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Prof. Duksan Ryu
Jeonbuk National University

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Research Keywords & Expertise

0 Software Engineering
0 Autonomous systems including self-driving cars
0 Software analytics
0 software defect prediction
0 software reliability engineering

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software defect prediction
Software Engineering

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Journal article
Published: 24 February 2021 in Applied Sciences
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Software is playing the most important role in recent vehicle innovations, and consequently the amount of software has rapidly grown in recent decades. The safety-critical nature of ships, one sort of vehicle, makes software quality assurance (SQA) a fundamental prerequisite. Just-in-time software defect prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in the maritime domain reported feasible prediction performance. However, we still consider that the prediction model has room for improvement since the parameters of the model are not optimized yet. Harmony search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond the baseline of a previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite the fact that we considered balance as a performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with a high class imbalance ratio. Finally, we expect that our research can be extended to improve the performance of JIT-SDP not only in maritime domain software but also in open source software.

ACS Style

Jonggu Kang; Sunjae Kwon; Duksan Ryu; Jongmoon Baik. HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software. Applied Sciences 2021, 11, 2002 .

AMA Style

Jonggu Kang, Sunjae Kwon, Duksan Ryu, Jongmoon Baik. HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software. Applied Sciences. 2021; 11 (5):2002.

Chicago/Turabian Style

Jonggu Kang; Sunjae Kwon; Duksan Ryu; Jongmoon Baik. 2021. "HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software." Applied Sciences 11, no. 5: 2002.

Special issue paper
Published: 04 November 2020 in Software: Practice and Experience
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Background The importance of software in maritime transportation is rapidly increasing as the industry seeks to develop and utilize innovative future ships, which can be realized using software technology. Due to the safety‐critical nature of ships, software quality assurance (SQA) has become an essential prerequisite for such development. Objective Based on the unique characteristics of the maritime domain, the purpose of this study was to achieve effective SQA resource allocation to reduce post‐release quality costs. Method Software defect prediction (SDP) is employed to predict defects in newly developed software based on models trained with past software defects and to update information using machine learning. This study demonstrated that just‐in‐time SDP is applicable to maritime domain practice and can reduce post‐release quality costs via combination with an estimation model, qCOPLIMO. Results Using real‐world datasets collected from the maritime industry, performance and cost‐benefit analyses of SDP were performed. A successful model was obtained that meets the performance criterion of 0.75 in within‐project defect prediction (WPDP) but not cross‐project defect prediction (CPDP). In addition, the cost‐benefit analysis results showed that 20% effort enables the detection of 56% of defects on average and that the post‐release quality cost can be reduced by 37.3% in the maritime domain. Conclusion SDP can be successfully applied to the maritime domain. Further, it is desirable to utilize WPDP instead of CPDP once minimum high‐quality commits are available that can be identified as defective or not. Finally, SDP can help reduce review effort and post‐release quality costs.

ACS Style

Jonggu Kang; Duksan Ryu; Jongmoon Baik. Predicting just‐in‐time software defects to reduce post‐release quality costs in the maritime industry. Software: Practice and Experience 2020, 51, 748 -771.

AMA Style

Jonggu Kang, Duksan Ryu, Jongmoon Baik. Predicting just‐in‐time software defects to reduce post‐release quality costs in the maritime industry. Software: Practice and Experience. 2020; 51 (4):748-771.

Chicago/Turabian Style

Jonggu Kang; Duksan Ryu; Jongmoon Baik. 2020. "Predicting just‐in‐time software defects to reduce post‐release quality costs in the maritime industry." Software: Practice and Experience 51, no. 4: 748-771.

Journal article
Published: 02 April 2018 in IEEE Transactions on Services Computing
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Many web-based software systems have been developed in the form of composite services. It is important to accurately predict the Quality of Service (QoS) value of atomic web services because the performance of such composite services depends greatly on the performance of the atomic web service adopted. In recent years, collaborative filtering based methods for predicting the web service QoS values have been proposed. However, they are mainly faced with a cold start problem that is difficult to make reliable prediction due to highly sparse historical data, newly introduced users and web services, and the existing work only deals with the case of newly introduced users. In this article, we propose a Location-based Matrix Factorization using a Preference Propagation method (LMF-PP) to address the cold start problem. LMF-PP fuses invocation and neighborhood similarity, and then the fused similarity is utilized by preference propagation. LMF-PP is compared with existing approaches on the real world dataset. Based on the experimental results, LMF-PP shows better performance than existing approaches in cold start environments as well as in warm start environments.

ACS Style

Duksan Ryu; Kwangkyu Lee; Jongmoon Baik. Location-Based Web Service QoS Prediction via Preference Propagation to Address Cold Start Problem. IEEE Transactions on Services Computing 2018, 14, 736 -746.

AMA Style

Duksan Ryu, Kwangkyu Lee, Jongmoon Baik. Location-Based Web Service QoS Prediction via Preference Propagation to Address Cold Start Problem. IEEE Transactions on Services Computing. 2018; 14 (3):736-746.

Chicago/Turabian Style

Duksan Ryu; Kwangkyu Lee; Jongmoon Baik. 2018. "Location-Based Web Service QoS Prediction via Preference Propagation to Address Cold Start Problem." IEEE Transactions on Services Computing 14, no. 3: 736-746.

Journal article
Published: 01 December 2016 in Applied Soft Computing
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Software defect prediction predicts fault-prone modules which will be tested thoroughly. Thereby, limited quality control resources can be allocated effectively on them. Without sufficient local data, defects can be predicted via cross-project defect prediction (CPDP) utilizing data from other projects to build a classifier. Software defect datasets have the class imbalance problem, indicating the defect class has much fewer instances than the non-defect class does. Unless defect instances are predicted correctly, software quality could be degraded. In this context, a classifier requires to provide high accuracy of the defect class without severely worsening the accuracy of the non-defect class. This class imbalance principle seamlessly connects to the purpose of the multi-objective (MO) optimization in that MO predictive models aim at balancing many of the competing objectives. In this paper, we target to identify effective multi-objective learning techniques under cross-project (CP) environments. Three objectives are devised considering the class imbalance context. The first objective is to maximize the probability of detection (PD). The second objective is to minimize the probability of false alarm (PF). The third objective is to maximize the overall performance (e.g., Balance). We propose novel MO naive Bayes learning techniques modeled by a Harmony Search meta-heuristic algorithm. Our approaches are compared with single-objective models, other existing MO models and within-project defect prediction models. The experimental results show that the proposed approaches are promising. As a result, they can be effectively applied to satisfy various prediction needs under CP settings.

ACS Style

Duksan Ryu; Jongmoon Baik. Effective multi-objective naïve Bayes learning for cross-project defect prediction. Applied Soft Computing 2016, 49, 1062 -1077.

AMA Style

Duksan Ryu, Jongmoon Baik. Effective multi-objective naïve Bayes learning for cross-project defect prediction. Applied Soft Computing. 2016; 49 ():1062-1077.

Chicago/Turabian Style

Duksan Ryu; Jongmoon Baik. 2016. "Effective multi-objective naïve Bayes learning for cross-project defect prediction." Applied Soft Computing 49, no. : 1062-1077.

Conference paper
Published: 01 January 2016 in 2016 International Conference on Big Data and Smart Computing (BigComp)
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As the need of software has been increasing, the danger of malicious attacks against software has been worse. In order to fortify software systems against adversaries, researchers have devoted significant efforts on mitigating software vulnerabilities. To eliminate security vulnerabilities from software with lower inspection effort, vulnerability prediction approaches have been emerged. By allocating human and time resource on the potentially vulnerable subset, development organization could eliminate vulnerabilities in a cost effective manner. In the vulnerability prediction approaches, a vulnerability prediction model is constructed based on various software attributes. However, vulnerability prediction models based on the traditional software attributes have provided poor prediction accuracy or low cost effectiveness since the traditional software attributes are unable to reflect vulnerability characteristics sufficiently. In this paper, we propose a novel vulnerability prediction approach based on the CERT-C Secure Coding Standard. To evaluate the efficacy of the proposed approach, the prediction results of the suggested prediction models and other traditional models were assessed in terms of prediction accuracy and cost effectiveness. The results show that the proposed method can improve the vulnerability prediction accuracy.

ACS Style

Joonseok Yang; Duksan Ryu; Jongmoon Baik. Improving vulnerability prediction accuracy with Secure Coding Standard violation measures. 2016 International Conference on Big Data and Smart Computing (BigComp) 2016, 115 -122.

AMA Style

Joonseok Yang, Duksan Ryu, Jongmoon Baik. Improving vulnerability prediction accuracy with Secure Coding Standard violation measures. 2016 International Conference on Big Data and Smart Computing (BigComp). 2016; ():115-122.

Chicago/Turabian Style

Joonseok Yang; Duksan Ryu; Jongmoon Baik. 2016. "Improving vulnerability prediction accuracy with Secure Coding Standard violation measures." 2016 International Conference on Big Data and Smart Computing (BigComp) , no. : 115-122.

Regular paper
Published: 14 September 2015 in Journal of Computer Science and Technology
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Software defect prediction (SDP) is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires sufficient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction (CPDP) using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using Nearest-Neighbor (HISNN) method that performs a hybrid classification selectively learning local knowledge (via k-nearest neighbor) and global knowledge (via naïve Bayes). Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD (probability of detection) or high PF (probability of false alarm) which is impractical to use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.

ACS Style

Duksan Ryu; Jong-In Jang; Jongmoon Baik. A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction. Journal of Computer Science and Technology 2015, 30, 969 -980.

AMA Style

Duksan Ryu, Jong-In Jang, Jongmoon Baik. A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction. Journal of Computer Science and Technology. 2015; 30 (5):969-980.

Chicago/Turabian Style

Duksan Ryu; Jong-In Jang; Jongmoon Baik. 2015. "A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction." Journal of Computer Science and Technology 30, no. 5: 969-980.

Journal article
Published: 01 September 2015 in Software Quality Journal
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Software defect prediction has been regarded as one of the crucial tasks to improve software quality by effectively allocating valuable resources to fault-prone modules. It is necessary to have a sufficient set of historical data for building a predictor. Without a set of sufficient historical data within a company, cross-project defect prediction (CPDP) can be employed where data from other companies are used to build predictors. In such cases, a transfer learning technique, which extracts common knowledge from source projects and transfers it to a target project, can be used to enhance the prediction performance. There exists the class imbalance problem, which causes difficulties for the learner to predict defects. The main impacts of imbalanced data under cross-project settings have not been investigated in depth. We propose a transfer cost-sensitive boosting method that considers both knowledge transfer and class imbalance for CPDP when given a small amount of labeled target data. The proposed approach performs boosting that assigns weights to the training instances with consideration of both distributional characteristics and the class imbalance. Through comparative experiments with the transfer learning and the class imbalance learning techniques, we show that the proposed model provides significantly higher defect detection accuracy while retaining better overall performance. As a result, a combination of transfer learning and class imbalance learning is highly effective for improving the prediction performance under cross-project settings. The proposed approach will help to design an effective prediction model for CPDP. The improved defect prediction performance could help to direct software quality assurance activities and reduce costs. Consequently, the quality of software can be managed effectively.

ACS Style

Duksan Ryu; Jong-In Jang; Jongmoon Baik. A transfer cost-sensitive boosting approach for cross-project defect prediction. Software Quality Journal 2015, 25, 235 -272.

AMA Style

Duksan Ryu, Jong-In Jang, Jongmoon Baik. A transfer cost-sensitive boosting approach for cross-project defect prediction. Software Quality Journal. 2015; 25 (1):235-272.

Chicago/Turabian Style

Duksan Ryu; Jong-In Jang; Jongmoon Baik. 2015. "A transfer cost-sensitive boosting approach for cross-project defect prediction." Software Quality Journal 25, no. 1: 235-272.

Journal article
Published: 17 December 2014 in Empirical Software Engineering
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It is well-known that software defect prediction is one of the most important tasks for software quality improvement. The use of defect predictors allows test engineers to focus on defective modules. Thereby testing resources can be allocated effectively and the quality assurance costs can be reduced. For within-project defect prediction (WPDP), there should be sufficient data within a company to train any prediction model. Without such local data, cross-project defect prediction (CPDP) is feasible since it uses data collected from similar projects in other companies. Software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects. In addition, the impact of imbalanced data on the real performance of models can be hidden by the performance measures chosen. We investigate if the class imbalance learning can be beneficial for CPDP. In our approach, the asymmetric misclassification cost and the similarity weights obtained from distributional characteristics are closely associated to guide the appropriate resampling mechanism. We performed the effect size A-statistics test to evaluate the magnitude of the improvement. For the statistical significant test, we used Wilcoxon rank-sum test. The experimental results show that our approach can provide higher prediction performance than both the existing CPDP technique and the existing class imbalance technique.

ACS Style

Duksan Ryu; Okjoo Choi; Jongmoon Baik. Value-cognitive boosting with a support vector machine for cross-project defect prediction. Empirical Software Engineering 2014, 21, 43 -71.

AMA Style

Duksan Ryu, Okjoo Choi, Jongmoon Baik. Value-cognitive boosting with a support vector machine for cross-project defect prediction. Empirical Software Engineering. 2014; 21 (1):43-71.

Chicago/Turabian Style

Duksan Ryu; Okjoo Choi; Jongmoon Baik. 2014. "Value-cognitive boosting with a support vector machine for cross-project defect prediction." Empirical Software Engineering 21, no. 1: 43-71.

Conference paper
Published: 01 December 2014 in 2014 IEEE 17th International Conference on Computational Science and Engineering
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Software defect prediction is important for improving software quality. Defect predictors allow software test engineers to focus on defective modules. Cross-Project Defect Prediction (CPDP) uses data from other companies to build defect predictors. However, outliers may lower prediction accuracy. In this study, we propose a transfer learning based model called VAB-SVM for CPDP robust in handling outliers. Notably, this method deals with the class imbalance problem which may decrease the prediction accuracy. Our proposed method computes similarity weights of the training data based on the test data. Such weights are applied to Boosting algorithm considering the class imbalance. VAB-SVM outperformed the previous research more than 10% and showed a sufficient robustness regardless of the ratio of outliers.

ACS Style

Duksan Ryu; Okjoo Choi; Jongmoon Baik. Improving Prediction Robustness of VAB-SVM for Cross-Project Defect Prediction. 2014 IEEE 17th International Conference on Computational Science and Engineering 2014, 994 -999.

AMA Style

Duksan Ryu, Okjoo Choi, Jongmoon Baik. Improving Prediction Robustness of VAB-SVM for Cross-Project Defect Prediction. 2014 IEEE 17th International Conference on Computational Science and Engineering. 2014; ():994-999.

Chicago/Turabian Style

Duksan Ryu; Okjoo Choi; Jongmoon Baik. 2014. "Improving Prediction Robustness of VAB-SVM for Cross-Project Defect Prediction." 2014 IEEE 17th International Conference on Computational Science and Engineering , no. : 994-999.

Conference paper
Published: 01 May 2012 in 2012 IEEE/ACIS 11th International Conference on Computer and Information Science
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Software product line engineering has become widely known that it provides software organization with tremendous profits through systematical reuse to a number of organizations that require producing various and similar kinds of software products. However, when adopting software product lines, it has been reported that there are several challenges, e.g., the shortage of procedural support for application engineering, and original reasons of complexity and implicit properties for variability management. In order to address these major problems, an integrated tool support is required to guide software product line engineering process. The integrated software management tool for adopting software product lines, called "ISMT4SPL" can offer beneficial traceability among the artifacts created from domain engineering and application engineering. Furthermore, all the artifacts could consistently be managed by explicit representation of the relationship between artifacts generated at each software development phase by ISMTSPL. ISMT4SPL would contribute to adopting and guiding software product lines into the organizations that demand the seamless introduction of systematic reuse since the ISMT4SPL is helpful to manage variability by delivering the method that can generate a flexible and automatic variability model, which reduces complexity for managing a number of variation points and variants. In addition, consistency between all the artifacts would reduce confusion and quality problems by removing mismatches among them.

ACS Style

KiBum Park; Duksan Ryu; Jongmoon Baik. An Integrated Software Management Tool for Adopting Software Product Lines. 2012 IEEE/ACIS 11th International Conference on Computer and Information Science 2012, 553 -558.

AMA Style

KiBum Park, Duksan Ryu, Jongmoon Baik. An Integrated Software Management Tool for Adopting Software Product Lines. 2012 IEEE/ACIS 11th International Conference on Computer and Information Science. 2012; ():553-558.

Chicago/Turabian Style

KiBum Park; Duksan Ryu; Jongmoon Baik. 2012. "An Integrated Software Management Tool for Adopting Software Product Lines." 2012 IEEE/ACIS 11th International Conference on Computer and Information Science , no. : 553-558.

Conference paper
Published: 01 May 2012 in 2012 IEEE/ACIS 11th International Conference on Computer and Information Science
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The demand of new Social Networking Service (SNS) is high because the SNSs have been popular these days. In order to deliver various SNSs as early as possible, software product line (SPL) approach can be useful. By using the state of the practices of SPL, this paper shows how to manage commonalities and variabilities of SNS. Specifically, to make an architecture design, presented practices include: understanding relevant domains, requirements engineering, architecture definition. The strengths and weaknesses of Face book architecture are evaluated with the Architecture Tradeoff Analysis Method (ATAM). As a result of applying a framework for SPL practice, layered view and component-based view are illustrated along with variabilities represented by Product Line UML-based Software Engineering (PLUS) and Orthogonal Variability Model (OVM). Based on the analysis of requirements of SNS, additional services such as file sharing and instant messaging are represented as optional components. In case of Face book, three key quality attributes, i.e., availability, scalability, and privacy are analyzed by using quality attribute utility tree. We identified that Face book employs client-server architecture. Through ATAM, Peer-to-Peer (P2P) approach promoting privacy is explained.

ACS Style

Duksan Ryu; Dan Lee; Jongmoon Baik. Designing an Architecture of SNS Platform by Applying a Product Line Engineering Approach. 2012 IEEE/ACIS 11th International Conference on Computer and Information Science 2012, 559 -564.

AMA Style

Duksan Ryu, Dan Lee, Jongmoon Baik. Designing an Architecture of SNS Platform by Applying a Product Line Engineering Approach. 2012 IEEE/ACIS 11th International Conference on Computer and Information Science. 2012; ():559-564.

Chicago/Turabian Style

Duksan Ryu; Dan Lee; Jongmoon Baik. 2012. "Designing an Architecture of SNS Platform by Applying a Product Line Engineering Approach." 2012 IEEE/ACIS 11th International Conference on Computer and Information Science , no. : 559-564.