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Duksan Ryu(류덕산),Jongmoon Baik(백종문) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.7
Software defect prediction (SDP) can help optimally allocate software testing resources on fault-prone modules. Typically, local data within a company are used to build classifiers. Unlike such Within-Project Defect Prediction (WPDP), there may exist some cases, e.g., pilot projects, without any collected data from historical projects. Cross-project defect prediction (CPDP) using data from other projects can be employed in such cases. The defect prediction performance may be degraded in the presence of irrelevant or redundant information. To address this issue, various feature selection techniques have been suggested. Until now, there has been no research on identifying effective feature selection techniques for CPDP. We present a comparative framework using feature selection to produce a high performance for CPDP. We compare eight existing feature selection techniques, for three CPDP and one WPDP model, based on feature subset evaluators and feature ranking methods. After the features are chosen that perform the best, classifiers are built, tested, and evaluated using the statistical significance and effect size tests. Hybrid Instance Selection using Nearest-Neighbor (HISNN) is better than the other CPDP models and comparable to the WPDP model. Results from the comparison show that a different distribution, class imbalance and feature selection should be considered to obtain a high performance CPDP model.
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교차 프로젝트 결함 예측 성능 향상을 위한 효과적인 하모니 검색 기반 비용 민감 부스팅 최적화
류덕산 ( Duksan Ryu ),백종문 ( Jongmoon Baik ) 한국정보처리학회 2018 정보처리학회 논문지 Vol.7 No.3
Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.
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유한상태기 계를 적용한 자율주행차의 경로계획기법 사례연구
류덕산 ( Duksan Ryu ),백종문 ( Jongmoon Baik ) 한국정보처리학회 2018 한국정보처리학회 학술대회논문집 Vol.25 No.1
자율주행차에서 경로계획기법은 지도,목적지 경로와 다른 정적/동적 장애물에 대한 예측 정보를 바탕으로,안전하고,합법적이며 효율적으로 차량을 조종하는 목표를 가진다. 고속도로 환경에서, 차량이 차선을 유지하고,다른 차량들과 충돌을 회피하며,더 느리게 움직이는 트래픽을 지나쳐 효율적이면서 안전한 경로를 생성하는 기법이 요구된다. 본 연구에서는,시스템의 행위를 모델링하는 기법 중의 하나인 유한상태기계를 적용하였다. 시뮬레이터를 통해,급가속/감속과,충돌 없이,차선을 유지/변경을 할 수 있음을 보였다. 자율주행차의 고속도로 주행의 경우,유한상태기계를 적용하여,효율적이고 안전한 경로계획을 수행할 수 있다.
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A Comparative Study on Similarity Measure Techniques for Cross-Project Defect Prediction
류덕산,백종문,Ryu, Duksan,Baik, Jongmoon Korea Information Processing Society 2018 정보처리학회 논문지 Vol.7 No.6
Software defect prediction is helpful for allocating valuable project resources effectively for software quality assurance activities thanks to focusing on the identified fault-prone modules. If historical data collected within a company is sufficient, a Within-Project Defect Prediction (WPDP) can be utilized for accurate fault-prone module prediction. In case a company does not maintain historical data, it may be helpful to build a classifier towards predicting comprehensible fault prediction based on Cross-Project Defect Prediction (CPDP). Since CPDP employs different project data collected from other organization to build a classifier, the main obstacle to build an accurate classifier is that distributions between source and target projects are not similar. To address the problem, because it is crucial to identify effective similarity measure techniques to obtain high performance for CPDP, In this paper, we aim to identify them. We compare various similarity measure techniques. The effectiveness of similarity weights calculated by those similarity measure techniques are evaluated. The results are verified using the statistical significance test and the effect size test. The results show k-Nearest Neighbor (k-NN), LOcal Correlation Integral (LOCI), and Range methods are the top three performers. The experimental results show that predictive performances using the three methods are comparable to those of WPDP.
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향상된 교차 버전 결함 예측을 위한 베이지안 최적화 프레임워크
최정환 ( Jeongwhan Choi ),류덕산 ( Duksan Ryu ) 한국정보처리학회 2021 정보처리학회 논문지 Vol.10 No.9
In recent software defect prediction research, defect prediction between cross projects and cross-version projects are actively studied. Cross-version defect prediction studies assume WP(Within-Project) so far. However, in the CV(Cross-Version) environment, the previous work does not consider the distribution difference between project versions is important. In this study, we propose an automated Bayesian optimization framework that considers distribution differences between different versions. Through this, it automatically selects whether to perform transfer learning according to the difference in distribution. This framework is a technique that optimizes the distribution difference between versions, transfer learning, and hyper-parameters of the classifier. We confirmed that the method of automatically selecting whether to perform transfer learning based on the distribution difference is effective through experiments. Moreover, we can see that using our optimization framework is effective in improving performance and, as a result, can reduce software inspection effort. This is expected to support practical quality assurance activities for new version projects in a cross-version project environment.
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