We propose requirements monitoring to aid in the maintenance of systems that reside in dynamic environments.By requirements monitoring we mean the insertion of code into a running system to gather infor/nation from which it can be determined whether, and to what degree, that running system is meeting its requirements. Monitoring is a commonly applied technique in support of perfonnance tuning, but the focus therein is primarily on computational performance requirements in short runs of systems. We wish to address systems that operate in a long lived, ongoing fashion in non-scientific, enterprise applications.We argue that the results of requirements monitoring can be ofbenejit to the designers, maintainers and users of a system -alerting them when the system is being used in an environment for which it was not designed, and giving them the information they need to direct their redesign of the system. Studies of two commercial systems are used to illustrate and justify our claims.

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Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the "local tuning" problem. Normally, predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable.This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems, STAR1 found one project where ASE were essential for minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.