一句话：开发基于 LLM 的项目问答工具 SPAC-B，帮助新成员理解软件项目并制定问题解决计划。
新开发者通常在中后期阶段加入软件团队，常因软件理解困难而难以做出早期贡献。为帮助新手理解项目，作者开发了基于 LLM 的工具 SPAC-B，能够回答与具体软件项目相关的问题。研究首先开展案例研究，在两个开源项目上评估 SPAC-B 回答常见开发者问题（用于解决 issue）时的相关性、完整性和正确性：在 1–5 的 Likert 量表上，相关性均值为 4.6，完整性与正确性均为 4.3。随后作者又进行了一项有十名软件开发者参与的实验，进一步考察该工具帮助新手制定真实开源 issue 解决方案计划的能力。结果表明，参与者在 SPAC-B 的辅助下能够制定出更好的实现计划，其中 8/10 的参与者认为该工具有所帮助。
New developers commonly join software teams for projects in the advanced stages of development. They frequently face barriers, especially in software comprehension, that impede their ability to make early contributions. To aid newcomers in understanding software projects, we have developed an LLM-based tool called SPAC-B that can answer software project-specific questions. In this study, a case study is conducted to investigate the accuracy of SPAC-B's answers to common developer questions when resolving issues on two open-source projects regarding relevance, completeness, and correctness. On a Likert scale (1–5), answers from SPAC-B scored a mean of 4.6 in relevance and 4.3 in completeness and correctness. An experiment with ten software developers is performed to further examine SPAC-B's ability to help newcomers formulate plans for resolving real-life open-source issues. Results show that the participants could make a better implementation plan with the use of SPAC-B and 8/10 participants found the tool to be helpful.