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Web Application Technology Stack Recommender

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dc.contributor.author Ramzan, Mohamed
dc.date.accessioned 2020-05-20T06:06:01Z
dc.date.available 2020-05-20T06:06:01Z
dc.date.issued 2019
dc.identifier.citation Ramzan, Mohamed (2019) WATSR: Web Application Technology Stack Recommender. BSc. Dissertation Informatics Institute of Technology. en_US
dc.identifier.other 2015237
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/395
dc.description.abstract Technology stack is a strong foundation for any project. Project success is dependent on the strength of the technologies used in the project. One of the essential factors of any project success is the technology stack. The technology decisions are made at the beginning of a project so during later phases changing the technology is hard. So selecting the most suitable technology stack for a project is essential. Due to various reasons, it has become a hassle to choose the most appropriate technology stack. Even after decades of technological advancements, there is no tool to recommend the most suitable technology stack. WATSR intends to solve this issue and help the community in increasing project success. The proposed system intends to recommend technology solution and technology stack for web application based on the requirements that can be given as either manually given requirement list or requirement specification. One other aspect intended solution covers are user perspective about the technology solution. The proposed system will summarise user discussions of technology solution based on the feature and user intention. So the developers can acquire summarised user perspectives of a technology solution. The primary target audience was beginner developers who lack the experience to choose the suitable technology stack. However, the author identified that this kind of tool could be beneficial for even very experienced architects due to the ever-growing number of technology solutions. en_US
dc.subject Automatic Text Classification en_US
dc.subject Sentiment Analysis en_US
dc.subject Natural Language Processing en_US
dc.title Web Application Technology Stack Recommender en_US
dc.type Thesis en_US


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