International audienceA proxy object is a surrogate or placeholder that controls access to another target object. Proxy objects are a widely used solution for different scenarios such as remote method invocation, future objects, behavioral reflection, object databases, inter-languages communications and bindings, access control, lazy or parallel evaluation, security, among others. Most proxy implementations support proxies for regular objects but are unable to create proxies for objects with an important role in the runtime infrastructure such as classes or methods. Proxies can be complex to install, they can have a significant overhead, they can be limited to certain kind of classes, etc. Moreover, proxy implementations are often not stratified and they do not have a clear separation between proxies (the objects intercepting messages) and handlers (the objects handling interceptions). In this paper, we present Ghost: a uniform and general-purpose proxy implementation for the Pharo programming language. Ghost provides low memory consuming proxies for regular objects as well as for classes and methods. When a proxy takes the place of a class, it intercepts both the messages received by the class and the lookup of methods for messages received by its instances. Similarly, if a proxy takes the place of a method, then the method execution is intercepted too

Context. An extension method is a method declared in a package other than the package of its host class. Thanks to extension methods, developers can adapt to their needs classes they do not own: adding methods to core classes is a typical use case. This is particularly useful for adapting software and therefore to increase reusability. Inquiry. In most dynamically-typed languages, extension methods are globally visible. Because any developer can define extension methods for any class, naming conflicts ocur: if two developers define an extension method with the same signature in the same class, only one extension method is visible and overwrites the other. Similarly, if two developers each define an extension method with the same name in a class hierarchy, one overrides the other. To avoid such "accidental overrides", some dynamically-typed languages limit the visibility of an extension method to a particular scope. However, their semantics have not been fully described and compared. In addition, these solutions typically rely on a dedicated and slow method lookup algorithm to resolve conflicts at runtime. Approach. In this article, we present a formalization of the underlying models of Ruby refinements, Groovy categories, Classboxes, and Method Shelters that are scoping extension method solutions in dynamicallytyped languages. Knowledge. Our formal framework allows us to objectively compare and analyze the shortcomings of the studied solutions and other different approaches such as MultiJava. In addition, language designers can use our formal framework to determine which mechanism has less risk of "accidental overrides". Grounding. Our comparison and analysis of existing solutions is grounded because it is based on denotational semantics formalizations. Importance. Extension methods are widely used in programming languages that support them, especially dynamically-typed languages such as Pharo, Ruby or Python. However, without a carefully designed mechanism, this feature can cause insidious hidden bugs or can be voluntarily used to gain access to protected operations, violate encapsulation or break fundamental invariants. ACM CCS Software and its engineering → Modules / packages;Theory of computation → Formalisms;

Abstract. Scoping behavioral variations to dynamic extents is useful to support non-functional concerns that otherwise result in cross-cutting code. Unfortunately, such forms of scoping are difficult to obtain with traditional reflection or aspects. We propose delegation proxies, a dynamic proxy model that supports behavioral intercession through the interception of various interpretation operations. Delegation proxies permit different behavioral variations to be easily composed together. We show how delegation proxies enable behavioral variations that can propagate to dynamic extents. We demonstrate our approach with examples of behavioral variations scoped to dynamic extents that help simplify code related to safety, reliability, and monitoring.