Prognostics and Health Management (PHM) is a discipline focused on predicting the point at which systems or components will cease to perform as intended, typically measured as Remaining Useful Life (RUL). RUL serves as a vital decision-making tool for contingency planning, guiding the timing and nature of system maintenance. Historically, PHM has primarily been applied to hardware systems, with its application to software only recently explored. In a recent study we introduced a methodology and demonstrated how changes in software can impact the RUL of software. However, in practical software development, real-time performance is also influenced by various environmental attributes, including operating systems, clock speed, processor performance, RAM, machine core count and others. This research extends the analysis to assess how changes in environmental attributes, such as operating system and clock speed, affect RUL estimation in software. Findings are rigorously validated using real performance data from controlled test beds and compared with predictive model-generated data. Statistical validation, including regression analysis, supports the credibility of the results. The controlled test bed environment replicates and validates faults from real applications, ensuring a standardized assessment platform. This exploration yields actionable knowledge for software maintenance and optimization strategies, addressing a significant gap in the field of software health management.

翻译：故障预测与健康管理(PHM)是一门聚焦于预测系统或组件何时会停止按预期运行的学科，其核心指标为剩余使用寿命(RUL)。RUL为应急规划提供了关键的决策工具，指导系统维护的时机与性质。历史上，PHM主要应用于硬件系统，其软件领域的应用直至近期才被探索。在最近的研究中，我们提出了一种方法论，并论证了软件变更如何影响软件的RUL。然而，在实际软件开发中，实时性能还受到多种环境属性的影响，包括操作系统、时钟频率、处理器性能、随机存取存储器(RAM)、机器核心数等。本研究扩展了分析范围，旨在评估操作系统和时钟频率等环境属性的变化如何影响软件的RUL估计。研究结果通过受控测试平台的实际性能数据进行了严格验证，并与预测模型生成的数据进行了对比。包括回归分析在内的统计验证支持了结果的可信度。受控测试平台环境能够复现并验证真实应用中的故障，确保了标准化的评估平台。这项探索为软件维护与优化策略提供了可操作的知识，填补了软件健康管理领域的重要空白。