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

2025-04-18

AI Summary

The component-based software development (CBSD) community has long lacked industry-accepted methods and tools for component selection. Method: We employed a mixed-methods approach—surveying and conducting semi-structured interviews with 98 practitioners and researchers—to systematically identify industrial pain points, current practices, and quality evaluation priorities. Contribution/Results: This study is the first to articulate, from an industrial perspective, three core requirement categories for AI-powered component selection tools: functionality, quality attributes (e.g., explainability, reliability, integrability), and AI trustworthiness—and to empirically quantify their acceptance thresholds. It delivers an industry-prioritized ranking of component selection quality criteria, bridging the academia–industry gap via co-prioritization. These findings establish a rigorous empirical foundation for designing and evaluating next-generation AI-assisted CBSD tools.

Technology Category

• Constraint Satisfaction and Optimization: Solvers and Tools
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Lack of standard AI tools for component selection in CBSD
• Industry-academia gap in component selection practices
• Prioritizing quality criteria for AI-driven component selection

Innovation

• Mixed methods survey of 100 professionals
• Identify quality criteria for component selection
• Explore AI-driven tools for CBSD