1 Ocak 2024 Konferans bildirisi Açık Erişim

Yalciner, Aybuke; Dikici, Ahmet; Gokalp, Ebru

Citation Style Language JSON

{
  "DOI": "10.1007/978-3-031-71139-8_2", 
  "abstract": "<p>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.</p>", 
  "author": [
    {
      "family": "Yalciner", 
      "given": " Aybuke"
    }, 
    {
      "family": "Dikici", 
      "given": " Ahmet"
    }, 
    {
      "family": "Gokalp", 
      "given": " Ebru"
    }
  ], 
  "id": "275607", 
  "issued": {
    "date-parts": [
      [
        2024, 
        1, 
        1
      ]
    ]
  }, 
  "title": "Data-Driven Software Engineering: A Systematic Literature Review", 
  "type": "paper-conference"
}