NLP-KAOS for Systems Goal Elicitation: Smart Metering System Case Study

被引：27

作者：

Casagrande, Erik ^{[1
]}

Woldeamlak, Selamawit ^{[1
]}

Woon, Wei Lee ^{[1
]}

Zeineldin, H. H. ^{[1
]}

Svetinovic, Davor ^{[1
]}

机构：

[1] Masdar Inst Sci & Technol, Dept Elect Engn & Comp Sci, Abu Dhabi, U Arab Emirates

来源：

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING | 2014年 / 40卷 / 10期

关键词：

Requirements engineering; goal elicitation; NLP; data mining; bibliometrics; REQUIREMENTS; ONTOLOGY;

D O I：

10.1109/TSE.2014.2339811

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper presents a computational method that employs Natural Language Processing (NLP) and text mining techniques to support requirements engineers in extracting and modeling goals from textual documents. We developed a NLP-based goal elicitation approach within the context of KAOS goal-oriented requirements engineering method. The hierarchical relationships among goals are inferred by automatically building taxonomies from extracted goals. We use smart metering system as a case study to investigate the proposed approach. Smart metering system is an important subsystem of the next generation of power systems (smart grids). Goals are extracted by semantically parsing the grammar of goal-related phrases in abstracts of research publications. The results of this case study show that the developed approach is an effective way to model goals for complex systems, and in particular, for the research-intensive complex systems.

引用

页码：941 / 956

页数：16

共 56 条

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