Quantum Neural Network Classifier for Cancer Registry System Testing: A Feasibility Study
With the rapid advancement of quantum computing, research on quantum machine learning (QML) algorithms has grown significantly. Among these, the Quantum Neural Network (QNN) stands out as one of the promising algorithms that integrates the principles of quantum computing with artificial neural networks to process data.
Inspired by applications of QNN across fields, we investigate their use in software testing for the Cancer Registry of Norway (CRN), part of the Norwegian Institute of Public Health (NIPH), responsible for cancer statistics among the Norwegian population. CRN develops a complex socio-technical software system, Cancer Registration Support System (CARESS), interacting with many entities (e.g., hospitals, medical laboratories, and other patient registries) to achieve its task. For cost-effective testing of CARESS, CRN has employed EvoMaster, an AI-based REST API testing tool combined with an integrated classical machine learning model EvoClass, to predict whether an API request generated by EvoMaster is likely to be invalid, so saving testing time.
Within this context, we propose EvoQlass to investigate the feasibility of using, inside EvoMaster, a QNN classifier instead of the existing classical machine learning model. Results indicate that EvoQlass can achieve performance comparable to that of EvoClass, but by using significantly fewer training features in certain configurations. We further explore the effects of various QNN configurations on performance and offer recommendations for optimal QNN settings for future QNN developers.
Thu 16 AprDisplayed time zone: Brasilia, Distrito Federal, Brazil change
11:00 - 12:30 | Testing and Analysis 9Research Track / Journal-first Papers / Demonstrations / New Ideas and Emerging Results (NIER) at Oceania II Chair(s): Shiyi Wei University of Texas at Dallas | ||
11:00 15mTalk | GUISpector: An MLLM Agent Framework for Automated Verification of Natural Language Requirements in GUI Prototypes Demonstrations Kristian Kolthoff Institute for Software and Systems Engineering, Clausthal University of Technology, Felix Kretzer human-centered systems Lab (h-lab), Karlsruhe Institute of Technology (KIT) , Simone Paolo Ponzetto Data and Web Science Group, University of Mannheim, Alexander Maedche human-centered systems Lab (h-lab), Karlsruhe Institute of Technology (KIT) , Christian Bartelt Institute for Software and Systems Engineering, TU Clausthal Pre-print Media Attached | ||
11:15 15mTalk | Valg: A Fast Reinforcement Learning-Based Runtime Verification Tool for Java Demonstrations Shinhae Kim Cornell University, Saikat Dutta Cornell University, Owolabi Legunsen Cornell University | ||
11:30 15mTalk | Quantum Neural Network Classifier for Cancer Registry System Testing: A Feasibility Study Journal-first Papers Xinyi Wang Simula Research Laboratory; University of Oslo, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University, Paolo Arcaini National Institute of Informatics, Narasimha Raghavan Veeraragavan Cancer Registry of Norway and Norwegian Institute of Public Health, Jan F. Nygård Cancer Registry of Norway Link to publication DOI | ||
11:45 15mTalk | Testora: Using Natural Language Intent to Detect Behavioral Regressions Research Track Michael Pradel CISPA Helmholtz Center for Information Security | ||
12:00 15mTalk | Automatic Validation of LLM-Generated Code with Prompt Paraphrasing New Ideas and Emerging Results (NIER) | ||
12:15 15mTalk | Causally Perturbed Fairness Testing Journal-first Papers | ||