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Development and Application of Guidelines for Compliance with IEC 62304 International Standards for AI Medical Device Software
DongYeop Kim, Ye-Seul Park, Byungjeong Lee, Jung-Won Lee
http://doi.org/10.5626/JOK.2021.48.1.71
Medical device software developers must implement the processes required by IEC 62304, the international standard for medical device software life-cycle processes, and they must also have a large amount of artifacts to obtain a license. Recently, AI medical device software based on medical images has been actively developed, and since it is treated as standalone software, it must be approved in accordance with IEC 62304 for medical device software. The international standard for AI technology is currently in the discussion stage, and the developer should arbitrarily establish the life-cycle process of AI medical device software, and by matching the specifications of IEC 62304, the performance and safety of AI products will be evaluated. It is unclear which quality management technique should be used to produce the best artifact. This paper provides a quality control technique for fulfilling the scope and requirements of IEC 62304 compliance for AI medical device software in the form of guidelines. These guidelines are also applied to actual AI products to check their potential use in real applications.
Improving Reliability of Smart Contracts and DApps by Applying Property-based and Model-based Test Methods to Different Test Levels
Kyeongsic Min, Jung-Won Lee, Byungjeong Lee
http://doi.org/10.5626/JOK.2019.46.8.763
Smart contract technology based on the blockchain enables transparent transactions and automated contract execution without third-party intervention. Ethereum provides Solidity and EVM (Ethereum Virtual Machine) that can be used to implement smart contracts. In addition, it can be used to create a DApp (Decentralized Application) without developing a new blockchain using smart contract. However, the source codes cannot be updated in smart contracts. Therefore, a lot of work is needed to fix even minor errors following deployment. Therefore, the source code should be thoroughly tested or analyzed prior to the deployment of the contract to ensure that it is free of defects. In this paper, we propose a method to identify the errors and verify the accuracy of smart contracts and DApps using dynamic testing methods. Toward this end, we defined the dynamic model needed in each test level and applied the current testing methodology, using property-based and model-based testing.
A Test Scenario Generation Method from Activity Diagram with Concurrency
Seungchan Back, Hyorin Choi, Byungjeong Lee, Jung-Won Lee
Currently, software testing is becoming increasingly important in the industrial field and a large body of research supports the improvement of efficient software testing. Model-based testing is generally used to formalize user requirement data for test design. Complex system that includes loop and concurrency has a high probability of path explosion problem. Specially, as threads are added to concurrency, test scenarios have also increased exponentially. However, it is difficult to solve this problem using existing techniques. In this paper, we propose novel path-search technique that focuses on behavioral features of concurrency path in order to avoid path explosion problem. A system that contains concurrent paths is represented by activity diagram in case study section. Efficiency of our study is shown through comparison with several generated test scenarios of other studies. The result indicate that our approach is efficient for finding faults in loop and concurrency with fewer test scenario.
Estimating the Time to Fix Bugs Using Bug Reports
Kimun Kwon, Kwanghue Jin, Byungjeong Lee
As fixing bugs is a large part of software development and maintenance, estimating the time to fix bugs -bug fixing time- is extremely useful when planning software projects. Therefore, in this study, we propose a way to estimate bug fixing time using bug reports. First, we classify previous bug reports with meta fields by applying a k-NN method. Next, we compute the similarity of the new bug and previous bugs by using data from bug reports. Finally, we estimate how long it will take to fix the new bug using the time it took to repair similar bugs. In this study, we perform experiments with open source software. The results of these experiments show that our approach effectively estimates the bug fixing time.
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