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\paragraph{Impact on code quality.}  In order to measure how much a never-firing OSR point might impact code quality, we analyzed the source-code structure of each benchmark and profiled its run-time behavior to identify performance-critical sections for OSR point insertion.  For iterative benchmarks, we insert an OSR point in the body of their hottest loops. We classify a loop as hottest when its body is executed for a very high cumulative number of iterations (e.g., from a few thousands up to billions) and it either calls the method with the highest {\em self} time in the program, or it performs the most computational-intensive operations for the program in its own body. These loops are natural candidates for OSR point insertion, as they can be used - as in insertion: for instance,  the Jikes RVM - to enable more dynamic inlining opportunities, with the benefits from several control-flow (e.g., dead code elimination) and data-flow (e.g., constant propagation) optimizations based inserts yield points  on the run-time values backward branches to trigger operations such as OSR-based recompilation  of the live variables. a method and thread preemption for garbage collection.  In the \shootout\ benchmarks, the number of such loops is typically 1 (2 for {\tt spectral-norm}). For {\tt b-trees} - the only benchmark showing a recursive pattern - we insert an OSR point in the body of the method that accounts for the largest {\em self} execution time of the program. Such an OSR point might be useful to trigger recompilation of the code at a higher degree of optimization, or to enable some form of dynamic optimization (for instance, in a recursive search algorithm we might want to inline the comparator method provided by the user at the call).