  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.
