Reformulating Regression Test Suite Optimization using Quantum Annealing - an Empirical Study

2024-11-24

International Journal on Software Tools for Technology Transfer (STTT)

AI Summary

Regression test suite optimization suffers from low selection efficiency, high computational overhead of classical optimization methods, and suboptimal performance of existing quantum approaches. Method: This paper pioneers modeling regression test suite optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem, solved via D-Wave quantum annealing hardware to identify the minimum-cost covering subset that simultaneously satisfies statement and branch coverage constraints while minimizing execution cost. We propose a scalable QUBO encoding strategy enabling end-to-end mapping from test coverage analysis to quantum hardware execution. Results: Experiments across multiple Java projects demonstrate an average 23.6% reduction in test case count while strictly preserving 100% statement and branch coverage. The quantum-annealed solutions match the quality of optimal classical algorithms—including GREEDY and Adaptive Random Testing—validating the feasibility and practicality of quantum annealing for software engineering optimization tasks.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability
• Machine Learning: Quantum Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Problem

• Software Regression Testing
• Optimization of Test Case Selection
• Quantum Computing Methods

Innovation

• Quantum Annealing
• Regression Testing Optimization
• SelectQA