A Survey for What Developers Require in AI-powered Tools that Aid in Component Selection in CBSD
This work addresses the gap between academia and industry in CBSD by surveying professionals to inform the development of AI-powered tools for component selection, though it is incremental as it focuses on gathering requirements rather than proposing a new solution.
The authors conducted a mixed-methods survey of nearly 100 professionals to understand industry problems and needs in component selection for component-based software development (CBSD), identifying and prioritizing quality criteria and exploring perceptions of AI-driven tools.
Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.