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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.
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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