Mining Aspects in Requirements
@inproceedings{Sampaio2005MiningAI, title={Mining Aspects in Requirements}, author={Am{\'e}rico Sampaio and Neil Loughran and Awais Rashid and Paul Rayson}, year={2005} }
The early identification and documentation of crosscutting concerns enables better change management and traceability of requirements. Moreover, this also improves the early identification of candidate aspects in the design and implementation stages. Current techniques for identifying aspects in requirements are ineffective when requirements are complex or unstructured. This paper describes an approach that utilises corpus-based natural language processing (NLP) techniques to effectively enable…
82 Citations
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This paper describes a tool, EA-Miner, that provides effective automated support for identifying and separating aspectual and non-aspectual concerns as well as their crosscutting relationships at the requirements level.
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Novel use of text mining is proposed as a technology to assist in the discovery and verification of early aspects in requirements documents and forms the foundation of domain-specific pattern identification as well as ontology construction.
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The early-AIM (early aspects identification method) approach that utilises corpus-based natural language processing (NLP) techniques to effectively enable the identification and modelling of early aspects in a semi-automated way is described.
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An information retrieval based approach for automating the detection and classification of non-functional requirements (NFRs) and then evaluates its effectiveness in an experiment based on fifteen requirements specifications developed as term projects by MS students at DePaul University.
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The design and validation of an automated NL requirements classifier for open source projects are described and initial results suggest that it can reduce the effort required to analyze requirements of open source Projects.
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The EA-Miner tool-based approach provides automated support for mining various types of concerns from a variety of early stage requirements documents and how these concepts can be structured into specific aspect-oriented requirements models (e.g., viewpoints-based, use-case-based).
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A machine learning (ML) method based on support vector machines is introduced to relate NFRs to classified "architectural concerns" in an automated way to improve the intelligence of repositories for requirements engineering.
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This paper investigates the effectiveness of Information Retrieval (IR) methods for identifying non functional requirements (NFR) and finds its capability to capture NFR in an effective manner.
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The proposed technique facilitates the identification of aspect candidates through inspection in the early phases of software development process, which can be costly and too time-consuming.
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Intelligent text analysis techniques utilized in software engineering tasks across the software life-cycle are detailed in order to analyze works focused on automatically bridging the gap between requirements and software architectures.
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