecting the distance between the test cases and the changed areas as well as the areas known to have issues in the past. The actor-critic RL agent uses these test case embeddings and the budget to decide whether to run, skip, or discard test cases. The performance of our proposal outstrips coverage-based heuristics, history-based ranking, a GA-based search, an enhanced QPSO, and two learning-based ablations, as validated by our experiments on four Java projects from Defects4J.  In terms of various programs and budgets, using GNN–RL has led to an improvement in the Average Percentage of Faults Detected (APFD) and Cost-cognizant Average Percentage of Faults Detected (APFDc) by approximately 6–9 percentage points on lower budgets. In addition, it is possible to obtain a 42% suite reduction and a 48% cost reduction while still retaining 98% of all fault-revealing tests in the final suite. The results show that it is possible to obtain promising solutions for adaptive regression testing using budget-aware reinforcement learning and graph-based representation learning.

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