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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.
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.
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.
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