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\section{Case Study}  \label{se:case-study}  In this section we show how \osrkit\ can be used in a production VM to support aggressive optimizations for dynamic languages. We focus on MATLAB's \feval\ construct, a widely used built-in higher-order function that applies the function passed as first parameter to the remaining arguments (e.g., {\tt feval(f,x,y)} computes {\tt f(x,y)}). This feature is heavily used by MATLAB programmers to parameterize numerical computations computations.  \ifdefined\fullver  such as iterative methods for approximate solutions of an ordinary  differential equations. equation (ODE) and simulated annealing heuristics to locate a good approximation to the global optimum of a function in a large search space.}\fi  \ifdefined\fullver  MATLAB is a popular dynamic language for scientific and numerical programming. Introduced in the late 1970s mainly as a scripting language for performing computations through efficient libraries, it has evolved over the years into a more complex programming language with support for high-level features such as functions, packages and object orientation. A popular feature of the language is the \feval\ construct, a built-in higher-order function that enables the invocation of the function specified as first argument with the remaining arguments for the \feval\ call, returning eventually the computed result. This feature is heavily used in many classes of numerical computations.   \fi  \ifdefined\fullver such as iterative methods for approximate solutions of an ordinary differential equation (ODE) and simulated annealing heuristics to locate a good approximation to the global optimum of a function in a large search space.}\fi A previous study by Lameed and Hendren~\cite{lameed2013feval} shows that the overhead of an \feval\ call is significantly high compared to higher than  a direct call, especially in JIT-based execution environments such as McVM McVM~\cite{chevalier2010mcvm}  and the proprietary MATLAB JIT accelerator by Mathworks. In fact, the presence of an \feval\ instruction can disrupt the results of intra- and inter-procedural level for type and array shape inference analyses, which are key factors for efficient code generation. Lameed and Hendren thus propose and implement in McVM two dynamic techniques for optimizing \feval\ instructions. The first one is based on OSR: using the McOSR library~\cite{lameed2013modular}, \feval\ calls inside loops are instrumented with an OSR point and profiling code to cache the last-known types for the arguments of each \feval\ instruction. When an OSR is fired at run-time, a code generator modifies the original function by inserting a guard to choose between a fast path containing a direct call and a slow path with the original \feval\ call. The second technique is less general and uses value-based JIT compilation: when the first argument of an \feval\ call is an argument of the enclosing function, the compiler replaces each call to this function in all of its callers with a call to a special dispatcher. At run-time, the dispatcher evaluates the value of the argument to use for the \feval\ and executes either a previously compiled cached code or generates and JIT-compiles a version of the function optimized for the current value.