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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.
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.
Efficient Compilation Error Localization with DNN
http://doi.org/10.5626/JOK.2022.49.6.434
There are few programs with no compilation errors. The compiler provides the programmers with compiler error messages as clues to solve the problem, but analyzing the error messages correctly also consumes much time. Although there are many proposals that suggest the error localization method and how to repair the error, most of the proposals are using data from novice programmers, or can be applied only to one specific programming language. It is difficult to apply practically in large-scale projects conducted in the company. In this study, to increase the efficiency of compile error handling in practical projects, we propose DeepErrorFinder which identifies the location of compilation errors using DNN. This model, which is based on the LSTM model, predicts the error location after training based on compilation error logs, and repair changes from mobile phone software development projects. As a result of the experiments, it showed an accuracy of 52% and reduced the elapsed time compared to a manual search. It can facilitate quickly finding the location of the compilation error code in practice projects.
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.
Behavior Model-Based Fault Localization for RESTful Web Applications
Jong-In Jang, Nakwon Lee, Duksan Ryu, Jongmoon Baik
http://doi.org/10.5626/JOK.2020.47.11.1044
Because of the nature of Web applications being more complex, larger in scale and more likely to be composed of black box components compared to traditional software systems wherein fault localization techniques are actively used, existing techniques can be only minimally applied to localize faults in Web applications. Also, existing studies to localize a fault in a complex system such as a Web application system also have limitations in capturing the indirect interactions in Web applications and suffers from the Web application’s dynamic nature. In this study, we propose a behavior modeling-based fault localization for the RESTful Web applications. The approach models a RESTful Web application as a sequence of behaviors that captures the direct and indirect interactions in the application. The modeling process is lightweight and it is not necessary to build the model in advance of the actual execution of application. The spectrum-based fault localization is then performed in the granularity of behavior pairs in the behavior model. To demonstrate the approach, a case study on the RESTful Web application built upon the YouTube Data API v3 was conducted and demonstrated that the approach can successfully resolve aforementioned difficulties and localize a fault in the RESTful Web application.
A Case Study of Industrial Software Defect Prediction in Maritime and Ocean Transportation Industries
Jonggu Kang, Duksan Ryu, Jongmoon Baik
http://doi.org/10.5626/JOK.2020.47.8.769
Software defect prediction is a field of study that predicts defects in newly developed software in advance of use, based on models trained with past software defects and software update information using various latest machine learning techniques. It can provide a guide to effectively operate and deploy software quality assurance (SQA) resources in industry practices. Recently, there have been papers that have investigated the industrial application of software defect prediction, but more active research is needed to analyze how this can be applied over diverse domains with different characteristics. In this paper, we present the possibility of applying software defect prediction in the maritime and ocean transportation industries. These are facing challenges to build and deploy the types of emerging transportations such as high-efficiency eco-friendly ships, connected ships, smart ships, unmanned ships, or autonomous ships. In our experiments using actual data collected from the domain, the software defect prediction showed high defect prediction performance with 0.91 accuracy and 0.831 f-measure. This suggests that software defect prediction can be a useful tool to allocate SQA resources effectively in this field.
Controlling a Traversal Strategy of Abstract Reachability Graph-based Software Model Checking
http://doi.org/10.5626/JOK.2017.44.10.1034
Although traversal strategies are important for the performance of model checking, many studies have ignored the impact of traversal strategies in model checking with a block-encoded abstract reachability graph. Studies have considered traversal strategies only for an abstract reachability graph without block-encoding. Block encoding plays a crucial role in the model checking performance. This paper therefore describes Dual-traversal strategy, a simple and novel technique to control traversal strategies in a block-encoded abstract reachability graph. This method uses two traversal strategies for a model checking, one for effective block-encoding, and the other for traversal in an encoded abstract reachability graph. Dual-traversal strategy is very simple and can be implemented without overhead compared to the existing single-traversal strategy. We implemented the Dual-traversal strategy in an open source model checking tool and compare the performances of different traversal strategies. The results show that the model checking performance varies from the traversal strategies for the encoded abstract reachability graph.
Automatic Prioritization of Requirements using Topic Modeling and Stakeholder Needs-Artifacts
Due to the limitations of budget, resources, and time invested in a project, software requirements should be prioritized and be implemented in order of importance. Existing approaches to prioritizing requirements mostly depend on human decisions. The manual prioritization process is based on intensive interactions with the stakeholders, thus raising the issues of scalability and biased prioritization. To solve these problems, we propose a fully automated requirements prioritization approach, ToMSN (Topic Modeling Stakeholder Needs for requirements prioritization), by topic modeling the stakeholder needs-artifacts earned in the requirements elicitation phase. The requirements dataset of a 30,000-user system was utilized for the performance evaluation. ToMSN showed competitive prioritizing accuracy with existing approaches without human aids, therefore solving scalability and biased prioritization issues.
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