Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge

Shakeri Hossein Abad, Z; Gervasi, V; Zowghi, D; Far, BH

Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge

Shakeri Hossein Abad, Z Gervasi, V Zowghi, D

Far, BH

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Software Engineering, 2019, 2019-May pp. 442 - 453
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Shakeri Hossein Abad, Z	en_US
dc.contributor.author	Gervasi, V	en_US
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155	en_US
dc.contributor.author	Far, BH	en_US
dc.date.available	2021-08-12T19:00:55Z
dc.date.issued	2019-05-01	en_US
dc.identifier.citation	Proceedings - International Conference on Software Engineering, 2019, 2019-May pp. 442 - 453	en_US
dc.identifier.isbn	9781728108698	en_US
dc.identifier.issn	0270-5257	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135837
dc.description.abstract	© 2019 IEEE. In many software development projects, analysts are required to deal with systems' requirements from unfamiliar domains. Familiarity with the domain is necessary in order to get full leverage from interaction with stakeholders and for extracting relevant information from the existing project documents. Accurate and timely extraction and classification of requirements knowledge support analysts in this challenging scenario. Our approach is to mine real-time interaction records and project documents for the relevant phrasal units about the requirements related topics being discussed during elicitation. We propose to use both generative and discriminating methods. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modelling of natural language processing tasks. We used an extended version of Support Vector Machines (SVMs) with variable-sized feature vectors to efficiently and dynamically extract and classify requirements-related knowledge from the existing documents. To evaluate the performance of our approach intuitively and quantitatively, we used edit distance and precision/recall metrics. We show in three case studies that the snippets extracted by our method are intuitively relevant and reasonably accurate. Furthermore, we found that statistical and linguistic parameters such as smoothing methods, and words contiguity and order features can impact the performance of both extraction and classification tasks.	en_US
dc.relation.ispartof	Proceedings - International Conference on Software Engineering	en_US
dc.relation.isbasedon	10.1109/ICSE.2019.00057	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-May	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Graduate Research School
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-May	en_US

Abstract:

© 2019 IEEE. In many software development projects, analysts are required to deal with systems' requirements from unfamiliar domains. Familiarity with the domain is necessary in order to get full leverage from interaction with stakeholders and for extracting relevant information from the existing project documents. Accurate and timely extraction and classification of requirements knowledge support analysts in this challenging scenario. Our approach is to mine real-time interaction records and project documents for the relevant phrasal units about the requirements related topics being discussed during elicitation. We propose to use both generative and discriminating methods. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modelling of natural language processing tasks. We used an extended version of Support Vector Machines (SVMs) with variable-sized feature vectors to efficiently and dynamically extract and classify requirements-related knowledge from the existing documents. To evaluate the performance of our approach intuitively and quantitatively, we used edit distance and precision/recall metrics. We show in three case studies that the snippets extracted by our method are intuitively relevant and reasonably accurate. Furthermore, we found that statistical and linguistic parameters such as smoothing methods, and words contiguity and order features can impact the performance of both extraction and classification tasks.

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http://hdl.handle.net/10453/135837