Requirements Evolution

2005 ◽  
Keyword(s):  
Requirements Evolution

scholarly journals Requirements Evolution and Evolution Requirements with Constrained Goal Models

2016 ◽  
pp. 544-552 ◽  
Author(s):  
Chi Mai Nguyen ◽  
Roberto Sebastiani ◽  
Paolo Giorgini ◽  
John Mylopoulos
Keyword(s):  
Requirements Evolution ◽  
Goal Models

Development of a Mesh Generation Code with a Graphical Front-End

2011 ◽  
Vol 23 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Jeffrey Carver
Keyword(s):  
Mesh Generation ◽  
Language Choice ◽  
Lessons Learned ◽  
Development Environment ◽  
Software Developer ◽  
Software Development Environment ◽  
Requirements Evolution ◽  
Code Structure ◽  
Scientists And Engineers

Scientists and engineers are increasingly developing software to enable them to do their work. A number of characteristics differentiate the software development environment in which a scientist or engineer works from the development environment in which a more traditional business/IT software developer works. This paper describes a case study, specifically about the development of a mesh-generation code. The goal of this case study was to understand the process for developing the code and identify some lessons learned that can be of use to other similar teams. Specifically, the paper reports on lessons learned concerning: requirements evolution, programming language choice, methods of communication among teammates, and code structure.

scholarly journals Sentiment Analysis Based Requirement Evolution Prediction

2019 ◽  
Vol 11 (2) ◽  
pp. 52 ◽  
Author(s):  
Lingling Zhao ◽  
Anping Zhao
Keyword(s):  
Short Term Memory ◽  
Topic Model ◽  
Short Term ◽  
Term Memory ◽  
Requirements Evolution ◽  
Product Features ◽  
Sentiment Orientation ◽  
Long Short Term Memory ◽  
Deep Learning Neural Network ◽  
Feature Evolution

To facilitate product developers capturing the varying requirements from users to support their feature evolution process, requirements evolution prediction from massive review texts is in fact of great importance. The proposed framework combines a supervised deep learning neural network with an unsupervised hierarchical topic model to analyze user reviews automatically for product feature requirements evolution prediction. The approach is to discover hierarchical product feature requirements from the hierarchical topic model and to identify their sentiment by the Long Short-term Memory (LSTM) with word embedding, which not only models hierarchical product requirement features from general to specific, but also identifies sentiment orientation to better correspond to the different hierarchies of product features. The evaluation and experimental results show that the proposed approach is effective and feasible.

Sign in / Sign up

Export Citation Format

Share Document