Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing - SETQA-NLP '09

2009 ◽  
Keyword(s):  
Quality Assurance ◽  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Language Processing

Ontology Formation and Comparison for Syllabus Structure Using NLP

2021 ◽  
Author(s):  
Masoom Raza ◽  
Aditee Patil ◽  
Mangesh Bedekar ◽  
Rashmi Phalnikar ◽  
Bhavana Tiple
Keyword(s):  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Comparative Study ◽  
Language Processing ◽  
Shared Knowledge ◽  
The Subject ◽  
The Creation ◽  
The Right ◽  
Collection And Management

Ontologies are largely responsible for the creation of a framework or taxonomy for a particular domain which represents the shared knowledge, concepts and how these concepts are related with each other. This paper shows the usage of ontology for the comparison of a syllabus structure of universities. This is done with the extraction of the syllabus, creation of ontology for the representing syllabus, then parsing the ontology and applying Natural language processing to remove unwanted information. After getting the appropriate ontologies, a comparative study is made on them. Restrictions are made over the extracted syllabus to the subject “Software Engineering” for convenience. This depicts the collection and management of ontology knowledge and processing it in the right manner to get the desired insights.

scholarly journals Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

2019 ◽  
Vol 48 (3) ◽  
pp. 432-445 ◽  
Author(s):  
Laszlo Toth ◽  
Laszlo Vidacs
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Software Systems ◽  
Functional Requirements ◽  
Natural Languages ◽  
System Analyst

Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients rarely emphasize these specifications as much as they might be justified. Identifying, classifying and reconciling the requirements is one of the main duty of the System Analyst, which task, without using a proper tool, can be very demanding and time-consuming. Tools which support text processing are expected to improve the accuracy of identification and classification of requirements even in an unstructured set of inputs. System Analysts can use them also in document archeology tasks where many documents, regulations, standards, etc. have to be processed. Methods elaborated in natural language processing and machine learning offer a solid basis, however, their usability and the possibility to improve the performance utilizing the specific knowledge from the domain of the software engineering are to be examined thoroughly. In this paper, we present the results of our work adapting natural language processing and machine learning methods for handling and transforming textual inputs of software development. The major contribution of our work is providing a comparison of the performance and applicability of the state-of-the-art techniques used in natural language processing and machine learning in software engineering. Based on the results of our experiments, tools can be designed which can support System Analysts working on textual inputs.

Sign in / Sign up

Export Citation Format

Share Document