Please use this identifier to cite or link to this item:

http://hdl.handle.net/10609/105807
Title: Automated similarity detection: identifying duplicated requirements
Author: Motger de la Encarnacion, Quim
Director: Ventura Royo, Carles  
Tutor: Palomares Bonache, Cristina
Keywords: requirements engineering
similarity detection
duplicated requirements
Issue Date: Dec-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: Machine-Learning (ML) and Natural-Language-Processing (NLP) are two of the most known areas of Artificial Intelligence (AI). ML is a general-purpose technology which uses data to learn real-world knowledge and to improve the reliability of a specific action - typically to extract autonomous predictions about partial data observations. On the other hand, NLP applies to the task of developing representations of features of natural language based on its textual information. One area of application of NLP and ML is the Requirements Engineering (RE) field. RE is the set of processes of Software Engineering (SE) focused on the management of a set of requirements that describes a system. Between the challenges of RE, it is highlighted the detection of duplicated requirements. If ignored, these duplicities may lead to redundancy in the textual information of a project and therefore this may lead to the duplicity of tasks. Moreover, the automation of this process and the standardized usage of specific, accurate tools are still at a state-of-the-art stage. This master thesis is a state-of-the-art analysis to apply automated requirements similarity detection, using AI techniques, for the detection of duplicates between project requirements. Based on a literature review, this thesis must be a practical evaluation and a development proposal of duplicate detection in SE project requirements. This work is developed within the OpenReq project, an EU-Horizon-2020 project whose goal is "to build an intelligent decision system for community-driven RE". This collaboration allows the usage of real requirements data to evaluate the algorithms developed in this project.
Language: English
URI: http://hdl.handle.net/10609/105807
Appears in Collections:Bachelor thesis, research projects, etc.

Share:
Export:
Files in This Item:
File Description SizeFormat 
jmotgerTFM1219memory.pdfTFM memory2.06 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons