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An Empirical Study of MISRA-C Related Source Code Changes in Open-source Software Projects
Suhyun Park, Jaechang Nam, Shin Hong
http://doi.org/10.5626/JOK.2024.51.8.718
This paper presents empirical studies on 75 open-source projects hosted on GitHub to explore how source code changes align with MISRA C coding guidelines. Through our analysis of the studied projects, we have identified eight distinctive keywords that represent the software domains where compliance with MISRA C coding guidelines is likely to be found. Additionally, we discovered that 54.75% of the studied projects utilizes at least one static rule checker. In the 75 studied projects, we found code changes associated with 75 MISRA C coding rules. The analyses of these code changes reveal that multiple MISRA C-related code changes often occur in a short timeframe, and, on average, each MISRA C-related code change modifies 1124 lines of code at once.
An Automated Interior Design Model using Interior Design Guidelines and Proximal Policy Optimization
Chanyoung Yoon, Soobin Yim, Sangbong Yoo, Yun Jang
http://doi.org/10.5626/JOK.2024.51.6.519
The interior design of a residential space greatly influences the satisfaction and impression of its residents. However, interior design is not easily accessible due to its requirement for professional design knowledge. Therefore, optimization and deep learning methods for automated interior design have been proposed. Nevertheless, these technologies have encountered difficulties such as taking a considerable amount of time to solve problems or requiring extensive training data. In this paper, we propose an automated interior design model using deep reinforcement learning. In reinforcement learning, there is no need to obtain training data because the agent learns a policy that interacts with the environment and maximizes the cumulative reward. We designed interior design guidelines proposed in previous studies as a reward function to create interior layouts that satisfy functional and visual criteria. Reinforcement learning agents used PPO to arrange furniture in continuous positions. We evaluated the performance of the proposed model through two experiments: a reward comparison experiment based on different combinations of furniture and room shapes, and a design comparison experiment based on different combinations of reward functions.
Query Intent Detection for Medical Advice: Training Data Construction and Intent Classification
Tae-Hoon Lee, Young-Min Kim, Eunji Jeong, Seon-Ok Na
http://doi.org/10.5626/JOK.2021.48.8.878
In most task-oriented dialogue systems, intent detection and named entity recognition need to precede. This paper deals with the query intent detection to construct a dialogue system for medical advice. We start from the appropriate intent categories for the final goal. We also describe in detail the data collection, training data construction, and the guidelines for the manual annotation. BERT-based classification model has been used for query intent detection. KorBERT, a Korean version of BERT has been also tested for detection. To verify that the DNN-based models outperform the traditional machine learning methods even for a mid-sized dataset, we also tested SVM, which produces a good result in general for such dataset. The F1 scores of SVM, BERT, and KorBERT are 69%, 78%, and 84% respectively. For future work, we will try to increase intent detection performance through dataset improvement.
A Traffic-Classification Method Using the Correlation of the Network Flow
YoungHoon Goo, Kyuseok Shim, Sungho Lee, Baraka D. Sija, MyungSup Kim
Presently, the ubiquitous emergence of high-speed-network environments has led to a rapid increase of various applications, leading to constantly complicated network traffic. To manage networks efficiently, the traffic classification of specific units is essential. While various traffic-classification methods have been studied, a methods for the complete classification of network traffic has not yet been developed. In this paper, a correlation model of the network flow is defined, and a traffic-classification method for which this model is used is proposed. The proposed network-correlation model for traffic classification consists of a similarity model and a connectivity model. Suggestion for the effectiveness of the proposed method is demonstrated in terms of accuracy and completeness through experiments.
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