Improving the Quality of Natural Language Requirements Specifications through Natural Language Requirements Patterns

2006 ◽  
Author(s):  
Sri Tjong ◽  
Nasreddine Hallam ◽  
Michael Hartley
Keyword(s):  
Natural Language ◽  
Requirements Specifications ◽  
Natural Language Requirements ◽  
Requirements Patterns

scholarly journals On systematically building a controlled natural language for functional requirements

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Alvaro Veizaga ◽  
Mauricio Alferez ◽  
Damiano Torre ◽  
Mehrdad Sabetzadeh ◽  
Lionel Briand
Keyword(s):  
Information System ◽  
Natural Language ◽  
Qualitative Methodology ◽  
Case Study Research ◽  
Empirical Evaluation ◽  
Functional Requirements ◽  
Industrial Case Study ◽  
Requirements Specifications ◽  
Financial Domain

AbstractNatural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.

scholarly journals Sentiment of App with Word Vectors

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2156-2159
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Data ◽  
Vector Representations ◽  
Text Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.

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