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Improved Software Defect Prediction with Gated Tab Transformer
Saranya Manikandan, Duksan Ryu
http://doi.org/10.5626/JOK.2025.52.3.196
Software Defect Prediction (SDP) plays a crucial role in ensuring software quality and reliability. Although, traditional machine learning and deep learning models are widely used for SDP, recent advancements in the field of natural language processing have paved the way for applying transformer-based models in software engineering tasks. This paper investigated transformer-based model as a potential approach to improve SDP model quality, ultimately aiming to enhance software quality and optimize testing resource allocation. Inspired by the Gated Tab Transformer’s (GTT) ability to effectively model relationship within features, we evaluated its effectiveness in SDP. We conducted experiments using 15 software defect datasets and compared results with other state-of-the-art machine learning and deep learning models. Our experiments showed that GTT outperformed state-of-the-art machine learning models in terms of recall, balance, and AUC (increase by 42.1%, 10.93%, and 7.1%, respectively). Cohen's d confirmed this advantage with large and medium effect sizes for GTT on these metrics. Additionally, an ablation study assessed the impact of hyperparameter variations on performance. Thus, GTT's effectiveness address the challenges of SDP, potentially leading to more effective testing resource allocation and improved software quality.
Review of QoS Research Considering Mobility in Mobile Edge Computing
Junseong Nam, Jaehyuk Lee, Duksan Ryu
http://doi.org/10.5626/JOK.2025.52.2.108
Mobile Edge Computing (MEC) is a technology that can enhance performance and reduce latency by performing data processing and cloud computing services not centrally, but at the edge of the network. Mobility is a key characteristic of MEC and a crucial factor in determining the Quality of Service (QoS) for users. The objective of this study is to review current state of research related to providing optimal services in MEC environments by predicting QoS, considering mobility. This study identified research areas related to MEC that aimed to provide optimal services to users while considering mobility, analyzed types of techniques used to address specific problem types, and examined characteristics and improvements made in each category. Based on analysis results, research was categorized into three main areas: security, QoS monitoring, and edge server placement. In the security domain, security techniques have been applied to data preprocessing, while the QoS monitoring domain has utilized collaborative filtering techniques considering data dependencies. The edge server placement domain has employed multi-objective optimization techniques. Through this study, follow-up researchers could better understand mobility-aware QoS research in MEC environments, thereby promoting further research on QoS improvement.
Reliability Evaluation of Cross Domain Solutions based on Reliability and Security Metrics
Eunjeong Ju, Jeonghwa Lee, Duksan Ryu
http://doi.org/10.5626/JOK.2024.51.10.870
This research focuses on developing a comprehensive reliability assessment approach for software used in Industry Control Systems (ICS) environments, particularly targeting safety inspection devices for control protocol traffic. Given the vulnerability of bidirectional communication in these devices, a novel reliability evaluation method tailored to their characteristics is essential. This study identifies and analyzes software reliability and security metrics throughout the Software Development Life Cycle (SDLC) based on prior research, with a specific emphasis on analysis, design, and implementation stages. The proposed assessment approach aimed to effectively address security and reliability issues that might arise in environments with bidirectional communication, offering valuable contributions to the development of highly reliable software for systems utilizing control protocols.
Cross-Project Defect Prediction for Ansible Projects
Sungu Lee, Sunjae Kwon, Duksan Ryu, Jongmoon Baik
http://doi.org/10.5626/JOK.2024.51.3.229
Infrastructure-as-Code (IaC) refers to the activities of automating overall management through code, such as creating and deploying infrastructure. Infrastructure-as-Code is used by many companies due to its efficiency, and many within-project defect prediction techniques have been proposed targeting Ansible, one of the IaC tools. Recently, a study on the applicability of Ansible"s cross-project defect prediction has been proposed. Therefore, Ansible’s cross-project defect prediction technique was used in this study, and its effectiveness was analyzed. Experimental results showed that the performance of the F1-based cross-project defect prediction was measured to be 0.3 to 0.5, and that it could be used as an alternative to the internal project defect prediction technique. It is therefore anticipated that this will be put to use in support of Ansible’s software quality assurance activities.
Comparative Analysis of Accuracy and Stability of Software Reliability Estimation Models based on Recurrent Neural Networks
Taehyoun Kim, Duksan Ryu, Jongmoon Baik
http://doi.org/10.5626/JOK.2023.50.8.688
Existing studies on software reliability estimation based on recurrent neural networks have used networks to create one model under the same conditions and evaluated the accuracy of the model. However, due to the randomness of artificial neural networks, such recurrent neural networks can generate different training results of models even under the same conditions, which can lead to inaccurate software reliability estimation. Therefore, this paper compares and analyzes which recurrent neural networks could estimate software reliability more stably and accurately. We estimated software reliability in eight real projects using three representative recurrent neural networks and compared and analyzed the performances of these models in terms of accuracy and stability. As a result, Long Short-Term Memory showed the most stable and accurate software reliability estimation performance. A more accurate and stable software reliability estimation model is expected to be selected based on the results of this study.
Analysis of Adversarial Learning-Based Deep Domain Adaptation for Cross-Version Defect Prediction
Jiwon Choi, Jaewook Lee, Duksan Ryu, Suntae Kim
http://doi.org/10.5626/JOK.2023.50.6.460
Software defect prediction is a helpful technique for effective testing resource allocation. Software cross-version defect prediction reflects the environment in which the software is developed in a continuous version, with software modules added or deleted through a version update process. Repetition of this process can cause differences in data distribution between versions, which can negatively affect defect prediction performance. Deep domain adaptation(DeepDA) techniques are methods used to reduce distribution difference between sources and target data in the field of computer vision. This paper aims to reduce difference in data distribution between versions using various DeepDA techniques and to identify techniques with the best defect prediction performance. We compared performance between deep domain adaptation techniques (i.e., Domain-Adversarial Neural Network (DANN), Adversarial Discriminator Domain Apaptation (ADDA), and Wasserstein Distance Guided Representation Learning (WDGRL)) and identified performance differences according to the pair of source data. We also checked performance difference according to the ratio of target data used in the learning process and performance difference in terms of hyperparameter setting of the DANN model. Experimental results showed that DANN was more suitable for cross-version defect prediction environments. The DANN model performed the best when using all previous versions of data except the target version as a source. In particular, it showed the best performance when setting the number of hidden layers of the DANN model to 3. In addition, when applying the DeepDA technique, the more target data used in the learning process, the better the performance. This study suggests that various DeepDA techniques can be used to predict software cross-version defects in the future.
RESEDA: Software REliability Model SElection using DAta-driven Software Reliability Prediction
Nakwon Lee, Duksan Ryu, Ilhoon Cho, Jeakun Song, Jongmoon Baik
http://doi.org/10.5626/JOK.2022.49.6.443
To solve the model generalization problem, i.e., there is no single best model that fits all types of software failure data, model selection techniques and data-driven reliability prediction techniques have been proposed. However, model selection techniques still wrongly select some failure data, and the reliability metrics that the data-driven techniques can observe are limited. In this paper, we propose a software reliability model selection technique using data-driven reliability prediction to improve the prediction accuracy with obtaining reliability metrics. The proposed approach decides either selection or data-driven for target failure data using a classifier generated from historical failure data sets. If data-driven is chosen, the proposed approach builds an augmented failure data using the prediction results of the data-driven technique and selects a model for the augmented data. The proposed approach shows a 21% lower median value of the mean error of prediction compared to the best technique for comparison. With the improved reliability prediction accuracy using the proposed approach, the higher software reliability is achieved.
Identification of Generative Adversarial Network Models Suitable for Software Defect Prediction
Jiwon Choi, Jaewook Lee, Duksan Ryu, Suntae Kim
http://doi.org/10.5626/JOK.2022.49.1.52
Software Defect Prediction(SDP) helps effectively allocate quality assurance resources which are limited by identifying modules that are likely to cause defects. Software defect data suffer from class imbalance problems in which there are more non-defective instances than defective instances. In most machine learning methods, the defect prediction performance is degraded when there is a disproportionate number of instances belonging to a particular class. Therefore, this research aimed to solve the class imbalance problem and improve defect prediction performance by using a Generative Adversarial Network(GAN) model. To this end, we compared different kinds of GAN models for their suitability for SDP and checked the applicability of GAN models that were not applied in the related work. In our study, Vanilla-GAN(GAN), Conditional GAN (cGAN), and Wasserstein GAN (WGAN) models which were initially proposed for image generation were adapted for software defect prediction. Then those modified models were compared with Tabular GAN(TGAN) and Modeling Tabular data using Conditional GAN(CTGAN). Our experimental results showed that the CTGAN model is suitable for SDP data. We also conducted a sensitivity analysis examining which hyper-parameter values of CTGAN increase the recall rate and lower the probability of false alarm (PF). Our experimental results indicated that the hyper-parameters should be adjusted according to the dataset. We expect that our proposed approach can help effectively allocate limited resources by improving the performance of SDP.
A Selection Technique of Source Project in Heterogeneous Defect Prediction based on Correlation Coefficients
Eunseob Kim, Jongmoon Baik, Duksan Ryu
http://doi.org/10.5626/JOK.2021.48.8.920
The software defect prediction techniques try to predict defect-prone modules and ensure the quality of the developing software using previous defect data. Nowadays, heterogeneous defect prediction (HDP) techniques have been applying defect prediction techniques even when the metrics between source and target projects are different. Previous HDP techniques focused on improving prediction performance when the source and target projects were given. However in a real development environment, more than one source projects exist for one target project, thus identifying a project that is suitable for source data is challenging. This paper suggests a correlation-based selection technique for source projects in HDP. After the metric matching process, correlation coefficients are calculated for each corresponding metric, and the project with the highest score is selected for source data. The experiment shows that the performance of the proposed selection method is higher than the results of random selection, and removing projects with less than 100 instances from the source candidates improves the performance. Therefore, using the proposed selection technique could improve the prediction accuracy in HDP.
Improved Prediction for Configuration Bug Report Using Text Mining and Dimensionality Reduction
Jeongwhan Choi, Jiwon Choi, Duksan Ryu, Suntae Kim
http://doi.org/10.5626/JOK.2021.48.1.35
Configuration bugs are one of the main causes of software failure. Software organizations collect and manage bug reports using an issue tracking system. The bug assignor can spend excessive amounts of time identifying whether a bug is a configuration bug or not. Configuration bug prediction can help the bug assignor reduce classification efforts and aid decision making. In this paper, we propose an improved classification model using text mining and dimensionality reduction. This paper extracts 4,457 bug reports from six open-source software projects, trains a model to classify configuration bug reports, and evaluates prediction performance. The best performance method is obtained using the k-Nearest Neighbors model with the SMOTEENN sampling technique after extracting the feature with Bag of Words and then reducing the dimension of the feature using Linear Discriminant Analysis. The results show that ROC-AUC is 0.9812 and MCC is 0.942. This indicates better performance than Xia et al."s method and solves the class imbalance problem of our previous study. By predicting these enhanced configuration bug reports, our proposed approach can provide the bug assignors with information they need to make informed decisions.
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