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This paper describes a collection of algorithms that we developed and implemented to facilitate the automatic recovery of the modular structure of a software system from its source code. We treat automatic modularization as an optimization problem. Our algorithms make use of traditional hill-climbing and genetic algorithms.
Despite advances in software engineering, software faults continue to cause system downtime. Software faults are difficult to detect before the system fails, especially since the first symptom of a fault is often system failure itself. This paper presents a computational geometry technique and a supporting tool to tackle the problem of timely fault(More)
Parallel distributed detection schemes for M-ary hypothesis testing often assume that for each observation the local detector transmits at least log 2 M bits to a data fusion center (DFC). However, it is possible for less than log 2 M bits to be available, and in this study we consider 1-bit local detectors with M > 2. We develop conditions for asymptotic(More)
Fault-detection approaches in autonomic systems typically rely on runtime software sensors to compute metrics for CPU utilization, memory usage, network throughput, and so on. One detection approach uses data collected by the runtime sensors to construct a convex-hull geometric object whose interior represents the normal execution of the monitored(More)