Automating Software Development Lifecycle with Machine Learning: Enhancing Efficiency and Quality Assurance

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@article{1704597,
      author = {Manoj Bhoyar},
      title = {Automating Software Development Lifecycle with Machine Learning: Enhancing Efficiency and Quality Assurance},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {12},
      pages = {1438-1446},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704597.pdf},
      abstract = {In the current world, ML deployment in the software development lifecycle (SDLC) has been scientifically adopted to boost the SVC. This paper investigates the approaches and technologies used to automate tasks within various SDLC phases, such as requirement specification, code development, testing, and installation. Through regular predictive analytics and intelligent automation, organizations can cut down time and cost and attempt to find workarounds to many challenges. In this paper, we present the role of ML algorithms in software testing, including aspects of automated bug detection and test case generation, and mention several examples of successful applications. Moreover, issues with integrating ML into current and future processes, such as data validity, model explainability, and the teams, are discussed. In conclusion, this research points out the ability of machine learning to enhance development procedures and improve the value of software solutions, thus enabling the consequent evolution of software environments.},
      keywords = {Software Development Lifecycle (SDLC), Machine Learning (ML), Automation, Efficiency, Quality Assurance},
      month = {June},
  }