@inproceedings {43377,
	title = {Automated Test Generation for Medical Rules Web Services: A Case Study at the Cancer Registry of Norway},
	journal = {31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023)},
	year = {2023},
	publisher = {ACM},
	doi = {10.1145/3611643.3613882},
	author = {Laaber, Christoph and Yue, Tao and Ali, Shaukat and Schwitalla, Thomas and Jan F. {Nyg{\r a}rd}}
}
@inproceedings {43324,
	title = {Challenges of Testing an Evolving Cancer Registration Support System in Practice},
	journal = {45th IEEE/ACM International Conference on Software Engineering: Companion Proceedings (ICSE-Companion 2023)},
	year = {2023},
	publisher = {IEEE},
	doi = {10.1109/ICSE-Companion58688.2023.00102},
	author = {Laaber, Christoph and Yue, Tao and Ali, Shaukat and Schwitalla, Thomas and Jan F. {Nyg{\r a}rd}}
}
@inproceedings {25464,
	title = {RCIA: Automated Change Impact Analysis to Facilitate a Practical Cancer Registry System},
	journal = {The International Conference on Software Maintenance and Evolution (ICSME)},
	year = {2017},
	pages = {603-612},
	publisher = {IEEE},
	abstract = {The Cancer Registry of Norway (CRN) employs a cancer registry system to collect cancer patient data (e.g., diagnosis and treatments) from various medical entities (e.g., clinic hospitals). The collected data are then checked for validity (i.e., validation) and assembled as cancer cases (i.e., aggregation) based on more than 1000 cancer coding rules in the system. However, it is frequent in practice that the collected cancer data changes due to various reasons (e.g., different treatments) and the cancer coding rules can also change/evolve due to new medical knowledge. Thus, such a cancer registry system requires an efficient means to automatically analyze these changes and provide consequent impacts to medical experts for further actions. This paper proposes an automated Rule-based Change Impact Analysis (CIA) approach named RCIA that includes: 1) a change classification to capture the potential changes that can occur at CRN; 2) in total 80 change impact analysis rules including 50 dependency rules and 30 impact rules; and 3) an efficient algorithm to analyze changes and produce consequent impacts. We evaluate RCIA via a case study with 12 real change sets from CRN and a conducted interview. The results showed that RCIA managed to produce 100\% actual change impacts and the medical expert at CRN is quite positive to apply RCIA to facilitate their cancer registry system. We also shared a set of lessons learned based on the collaboration with CRN.\&nbsp;},
	author = {Wang, Shuai and Schwitalla, Thomas and Yue, Tao and Ali, Shaukat and Nyg{\r a}rd, Jan},
	editor = {IEEE, }
}
@mastersthesis {25423,
	title = {A Rule-Based Framework for Supporting Automated Change Impact Analysis in the Cancer Registry of Norway},
	journal = {The Department of Informatics, University of Oslo},
	year = {2017},
	author = {Schwitalla, Thomas}
}
