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Software Product Line Feature Extraction from Natural Language Documents using Machine Learning Techniques
MSc Yang Li
Land (Sachsen-Anhalt) ;
Feature model construction from the requirements or textual descriptions of products can be often tedious and ineffective. In this project, through automatically learning natural language documents of products, cluster tight-related requirements into features in the phase of domain analysis based on machine learning techniques. This method can assist the developer by suggesting possible features, and improve the efficiency and accuracy of feature modeling to a certain extent.

This research will focus on feature extraction from requirements or textual descriptions of products in domain analysis. Extract the descriptors from requirements or textual descriptions of products. Then, descriptors are transformed into vectors and form a word vector space. Based on clustering algorithm, a set of descriptors are clustered into features. Their relationships will be inferred. Design the simulation experiment of feature extraction from natural language documents of products to prove that it can handle feature-extracting in terms of machine learning techniques.


Feature extraction, Software Product Line, machine learning, natural language documents
Prof. Dr. Gunter Saake

Prof. Dr. Gunter Saake

Otto-von-Guericke-Universität Magdeburg

Fakultät für Informatik

Institut für Technische und Betriebliche Informationssysteme

Universitätsplatz 2



Tel.+49 391 6758800

Fax:+49 391 6712020


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