1 Ocak 2024 Konferans bildirisi Açık Erişim

Yalciner, Aybuke; Dikici, Ahmet; Gokalp, Ebru

Dublin Core

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  <dc:creator>Yalciner, Aybuke</dc:creator>
  <dc:creator>Dikici, Ahmet</dc:creator>
  <dc:creator>Gokalp, Ebru</dc:creator>
  <dc:date>2024-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/275607</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:275607</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Data-Driven Software Engineering: A Systematic Literature Review</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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