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Safety Requirement Elicitation for Small Aircraft Collision Avoidance Software using STPA, FTA and FMEA
Jongwon Lee, Uicheon Lee, Taehwan Kim, Seonah Lee
http://doi.org/10.5626/JOK.2024.51.8.706
With the growing trend of urban air traffic, aircraft are becoming smaller and more reliant on software. As a result, safety analysis techniques and standards, which have traditionally focused on ARP4761, the aircraft safety evaluation process, must evolve to incorporate a software-centered approach. In this paper, we propose how to link STPA method to FTA and FMEA for safety analysis in air mobility, which is a software-intensive system. To assess the feasibility and effectiveness of this approach, we conducted a safety analysis case study focusing on the collision avoidance software of a small aircraft. The results of the study confirmed the effectiveness of linking STPA, FTA, and FMEA methods and enabled the derivation of safety requirements.
A Multi-label Classification Bot for Issue Management System in GitHub
Doje Park, Yyejin Yang, Gwang Choi, Seonah Lee, Sungwon Kang
http://doi.org/10.5626/JOK.2021.48.8.928
The GitHub platform, on which many developers develop open-source software projects, provides an issue management system. Using the system, the stakeholders can report software problems or functional requests as issues. The issue management system provides issue report forms and allows developers to create and use labels to classify issues. However, since the labeling work is manually done, it requires a lot of effort from the developers and inaccurate labeling can easily occur. In addition, it takes a lot of time for a project manager to read and give feedback on each issue. To mitigate this problem, previous studies have proposed attaching a single label to an issue automatically. However, in practice, there are a number of issue reports that need multiple labels to be attached. Therefore, in this study, we proposed a multi-labeling bot that automatically attaches multiple labels to an issue report in order to reduce the effort required by a project manager to read issue reports and give feedback in GitHub. The multi-label classification of our bot showed F-score ranging from 0.54 to 0.78.
Reducing the Learning Time of Code Change Recommendation System Using Recurrent Neural Network
Byeong-il Bae, Sungwon Kang, Seonah Lee
http://doi.org/10.5626/JOK.2020.47.10.948
Since code change recommendation systems select and recommend files that needing modifications, they help developers save time spent on software system evolution. However, these recommendation systems generally spend a significant amount of time in learning accumulated data and relearning whenever new data are accumulated. This study proposes a method to reduce the time spent on learning when using Code change Recommendation System using Recurrent Neural Network (RNN-CRS), which works by avoiding the learning that is unlikely to contribute to new knowledge. For the five products used in the experimental evaluation, our proposed method reduced the time to relearn data and re-generate a learning model by as much as 49.08%-68.15%, and by 10.66% in the least effective case, compared to the existing method.
A Technique for Updating Method Calls by Utilizing Software Change Rules
http://doi.org/10.5626/JOK.2019.46.2.161
Previous studies have proposed a method of extracting change rules by analyzing differences in releases of the client software framework with an aim of reducing developer’s efforts in updating the client software. However, these studies only discuss the generation of change rules and do not directly discuss updating the client system. To overcome this limitation, we propose a method that automatically updates the method calls in the client by using the change rules produced by the existing rule generation tools. We also implement the proposed method as a tool and evaluate how well the method automatically updates method calls using the extracted change rules. Results showed that 279 out of 547 method calls were automatically updated and only 2 compilation errors were found. This study contributes to research into reducing the efforts of client developers to update method calls after any method changes to the framework.
A Class Diagramming Tool for Visualizing the Latest Revision of Software Change History
Jaekyeong Sim, HeeTae Cho, Jongyeol Park, Seonah Lee
http://doi.org/10.5626/JOK.2018.45.2.150
Software visualization can assist developers to understand a software system and change its code. The recent development of bottom-up visualization tools demonstrates the advantages by revealing the code that is directly related to a software evolution task. However, the information provided by these tools is limited to the code already investigated by the developers in that task session. To broaden the scope and provide the code information that developers should explore, we propose to present the latest revision of a software system via a class diagram. When a developer clicks on a button, the proposed tool reveals the code changes committed to a configuration management system, and facilitates the understanding of code changes. We also conduct case studies illustrating the advantages of the proposed tool.
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