C4.5: programs for machine learning
C4.5: programs for machine learning
Non-functional requirements: from elicitation to modelling languages
Proceedings of the 24th International Conference on Software Engineering
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Quantifying Non-Functional Requirements: A Process Oriented Approach
RE '04 Proceedings of the Requirements Engineering Conference, 12th IEEE International
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The Detection and Classification of Non-Functional Requirements with Application to Early Aspects
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Automatic Quality Assessment of SRS Text by Means of a Decision-Tree-Based Text Classifier
QSIC '07 Proceedings of the Seventh International Conference on Quality Software
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Approximation of COSMIC functional size to support early effort estimation in Agile
Data & Knowledge Engineering
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Non-functional Requirements (NFRs) such as software quality attributes, software design constraints and software interface requirements hold crucial information about the constraints on the software system under development and its behavior. NFRs are subjective in nature and have a broad impact on the system as a whole. Being distinct from Functional Requirements (FR), NFRs are dealt with special attention, as they play an integral role during software modeling and development. However, since Software Requirements Specification (SRS) documents, in practice, are written in natural language, solely holding the perspectives of the clients, the documents often end up with FR and NFR statements mixed together in the same paragraphs. It is, therefore, left upon the software analysts to classify and separate them manually. The research, presented in this paper, aims to automate the process of detecting NFR sentences by using a text classifier equipped with a part-of-speech (POS) tagger. The results reported in this paper outperform the recent work in the field, and achieved a higher accuracy of 98.56% using 10-folds-cross-validation over the same data used in the literature. The research reported in this paper is part of a larger project aimed at applying Natural Language Processing techniques in Software Requirements Engineering.