eISSN : 3022-7011
ISSUER : KIPS
 
After the Korea Information Processing Society (KIPS) Transactions journal was founded in 1994, it was reorganized into the KIPS Transactions: Computer and Communication Systems(2287-5891/2734-049X ) and the KIPS Transactions: Software and Data Engi neering(2287-5905/2734-0503) in 2012. Through the KIPS official meeting on January 8th, 2024, the new KIPS Transaction journal was founded by integrating two KIPS Journals, KIPS Transactions: Computer and Communication Systems and KIPS Transactions: Software and Data Engineering. The new journal aims to realize social value and contribute to the development of South Korea’s science and technology with support from the lottery fund of the Ministry of Strategy and Finance and the science/technology promotion fund of the Ministry of Science and ICT. It is indexed in the Korea Science Academic Database, Korea Citation Index (KCI), and EBSCO.

HighlightsMore

A Study on Improving Performance of Software Requirements Classification Models by Handling Imbalanced Data

Jong-Woo Choi  Young-Jun Lee  Chae-Gyun Lim  Ho-Jin Choi

Software requirements written in natural language may have different meanings from the stakeholders’ viewpoint. When designing an architecture based on quality attributes, it is necessary to accurately classify quality attribute requirements because...

Extending StarGAN-VC to Unseen Speakers Using RawNet3 Speaker Representation

Bogyung Park  Somin Park  Hyunki Hong

Voice conversion, a technology that allows an individual’s speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes a...

A Study on Classification Models for Predicting Bankruptcy Based on XAI

Jihong Kim  Nammee Moon

Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions and reducing loan default rates. In many studies, classification models using artificial intelligence technology hav...

Detecting Common Weakness Enumeration(CWE) Based on the Transfer Learning of CodeBERT Model

Chansol Park  So Young Moon  R. Young Chul Kim

Recently the incorporation of artificial intelligence approaches in the field of software engineering has been one of the big topics. In the world, there are actively studying in two directions: 1) software engineering for artificial intelligence an...

Latest Publication   (Vol. 13, No. 5, May.  2024)

Sentiment Analysis of News Based on Generative AI and Real Estate Price Prediction: Application of LSTM and VAR Models
Sua Kim  Mi Ju Kwon  Hyon Hee Kim
Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a varietyof unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transactionprices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a NewsSentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index,the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutralsentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used forreal estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM predictionmodel, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724,and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254,and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413,and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.
The Transactions of the Korea Information Processing Society, Vol. 13, No. 5, pp. 209-216, May. 2024
https://doi.org/10.3745/TKIPS.2024.13.5.209
Generated AI Prediction of Real Estate Price News Sentiment Index Multi-head Attention LSTM Vetor Auto Regression
Evaluating the Efficiency of Models for Predicting Seismic Building Damage
Chae Song Hwa  Yujin Lim
Predicting earthquake occurrences accurately is challenging, and preparing all buildings with seismic design for such random events is a difficult task. Analyzing building features to predict potential damage and reinforcing vulnerabilities based on this analysis can minimize damages even in buildings without seismic design. Therefore, research analyzing the efficiency of building damage prediction models is essential. In this paper, we compare the accuracy of earthquake damage prediction models using machine learning classification algorithms, including Random Forest, Extreme Gradient Boosting, LightGBM, and CatBoost, utilizing data from buildings damaged during the 2015 Nepal earthquake.
The Transactions of the Korea Information Processing Society, Vol. 13, No. 5, pp. 217-220, May. 2024
https://doi.org/10.3745/TKIPS.2024.13.5.217
Earthquake Earthquake Damage Prediction Machine Learning(ml)
Search Re-ranking Through Weighted Deep Learning Model
Gi-Taek An  Woo-Seok Choi  Jun-Yong Park  Jung-Min Park  Kyung-Soon Lee
In information retrieval, queries come in various types, ranging from abstract queries to those containing specific keywords, making it a challenging task to accurately produce results according to user demands. Additionally, search systems must handle queries encompassing various elements such as typos, multilingualism, and codes. Reranking is performed through training suitable documents for queries using DeBERTa, a deep learning model that has shown high performance in recent research. To evaluate the effectiveness of the proposed method, experiments were conducted using the test collection of the Product Search Track at the TREC 2023 international information retrieval evaluation competition. In the comparison of NDCG performance measurements regarding the experimental results, the proposed method showed a 10.48% improvement over BM25, a basic information retrieval model, in terms of search through query error handling, provisional relevance feedback-based product title-based query expansion, and reranking according to query types, achieving a score of 0.7810.
The Transactions of the Korea Information Processing Society, Vol. 13, No. 5, pp. 221-226, May. 2024
https://doi.org/10.3745/TKIPS.2024.13.5.221
Information Retrieval Deep Learning Model DeBERTa Product Search
Development of Reliability Measurement Method and Tool for Nuclear Power Plant Safety Software
Lingjun Liu  Wooyoung Choi  Eunkyoung Jee  Duksan Ryu
Since nuclear power plants (NPPs) increasingly employ digital I&C systems, reliability evaluation for NPP software has become crucial for NPP probabilistic risk assessment. Several methods for estimating software reliability have been proposed, but there is no available tool support for those methods. To support NPP software manufacturers, we propose a reliability measurement tool for NPP software. We designed our tool to provide reliability estimation depending on available qualitative and quantitative information that users can offer. We applied the proposed tool to an industrial reactor protection system to evaluate the functionality of this tool. This tool can considerably facilitate the reliability assessment of NPP software.
The Transactions of the Korea Information Processing Society, Vol. 13, No. 5, pp. 227-235, May. 2024
https://doi.org/10.3745/TKIPS.2024.13.5.227
Software Reliability Measurement Nuclear Power Plant Software Bayesian Belief Network Statistical Testing Reliability Measurement Tool
Software Defect Prediction Based on SAINT
Sriman Mohapatra  Eunjeong Ju  Jeonghwa Lee  Duksan Ryu
Software Defect Prediction (SDP) enhances the efficiency of software development by proactively identifying modules likely to contain errors. A major challenge in SDP is improving prediction performance. Recent research has applied deep learning techniques to the field of SDP, with the SAINT model particularly gaining attention for its outstanding performance in analyzing structured data. This study compares the SAINT model with other leading models (XGBoost, Random Forest, CatBoost) and investigates the latest deep learning techniques applicable to SDP. SAINT consistently demonstrated superior performance, proving effective in improving defect prediction accuracy. These findings highlight the potential of the SAINT model to advance defect prediction methodologies in practical software development scenarios, and were achieved through a rigorous methodology including cross-validation, feature scaling, and comparative analysis.
The Transactions of the Korea Information Processing Society, Vol. 13, No. 5, pp. 236-242, May. 2024
https://doi.org/10.3745/TKIPS.2024.13.5.236
Transformer SAINT Software Defect Prediction