Automatic Generation of Unit Tests and Test Oracles for Quantum Programs

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT), grant 2023.02439.BD. Runs 2023-09-01 to 2027-08-31. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2025-07-18); national funding data, not CORDIS. Abstract given by the source: Quantum software — like classic software — needs to be designed, specified, developed, and, most importantly, tested by developers. Writing tests is a complex, error-prone, and time-consuming task. Due to the particular properties of quantum physics (e.g., superposition), quantum software is inherently more complex to develop and effectively test than classical software. Our aim is, therefore, to help developers design better-quality quantum software. Thus, in this proposal, we first aim to develop a novel approach to automatically generate unit tests for quantum programs that will explore and exercise the idiosyncrasies of quantum programs. Secondly, we aim to develop a novel approach to augment the generated tests with effective test oracles. Thirdly, we propose to develop a benchmark of real quantum programs and real quantum bugs to systematically, automatically, reproducibly, and deterministically evaluate any approach developed in this proposal.

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Fundação para a Ciência e a Tecnologia, I.P. (FCT). Retrieved 2026-10-28. Licence: Creative Commons Attribution 4.0 (OpenAIRE Graph). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: Creative Commons Attribution 4.0 (CC BY 4.0).

AI-assisted summary: The project "Automatic Generation of Unit Tests and Test Oracles for Quantum Programs" is funded by FCT from 2023-09-01 to 2027-08-31. It aims to improve quantum software testing through automated unit test generation and effective test oracles. Suggested technology pillar: computing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.