Evaluating Mutation-based Fault Localization for Quantum Programs

TL;DR

This paper evaluates mutation-based fault localization (MBFL) for quantum programs, demonstrating its effectiveness and limitations in identifying faults in real-world and artificial quantum code, with implications for debugging quantum software.

Contribution

The study applies and assesses quantum-specific mutation-based fault localization techniques on real and artificial faults in quantum programs, highlighting its potential and challenges.

Findings

Real-world faults are more challenging for MBFL than artificial faults.

Median EXAM score is 1.2% for artificial faults and 19.4% for real-world faults.

MBFL shows potential but has limitations depending on fault type and mutation operations.

Abstract

Quantum computers leverage the principles of quantum mechanics to execute operations. They require quantum programs that define operations on quantum bits (qubits), the fundamental units of computation. Unlike traditional software development, the process of creating and debugging quantum programs requires specialized knowledge of quantum computation, making the development process more challenging. In this paper, we apply and evaluate mutation-based fault localization (MBFL) for quantum programs with the aim of enhancing debugging efficiency. We use quantum mutation operations, which are specifically designed for quantum programs, to identify faults. Our evaluation involves 23 real-world faults and 305 artificially induced faults in quantum programs developed with Qiskit(R). The results show that real-world faults are more challenging for MBFL than artificial faults. In fact, the median EXAM score, which represents the percentage of the code examined before locating the faulty statement (lower is better), is 1.2% for artificial benchmark and 19.4% for the real-world benchmark in the worst-case scenario. Our study highlights the potential and limitations of MBFL for quantum programs, considering different fault types and mutation operation types. Finally, we discuss future directions for improving MBFL in the context of quantum programming.