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              <text>The realist approach for evaluation of computational intelligence in software engineering</text>
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              <text>Computational intelligence; Data science; Software engineering; Software requirements management; Threat modeling</text>
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              <text>Secured software development must employ a security mindset across software engineering practices. Software security must be considered during the requirements phase so that it is included throughout the development phase. Do the requirements gathering team get the proper input from the technical team? This paper unearths some of the data sources buried within software development phases and describes the potential approaches to understand them. Concepts such as machine learning and deep learning are explored to understand the data sources and explore how these learnings can be provided to the requirements gathering team. This knowledge system will help bring objectivity in the conversations between the requirements gathering team and the customer's business team. A literature review is also done to secure requirements management and identify the possible gaps in providing future research direction to enhance our understanding. Feature engineering in the landscape of software development is explored to understand the data sources. Experts offer their insight on the root cause of the lack of security focus in requirements gathering practices. The core theme is statistical modeling of all the software artifacts that hold information related to the software development life cycle. Strengthening of some traditional methods like threat modeling is also a key area explored. Subjectivity involved in these approaches can be made more objective.  2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.</text>
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              <text>Althar R.R.; Samanta D.</text>
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              <text>Innovations in Systems and Software Engineering, Vol-17, No. 1, pp. 17-27.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2021-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/s11334-020-00383-2" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s11334-020-00383-2&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099971163&amp;amp;doi=10.1007%2Fs11334-020-00383-2&amp;amp;partnerID=40&amp;amp;md5=dbde10a4b8d25faa1a3ed1efb4cba619" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099971163&amp;amp;doi=10.1007%2fs11334-020-00383-2&amp;amp;partnerID=40&amp;amp;md5=dbde10a4b8d25faa1a3ed1efb4cba619&lt;/a&gt;</text>
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              <text>ISSN: 16145046</text>
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              <text>Althar R.R., Data Science Department, CHRIST (Deemed To Be University) and Specialist-QMS, American India Private Ltd, Bangalore, First, India; Samanta D., Computer Science Department, CHRIST (Deemed To Be University), Bangalore, India</text>
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