2024, Vol.20, No.3

Journal of Software Assessment and Valuation 2024 KCI Impact Factor : 0.32

pISSN : 2092-8114 / eISSN : 2733-4384
https://journal.kci.go.kr/ksavs

With the rapid development of artificial intelligence technology, the copyright of generative AI outputs is being debated. The main issues are the acquisition of data used for training and the attribution and infringement of the resulting works. This paper describes other copyright infringement concerns that may arise from the technical components of generative AI. In the first half of this paper, I briefly review how generative AI is implemented in text, image, and sound/voice, which are typical application areas, and in the second half, I review the copyright infringement cases that may arise from the technical elements. While previous studies have approached the copyright discussion from the perspective of training and output, this paper is significant in that it discusses copyright in the technical components of generative AI. I hope that this paper has established the copyright content of technical elements.

Most of the disputes between client and contractor in various software development sites in Korea and abroad have similar forms. In the case of the client, disputes arise over defects in the software and the appropriateness of development costs, while in the case of the customer, disputes over unclear requirements, frequent modifications, and non-settlement of development costs are common phenomena. Even after the development is completed, disputes arise over the recognition, correction, cost, and responsibility of defects, which account for a large part of software emotions. This paper’s goal is how to calculate the completeness and development cost of a mobile app based on off-the-shelf knowledge. In order to determine the ready-made contribution rate of the 'mobile app' development output delivered by Company A to Company B, and to specify the development cost to be paid by Company A to Company B based on this, we calculated the completed and incomplete parts of the mobile app development, the development cost of the already completed part, the development cost to complete the incomplete part, and the proportion of the already completed part of the total development cost.

Traditional machine learning-based Android malicious app(malware) detection techniques have limitations in detecting new types of malware due to concept drift. In other words, traditional machine learning-based malware detection techniques may not be sustainable. Concept drift refers to the evolving nature of malware features over time and the resulting degradation in the performance of machine learning-based detection models In this paper, we propose a technique to improve the sustainability of the method for detecting Android malware using API call information and machine learning. In the proposed technique, apps are first grouped using K-means clustering, and then classification models are applied to detect malicious apps for each group. In the K-means clustering, the elbow method is used to find the optimal k value, and thresholding and hyperparameter optimization processes are applied to the classifiers for each cluster. The classifiers include random forest, K-nearest neighbor, and AdaBoost. The experimental results show that the random forest classifier showed the highest performance, with the F1 score and AUT value calculated by the micro-means method being improved by 20.1%p and 20.4%p, respectively, compared to the traditional random forest model.