Biblio
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2020. Efficiency Metrics and Test Case Design for Test Automation. 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :15—23.
In this paper, we present our test automation work applied on national e-health portal for residents in Norway which has over million monthly visits. The focus of the work is threefold: delegating automation tasks and increasing reusability of test artifacts; metrics for estimating efficiency when creating test artifacts and designing robust automated test cases. Delegating (part of) test automation tasks from technical specialist (e.g. programmer - expensive resource) to non-technical specialist (e.g. domain expert, functional tester) is carried out by transforming low level test artifacts into high level test artifacts. Such transformations not only reduce dependency on specialists with coding skills but also enables involving more stakeholders with domain knowledge into test automation. Furthermore, we propose simple metrics which are useful for estimating efficiency during such transformations. Examples of the new metrics are implementation creation efficiency and test creation efficiency. We describe how we design automated test cases in order to reduce the number of false positives and minimize code duplication in the presence of test data challenge (i.e. using same test data both for manual and automated testing). We have been using our test automation solution for over three years. We successfully applied test automation on 2 out of 6 Scrum teams in Helsenorge. In total there are over 120 automated test cases with over 600 iterations (as of today).
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2019. Tracking Sensitive Information and Operations in Integrated Clinical Environment. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :192–199.
Integrated Clinical Environment (ICE) is a standardized framework for achieving device interoperability in medical cyber-physical systems. The ICE utilizes high-level supervisory apps and a low-level communication middleware to coordinate medical devices. The need to design complex ICE systems that are both safe and effective has presented numerous challenges, including interoperability, context-aware intelligence, security and privacy. In this paper, we present a data flow analysis framework for the ICE systems. The framework performs the combination of static and dynamic analysis for the sensitive data and operations in the ICE systems. Our experiments demonstrate that the data flow analysis framework can record how the medical devices transmit sensitive data and perform misuse detection by tracing the runtime context of the sensitive operations.