Modeling The Impact Of AI On Software Development: An Automotive Case Study

Sammanfattning

As the trends of integrating artificial intelligence (AI) into software development continue to increase, assessing its impact is crucial, especially in complex, safetycritical domains such as the automotive industry. This study investigates the impact of AI in software development processes through a case study involving two realworld AI-powered solutions at Volvo Trucks: CS-testing and API-testing tools. This study proposes a structured five-phase framework to model the impact of AI from stakeholder-defined perspectives, employing a mixed-methods approach that combines quantitative and qualitative methods. These include interviews, surveys, and various process analyses. The framework focuses on evaluating factors involving quality, efficiency, automation, and stakeholder alignment to categorize and prioritize metrics and methods. The evaluation shows that AI-driven tools significantly improve two software testing processes by increasing efficiency and quality, addressing prioritized stakeholder pain points, and preserving existing strengths. Moreover, the solutions deliver measurable value and achieve a high automation level (Level 4), supported by a practical decision tree that helps developers choose suitable automation methods and highlights their direct impact. Furthermore, this study highlights how AI-driven solutions can facilitate testing workflows and support stakeholders’ decision-making processes. The results showcase a practical methodology for assessing AI’s impact and value in software development, guiding organizations in determining whether to integrate AI solutions into their processes.