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            <name>Title</name>
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                <text>Book Chapter</text>
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    <name>Book Chapter</name>
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          <name>Title</name>
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              <text>Ensemble Model of Machine Learning for Integrating Risk in Software Effort Estimation</text>
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          <name>Subject</name>
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              <text>Actual effort; Effort driver; Estimated effort; Predictive model; Risk score</text>
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              <text>The development of software involves expending a significant quantum of time, effort, cost, and other resources, and effort estimation is an important aspect. Though there are many software estimation models, risks are not adequately considered in the estimation process leading to wide gap between the estimated and actual efforts. Higher the level of accuracy of estimated effort, better would be the compliance of the software project in terms of completion within the budget and schedule. This study has been undertaken to integrate risk in effort estimation process so as to minimize the gap between the estimated and the actual efforts. This is achieved through consideration of risk score as an effort driver in the computation of effort estimates and formulating a machine learning model. It has been identified that risk score reveals feature importance and the predictive model with integration of risk score in the effort estimates indicated an enhanced fit.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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          <name>Creator</name>
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              <text>Natarajan R.; Balachandran K.</text>
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              <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 635-644.</text>
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          <name>Publisher</name>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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          <name>Date</name>
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              <text>2022-01-01</text>
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          <name>Identifier</name>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-16-9416-5_46" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-16-9416-5_46&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133653124&amp;amp;doi=10.1007%2F978-981-16-9416-5_46&amp;amp;partnerID=40&amp;amp;md5=91f0125cf453035f04d6104ce6a84da8" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133653124&amp;amp;doi=10.1007%2f978-981-16-9416-5_46&amp;amp;partnerID=40&amp;amp;md5=91f0125cf453035f04d6104ce6a84da8&lt;/a&gt;</text>
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          <name>Rights</name>
          <description>Information about rights held in and over the resource</description>
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            <elementText elementTextId="157075">
              <text>Restricted Access</text>
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          <name>Relation</name>
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              <text>ISSN: 23674512</text>
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          <name>Format</name>
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              <text>Online</text>
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          <name>Language</name>
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              <text>English</text>
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          <name>Type</name>
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              <text>Book chapter</text>
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              <text>Natarajan R., School of Business and Management, CHRIST (Deemed To Be University), Bangalore, India; Balachandran K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed To Be University), Bangalore, India</text>
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