Earticle

초록

영어

In the past years, the scale of software is growing quickly as more and more organizations begin to deploy their business on Internet. As a result, requirement analysis becomes a challenging issue and conventional approaches might significantly increase the costs of software development. Therefore, automatic requirement analysis techniques have attract more and more attentions, which allows for modeling and analyzing requirements formally, rapidly and automatically, avoiding mistakes made by misunderstanding between engineers and users, and saving lots of time and manpower. In this paper, we propose an approach to acquiring requirements automatically, which adopts automated planning techniques and machine learning methods to convert software requirement into an incomplete planning domain. By this approach, we design an algorithm called Intelligent Planning based Requirement Analysis (IPRA), to learn action models with uncertain effects. A concrete experiment is conducted to investigate the proposed algorithm, and the results indicate that it can obtain a complete planning domain and convert it into software requirement specification.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Problem Description
 4. Framework of IPRA Algorithm
  4.1. Encode Plan Traces
  4.2. Generate Candidate Formulas of Actions
  4.3. Learn Weights of Candidate Formulas
  4.4. Obtain Action Model
 5. Experiments and Evaluation
  5.1. Settings
  5.2. Accuracy and Observed Intermediate States
  5.3. Plan Traces in Action-Model Learning
 6. Conclusion
 References

키워드

저자

• Tienan Zhang [ School of Computer & Communication, Hunan Institute of Engineering ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605