Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A vector space model for automatic indexing
Communications of the ACM
Exploring the Web with reconnaissance agents
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Storage and Retrieval Systems: Theory and Implementation
Information Storage and Retrieval Systems: Theory and Implementation
Lightweight validation of natural language requirements
Software—Practice & Experience
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Application of Linguistic Techniques for Use Case Analysis
RE '02 Proceedings of the 10th Anniversary IEEE Joint International Conference on Requirements Engineering
Computer-Assisted Analysis and Refinement of Informal Software Requirements Documents
APSEC '98 Proceedings of the Fifth Asia Pacific Software Engineering Conference
Nonfunctional Requirements: From Elicitation to Conceptual Models
IEEE Transactions on Software Engineering
Market research for requirements analysis using linguistic tools
Requirements Engineering
RE '04 Proceedings of the Requirements Engineering Conference, 12th IEEE International
IEEE Transactions on Knowledge and Data Engineering
Reasoning about inconsistencies in natural language requirements
ACM Transactions on Software Engineering and Methodology (TOSEM)
Using a Hybrid Method for Formalizing Informal Stakeholder Requirements Inputs
CERE '06 Proceedings of the Fourth Internationa Workshop on Comparative Evaluation in Requirements Engineering
Speech Detection of Stakeholders' Non-Functional Requirements
MERE '06 Proceedings of the First International Workshop on Multimedia Requirements Engineering
The Detection and Classification of Non-Functional Requirements with Application to Early Aspects
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Automated classification of non-functional requirements
Requirements Engineering
Information and Software Technology
Fourth international workshop on Software quality assurance: in conjunction with the 6th ESEC/FSE joint meeting
Using data mining and recommender systems to scale up the requirements process
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
A Context Awareness Non-functional Requirements Metamodel Based on Domain Ontology
WSCS '08 Proceedings of the IEEE International Workshop on Semantic Computing and Systems
Potentials and challenges of recommendation systems for software development
Proceedings of the 2008 international workshop on Recommendation systems for software engineering
A recommender system for requirements elicitation in large-scale software projects
Proceedings of the 2009 ACM symposium on Applied Computing
ElicitO: a quality ontology-guided NFR elicitation tool
REFSQ'07 Proceedings of the 13th international working conference on Requirements engineering: foundation for software quality
Semi-Supervised Learning
A clustering-based approach for discovering flaws in requirements specifications
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Automatic analysis of multimodal requirements: a research preview
REFSQ'12 Proceedings of the 18th international conference on Requirements Engineering: foundation for software quality
Mining textual requirements to assist architectural software design: a state of the art review
Artificial Intelligence Review
The state of the art in automated requirements elicitation
Information and Software Technology
Is knowledge power? the role of knowledge in automated requirements elicitation
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
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Context: Early detection of non-functional requirements (NFRs) is crucial in the evaluation of architectural alternatives starting from initial design decisions. The application of supervised text categorization strategies for requirements expressed in natural language has been proposed in several works as a method to help analysts in the detection and classification of NFRs concerning different aspects of software. However, a significant number of pre-categorized requirements are needed to train supervised text classifiers, which implies that analysts have to manually assign categories to numerous requirements before being able of accurately classifying the remaining ones. Objective: We propose a semi-supervised text categorization approach for the automatic identification and classification of non-functional requirements. Therefore, a small number of requirements, possibly identified by the requirement team during the elicitation process, enable learning an initial classifier for NFRs, which could successively identify the type of further requirements in an iterative process. The goal of the approach is the integration into a recommender system to assist requirement analysts and software designers in the architectural design process. Method: Detection and classification of NFRs is performed using semi-supervised learning techniques. Classification is based on a reduced number of categorized requirements by taking advantage of the knowledge provided by uncategorized ones, as well as certain properties of text. The learning method also exploits feedback from users to enhance classification performance. Results: The semi-supervised approach resulted in accuracy rates above 70%, considerably higher than the results obtained with supervised methods using standard collections of documents. Conclusion: Empirical evidence showed that semi-supervision requires less human effort in labeling requirements than fully supervised methods, and can be further improved based on feedback provided by analysts. Our approach outperforms previous supervised classification proposals and can be further enhanced by exploiting feedback provided by analysts.