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One of the most important features of an ERP system is that it integrates all the units of a company, such as sales, production, purchasing, inventory, accounting, and human resources, in real time and with a close relationship to each other. This integration enables total optimization and makes the business process more customer-oriented. Complaints about defects in the software, estimation of appropriate development costs, etc. are raised by the client, while complaints about unstable requirements, excessive changes, delayed delivery and non-payment of development costs are raised by the customer. This paper is a study on how to estimate the completion level and appropriate development cost and defect repair cost in a project where ERP system development is completed. Based on the completeness of the ERP system delivered by Company A to Company B and the IFPUG's FP estiamtion manual and the detailed method of the software cost estimation guide, the function point was calculated, and based on this, the appropriate development cost, development period, and defect repair cost were calculated.

SW IP grants exclusive rights to control how SW is used, copied, and distributed, and helps establish the financial value of SW. In addition, from the perspective of SW developers and investors, accurately assessing the value of SW IP is essential because it not only helps to suggest an appropriate price for SW licensing or sales, but also provides information on future commercial viability based on investment, collateral, etc. However, existing IP valuation models were mainly designed based on the premise of patent valuation. Although patents and copyrights in SW have different legal protection scopes and economic properties, it is difficult to find cases where the characteristics of SW copyrights are considered in SW IP valuation. Therefore, this study aims to build a valuation model targeting SW copyrights. As a first step, It analyzes the unique legal and economic characteristics of the copyright system and suggests detailed factors of the right evaluation by categorizing them into rights scope, rights stability, and copyright transaction availability.

Recentlry, the application of deep learning to assess source code similarity has been an area of interest. Although this approach is very promising in that it can improve the accuracy and efficiency of similarity detection tasks by leveraging the ability of deep learning models to automatically learn and extract features from various representations of source code, similarity assessment using deep learning has issues regarding the accuracy, fairness, and interpretability of the results. This paper considers the issues related to deep learning-based software similarity assessment techniques and discusses the improvements required for deep learning models to be used in software assessment from a technical perspective. This study aims to provide a practical method for deep learning-based software similarity assessment to be industrially efficient while minimizing legal disputes. Suggested improvements include introducing a hybrid approach, automating dataset augmentation and labeling, lightweighting and efficient learning of models, and introducing explainable AI. In addition, we present scenarios for utilizing various evaluation indicators to improve reliability.