Explanations are essential in software engineering (SE) and requirements communication, helping stakeholders clarify ambiguities, justify design choices, and build shared understanding. Online Q&A forums such as Stack Overflow provide large-scale settings where such explanations are produced and evaluated, offering valuable insights into what makes them effective. While prior work has explored answer acceptance and voting behavior, little is known about which specific features make explanations genuinely useful. The relative influence of structural, contextual, and linguistic factors, such as content richness, timing, and sentiment, remains unclear. We analyzed 3,323 questions and 59,398 answers from Stack Overflow, combining text analysis and statistical modeling to examine how explanation attributes relate to perceived usefulness (normalized upvotes). Structural and contextual factors, especially explanation length, code inclusion, timing, and author reputation, show small to moderate positive effects. Sentiment polarity has negligible influence, suggesting that clarity and substance outweigh tone in technical communication. This study provides an empirical account of what drives perceived usefulness in developer explanations. It contributes methodological transparency through open data and replication materials, and conceptual insight by relating observed communication patterns to principles of requirements communication. The findings offer evidence-based implications for how developers and RE practitioners can craft clearer and more effective explanations, potentially supporting fairer communication in both open and organizational contexts. From an RE perspective, these determinants can be interpreted as practical signals for ambiguity reduction and rationale articulation in day-to-day requirements communication.

翻译：解释在软件工程（SE）和需求沟通中至关重要，有助于利益相关者澄清歧义、论证设计选择并建立共识。Stack Overflow等在线问答论坛为此类解释的产生与评估提供了大规模场景，为理解其有效性提供了宝贵洞见。尽管已有研究探讨了答案采纳与投票行为，但对于哪些具体特征使解释真正有用仍知之甚少。结构、语境和语言因素（如内容丰富度、时效性和情感倾向）的相对影响尚不明确。我们分析了Stack Overflow的3,323个问题与59,398条答案，结合文本分析与统计建模，探究解释属性如何关联感知有用性（标准化赞同数）。结构性和语境因素——特别是解释长度、代码包含、时效性和作者声誉——呈现轻微至中等程度的正向影响。情感极性影响可忽略，表明技术沟通中清晰度与实质内容比语气更重要。本研究通过实证揭示了开发者解释中感知有用性的驱动因素：通过公开数据与复现材料贡献方法透明度，并通过将观察到的沟通模式与需求沟通原则相关联提供概念洞见。研究结果为开发者和需求工程实践者如何构建更清晰有效的解释提供了实证依据，可能支持开放环境与组织语境中更公平的沟通。从需求工程视角看，这些决定因素可解读为日常需求沟通中降低歧义与阐明理据的实践信号。