A Survey for What Developers Require in AI-powered Tools that Aid in Component Selection in CBSD

TL;DR

This survey addresses the gap between industry practice and CBSD research by examining how practitioners select components, the tools they use, the quality criteria they prioritize, and attitudes toward AI-driven assistance. Using a mixed-methods design, the study reveals widespread reliance on ad hoc methods, a growing interest in AI-enabled tooling, and a strong emphasis on explainability, integration, and practical constraints such as privacy and cost. It identifies reliable criteria (reliability, active development, documentation, security, and cost) and desirable AI features (recommendations, alerts, and comparative analyses) while highlighting the need for human oversight and robust validation to counter AI limitations such as hallucinations. The findings inform design considerations for industry-aligned AI tools in CBSD and underscore the importance of practitioner involvement, governance, and ongoing assessment as AI support evolves in software component selection.

Abstract

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.