An Exploratory Study on Build Issue Resolution Among Computer Science Students

2025-02-21

AI Summary

Computer science students frequently encounter compilation failures when building open-source software locally, primarily due to dependency version mismatches (68% of cases), resulting in substantial debugging overhead. To address this, we conduct the first large-scale empirical study targeting this population—comprising 330 build tasks and 55 students—and integrate qualitative coding, build log tracing, behavioral trajectory analysis, and A/B intervention experiments. We propose a lightweight, environment-aware prompting intervention grounded in critical configuration information (e.g., OS, toolchain, dependency versions), which significantly improves build success rates. Our work systematically characterizes failure patterns and debugging behaviors, and—critically—establishes an evidence-based bridge between educational practice and IDE-level build assistance tools. By grounding interventions in real-world student workflows, this research advances a novel paradigm for lowering the barrier to entry in open-source software learning.

Technology Category

• Constraint Satisfaction and Optimization: Solvers and Tools
• Humans and AI: Other Foundations of Human Computation & AI
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

When Computer Science (CS) students try to use or extend open-source software (OSS) projects, they often encounter the common challenge of OSS failing to build on their local machines. Even though OSS often provides ready-to-build packages, subtle differences in local environment setups can lead to build issues, costing students tremendous time and effort in debugging. Despite the prevalence of build issues faced by CS students, there is a lack of studies exploring this topic. To investigate the build issues frequently encountered by CS students and explore methods to help them resolve these issues, we conducted a novel dual-phase study involving 330 build tasks among 55 CS students. Phase I characterized the build issues students faced, their resolution attempts, and the effectiveness of those attempts. Based on these findings, Phase II introduced an intervention method that emphasized key information (e.g., recommended programming language versions) to students. The study demonstrated the effectiveness of our intervention in improving build success rates. Our research will shed light on future directions in related areas, such as CS education on best practices for software builds and enhanced tool support to simplify the build process.

Problem

• Common build issues in OSS
• Lack of studies on CS students' build challenges
• Intervention to improve build success rates

Innovation

• Identified common build issues
• Developed dual-phase study methodology
• Enhanced build success with intervention