Banca de QUALIFICAÇÃO: ELTONI ALVES GUIMARÃES

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : ELTONI ALVES GUIMARÃES
DATE: 22/02/2022
TIME: 09:00
LOCAL: Remoto
TITLE:

Identifying and Classifying Ambiguities in User Stories: A Study Using Machine Learning

KEY WORDS:

Ambiguity in natural language; User Stories; Machine Learning

PAGES: 60
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Engenharia de Software
SUMMARY:

 

Ambiguity in requirements writing is one of the most common defects found in requirements documents. There are a variety of concepts about what is ambiguity in requirements and to identify ambiguity one must better understand each concept. Ambiguity can compromise the quality of User Stories and can be present in requirements written in natural language. In the literature, there are few studies that investigate the potential of Machine Learning algorithms to classify ambiguity in User Stories. This dissertation aims to propose an approach to identify and classify ambiguity in User Stories through the use of Machine Learning algorithms. Thus, a checklist was developed to help in the identification of ambiguities in User Stories and a Machine Learning approach will be used using two algorithms: (i) Support Vector Machine; (ii) Random Forest. Each model generated by the algorithm will be evaluated and compared.

BANKING MEMBERS:
Presidente - 2195240 - MARCIA JACYNTHA NUNES RODRIGUES LUCENA
Interno - 1671962 - EDUARDO HENRIQUE DA SILVA ARANHA
Externo ao Programa - 2885532 - IVANOVITCH MEDEIROS DANTAS DA SILVA