1 Ocak 2024 Konferans bildirisi Açık Erişim

Yalciner, Aybuke; Dikici, Ahmet; Gokalp, Ebru

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/275607</identifier>
  <creators>
    <creator>
      <creatorName>Yalciner, Aybuke</creatorName>
      <givenName>Aybuke</givenName>
      <familyName>Yalciner</familyName>
    </creator>
    <creator>
      <creatorName>Dikici, Ahmet</creatorName>
      <givenName>Ahmet</givenName>
      <familyName>Dikici</familyName>
    </creator>
    <creator>
      <creatorName>Gokalp, Ebru</creatorName>
      <givenName>Ebru</givenName>
      <familyName>Gokalp</familyName>
      <affiliation>Hacettepe Univ, Dept Comp Sci, Ankara, Turkiye</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Data-Driven Software Engineering: A Systematic Literature Review</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2024</publicationYear>
  <dates>
    <date dateType="Issued">2024-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/275607</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-031-71139-8_2</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Over the past few years, emerging technologies have had a significant impact on the processes of software engineering (SE). Consequently, there has been a shift from a more experience-based approach to a data-driven decision-making approach. This shift to data-driven decision-making has resulted in more reliable and accurate decision-making, ultimately leading to more efficient and effective SE processes and a reduction in rework. Our study involved a comprehensive systematic literature review(SLR) examining the utilization of data-driven approaches in SE processes over the last decade. Our analysis of 34 primary studies revealed that data-driven approaches are most commonly utilized. After analyzing the primary studies, we found that data-driven-methods are commonly employed in SE processes for software management and software testing. Researchers are delving into subfields of artificial intelligence, including machine learning and deep learning, to devise decision-making models for SE processes that have undergone extensive validation. We aim to provide valuable insights into the usage of data-driven approaches in SE by conducting a systematic mapping based on the studies that we have found.&lt;/p&gt;</description>
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