The automotive industry is increasingly reliant on software, driven by advancements like connected and autonomous vehicles, IoT integration, and automated testing. Traditional software development models like Waterfall and V-Model have limitations, including inflexibility and the inability to accommodate changes once requirements are fixed. Agile methodologies, particularly Scrum, offer a solution to these challenges, but adoption has been hindered by factors like resistance to change and complex automotive requirements. To address this, Agile Hybrid Models, such as Waterfall-Scrum and V-Model-Scrum, have been proposed. These models combine the benefits of traditional and agile approaches, easing the transition. Results of data analyzed, supports the effectiveness of Agile framework in the automotive industry. Agile Hybrid Model, “Agile Water-V Model,” is introduced to address existing challenges. The paper concludes by addressing constraints and proposing potential avenues for future research.

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Agile and Scrum Hybrid Model for SDLC: Efficacy Analysis in Automotive Industry

  • Swastika Saxena,
  • Souvik Pal

摘要

The automotive industry is increasingly reliant on software, driven by advancements like connected and autonomous vehicles, IoT integration, and automated testing. Traditional software development models like Waterfall and V-Model have limitations, including inflexibility and the inability to accommodate changes once requirements are fixed. Agile methodologies, particularly Scrum, offer a solution to these challenges, but adoption has been hindered by factors like resistance to change and complex automotive requirements. To address this, Agile Hybrid Models, such as Waterfall-Scrum and V-Model-Scrum, have been proposed. These models combine the benefits of traditional and agile approaches, easing the transition. Results of data analyzed, supports the effectiveness of Agile framework in the automotive industry. Agile Hybrid Model, “Agile Water-V Model,” is introduced to address existing challenges. The paper concludes by addressing constraints and proposing potential avenues for future research.