Digital Library[ Search Result ]
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.
Improvement Study on Active Learning-based Cross-Project Defect Prediction System
http://doi.org/10.5626/JOK.2023.50.11.931
This study proposes a practical improvement method for an active learning-based system for cross-project defect prediction. A previous study applied active learning tech- niques to practically improve the performance of cross-project defect prediction, but it used a traditional machine learning model that used hand-made features as input for active learning target selection and defect prediction, therefore feature extraction was expensive and performance was limited. In addition, the problem of performance deviation according to the selection of the input project remained. In this study, the following methods were proposed to overcome these limitations. First, we used a deep learning model that can use the source code as an input to lower the model building cost and improve prediction performance. Second, a Bayesian convolutional neural network is applied to select an active learning target using a deep learning model. Third, instead of considering a single source project, we applied a method that automatically extracts a training data set from multiple projects. Applying the system proposed in this study to 7 open source projects improved the average prediction performance by 13.58% compared to the previous latest research.
An Effective Comparative Framework for Cross-Project Defect Prediction Based on the Feature Selection Technique
http://doi.org/10.5626/JOK.2018.45.7.635
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.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr