Automation requirements engineering using machine learning

dc.contributor.advisorAkhond, Mosta jur Rahman
dc.contributor.authorAbid, Md.Mehedy Hasan
dc.contributor.authorTanna, Rubaya Neshat
dc.contributor.authorNoyon, Mahbubur Rahman
dc.contributor.authorMasud, Jahidul Hasan
dc.contributor.authorAkter, Tahmina
dc.date.accessioned2022-03-14T08:24:31Z
dc.date.available2022-03-14T08:24:31Z
dc.date.issued2021-09
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 22-23).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
dc.description.abstractMachine learning algorithms help to automate the process in many di erent problem domains. In the eld of Software engineering. Requirement engineering is one of the rst stages of software development. This research aims to automate the process of requirements engineering by integrating machine-learning algorithms, which should reduce the development cost and the possibility of human errors in several stages of the software engineering process. The thesis requires extensive machine learning algorithms to identify the best-suited technologies in the software engineering arena. Finally, we will identify some evaluation matrix to identify the e ectiveness of our proposed algorithms for real-life software requirements speci cation.
dc.identifier.otherID 17101033
dc.identifier.otherID 17101204
dc.identifier.otherID 17101214
dc.identifier.otherID 17101418
dc.identifier.otherID 17301226
dc.identifier.otherhttps://dspace.bracu.ac.bd/server/api/core/items/108d974a-eb63-4f87-995d-ad1425967aa5
dc.identifier.urihttp://hdl.handle.net/10361/16453
dc.language.isoen
dc.publisherBRAC University
dc.sourceBRAC University Institutional Repository
dc.subjectAutomate
dc.subjectIntegrating
dc.subjectExtensive
dc.subjectIdentify
dc.subjectRequirement engineering
dc.subjectSoftware engineering
dc.subjectNLP
dc.subjectBERT
dc.titleAutomation requirements engineering using machine learning
dc.typeThesis

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