A Semantical Approach for Automatically Transforming Software Requirement Specification into Formal Presentation

2011 ◽  
Vol 225-226 ◽  
pp. 776-779
Author(s):  
Shu Chen ◽  
Ming Kai Chen
Keyword(s):  
Natural Language ◽  
Formal Model ◽  
High Quality ◽  
Requirement Specification ◽  
Requirement Model ◽  
Formal Presentation ◽  
Automatic Mapping ◽  
Requirement Specifications ◽  
Software Requirement Specification ◽  
Quality Production

Software engineering is a critical step in obtaining high quality production. However, requirement specifications that written in natural language is inevitably has ambiguity. Modern driven architecture makes use of requirement model for the complement of requirement specification to eliminate such ambiguity. However, currently, the transformation from requirement specification into formal model only limited in syntax level, thus lack of correctness and precision. This paper proposed an approach in semantical level to process textual specifications of the requirements of unlimited natural language and their automatic mapping to the formal presentation.

scholarly journals Actionable Analytics on Software Requirement Specifications

2020 ◽  
Vol 8 (5) ◽  
pp. 1921-1928
Keyword(s):  
Software Engineering ◽  
Relevant Information ◽  
Semantic Space ◽  
Requirement Specification ◽  
Software Requirement ◽  
Mining Software Engineering Data ◽  
Using Data ◽  
Software Practitioners ◽  
Requirement Specifications ◽  
Software Requirement Specification

The volume of data and need for churning this data to provide useful information has increased the scope of data mining and made it promising in recent years. Software intelligence (SI) (as the future of the mining software engineering data) presents theories and techniques to augment software decision making by using fact-based support systems. SI exposes software practitioners to up-to-date and relevant information to support their daily decision activities over the complete software development life cycle. Software documents contain important information for a plenty of software engineering tasks and one such important document is Software requirement specification (SRS) which details the system and user requirements. Inexplicit, ambiguous or imperfect requirements guide leads to a non-acceptable product by users. Constructing of a strong software specification can be supported by building a semantic space, validating new specification for completeness, categorization of software requirement specification and identification of significant concepts and related keywords. This paper proposes a knowledge management system for software document repositories using data analytics and demonstrates its creation and usage for a document set of software requirement specifications

scholarly journals Algorithm for Automatic Detection of Ambiguities from Software Requirements

2019 ◽  
Vol 8 (9S) ◽  
pp. 878-882
Keyword(s):  
Natural Language ◽  
Automatic Detection ◽  
Software Requirements ◽  
Software Systems ◽  
Requirement Specification ◽  
Software Requirement ◽  
Requirement Document ◽  
Different Types ◽  
Specification Document ◽  
Software Requirement Specification

Software Systems are built by the Software engineers and must ensure that software requirement document (SRS) should be specific. Natural Language is the main representation of Software requirement specification document, because it is the most flexible and easiest way for clients or customers to express their software requirements [2]. However being stated in natural language, software requirement specification document may lead to ambiguities [28]. The main goal of presented work to automatically detection of the different types of ambiguities like Lexical, Syntactic, Syntax and Pragmatic. Then an algorithm is proposed to early detection the different types of ambiguities from software requirement document. Part of Speech (POS) technique and regular expression is used to detect each type of ambiguities. An algorithm presented in this paper have two main goals (1) Automatic detection of different types of ambiguities. (2) Count the total number of each types of ambiguities found and evaluate the percentage of ambiguous and non- ambiguous statements detected from software requirement document. The suggested algorithm can absolutely support the analyst in identifying different kinds of ambiguities in Software requirements specification (SRS) document.

scholarly journals Human Annotated Dialogues Dataset for Natural Conversational Agents

2020 ◽  
Vol 10 (3) ◽  
pp. 762
Author(s):  
Erinc Merdivan ◽  
Deepika Singh ◽  
Sten Hanke ◽  
Johannes Kropf ◽  
Andreas Holzinger ◽  
...  
Keyword(s):  
Natural Language ◽  
Detailed Analysis ◽  
Human Perception ◽  
Natural Language Understanding ◽  
Industrial Applications ◽  
Benchmark Dataset ◽  
Conversational Agents ◽  
High Quality ◽  
Language Understanding ◽  
Major Drawback

Conversational agents are gaining huge popularity in industrial applications such as digital assistants, chatbots, and particularly systems for natural language understanding (NLU). However, a major drawback is the unavailability of a common metric to evaluate the replies against human judgement for conversational agents. In this paper, we develop a benchmark dataset with human annotations and diverse replies that can be used to develop such metric for conversational agents. The paper introduces a high-quality human annotated movie dialogue dataset, HUMOD, that is developed from the Cornell movie dialogues dataset. This new dataset comprises 28,500 human responses from 9500 multi-turn dialogue history-reply pairs. Human responses include: (i) ratings of the dialogue reply in relevance to the dialogue history; and (ii) unique dialogue replies for each dialogue history from the users. Such unique dialogue replies enable researchers in evaluating their models against six unique human responses for each given history. Detailed analysis on how dialogues are structured and human perception on dialogue score in comparison with existing models are also presented.

scholarly journals Quality characteristics of spatial furniture

2009 ◽  
pp. 135-145 ◽  
Author(s):  
Dusan Skakic ◽  
Igor Dzincic
Keyword(s):  
Quality Control ◽  
Quality Characteristics ◽  
High Quality ◽  
Production Economy ◽  
Production Practice ◽  
Basic Quality ◽  
Quality Of Products ◽  
Quality Production

The quality of products represents one of the key aims of any modern organized production. In the production practice, it is essential to establish the optimal relationship between quality, production economy and delivery deadlines. Furniture quality is evaluated by three levels and they are: basic quality, high quality and especially high quality. The results presented in this paper are based on the sample measurements of chairs and tables during 2007 and 2008 at the Institute for Furniture Quality Control.

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