Software bug reports reported on bug-tracking systems often lack crucial information for the developers to promptly resolve them, costing companies billions of dollars. There has been significant research on effectively eliciting information from bug reporters in bug tracking systems using different templates that bug reporters need to use. However, the need for asking follow-up questions persists. Recent studies propose techniques to suggest these follow-up questions to help developers obtain the missing details, but there has been little research on answering these follow up questions, which are often unanswered. In this paper, we propose a novel approach that uses CodeT5 in combination with Lucene, an information retrieval technique that leverages the relevance of different bug reports, their components, and follow-up questions to recommend answers. These top-performing answers, along with their bug report, serve as additional context apart from the deficient bug report to the deep learning model for generating an answer. We evaluate our recommended answers with the manually annotated answers using similarity metrics like Normalized Smooth BLEU Score, METEOR, Word Mover's Distance, and Semantic Similarity. We achieve a BLEU Score of up to 34 and Semantic Similarity of up to 64 which shows that the answers generated are understandable and good according to Google's standard and can outperform multiple baselines.

翻译：软件缺陷追踪系统中的Bug报告常缺乏关键信息，导致开发者无法及时修复问题，给企业造成数十亿美元损失。现有研究已通过要求报告者使用特定模板来有效获取信息。然而，后续问题的询问需求依然存在。近期研究提出了建议后续问题的技术帮助开发者获取缺失细节，但针对这些常被忽略的后续问题回答环节仍缺乏研究。本文提出一种创新方法，将CodeT5与Lucene（一种利用不同Bug报告、报告组件及后续问题相关性进行答案推荐的信息检索技术）相结合。这些高性能答案与对应的Bug报告共同作为额外上下文输入深度学习模型，生成最终回答。我们采用归一化平滑BLEU分数、METEOR、词移距离和语义相似度等指标，将推荐答案与人工标注答案进行对比评估。实验结果显示BLEU分数最高达34，语义相似度达64，表明生成的答案具有可理解性且达到谷歌标准，并优于多种基线方法。