Abstract: Functional Reactive Programming (FRP) is an approach to reactive programming where systems are structured as networks of functions operating on signals. FRP is based on the synchronous data-flow paradigm and supports both continuous-time and discrete-time signals (hybrid systems). What sets FRP apart from most other languages for similar applications is its support for systems with dynamic structure and for higher-order data-flow constructs. This raises a range of implementation challenges. This paper contributes towards advancing the state of the art of FRP implementation by studying the notion of signal change and change propagation in a setting of hybrid signal function networks with dynamic structure. To sidestep some problems of certain previous FRP implementations that are structured using arrows, we suggest working with a notion of composable, multi-input and multi-output signal functions. A clear conceptual distinction is also made between continuous-time and discrete-time signals. We then show how establishing change-related properties of the signal functions in a network allows such networks to be simplified (static optimisation) and can help reducing the amount of computation needed for executing the networks (dynamic optimisation). Interestingly, distinguishing between continuous-time and discrete-time signals allows us to characterise the change-related properties of signal functions more precisely than what we otherwise would have been able to, which is helpful for optimisation.

Functional Reactive Programming (FRP) is an approach to reactive programming where systems are structured as networks of functions operating on time-varying values (signals). FRP is based on the synchronous data-flow paradigm and supports both continuous-time and discretetime signals (hybrid systems). What sets FRP apart from most other reactive languages is its support for systems with highly dynamic structure (dynamism) and higher-order reactive constructs (higher-order data-flow). However, the price paid for these features has been the loss of the safety and performance guarantees provided by other, less expressive, reactive languages. Statically guaranteeing safety properties of programs is an attractive proposition. This is true in particular for typical application domains for reactive programming such as embedded systems. To that end, many existing reactive languages have type systems or other static checksthatguaranteedomain-specificconstraints, suchasfeedbackbeingwell-formed(causality analysis). However, comparedwithFRP,theyarelimitedintheircapacitytosupportdynamism andhigher-orderdata-flow. Ontheotherhand, asestablishedstatictechniquesdonotsufficefor highly structurally dynamic systems, FRP generally enforces few domain-specific constraints, leaving the FRP programmer to manually check that the constraints are respected. Thus, there

Programming interactive systems by means of the observer pattern is hard and error-prone yet is still the implementation standard in many production environments. We present an approach to gradually deprecate observers in favor of reactive programming abstractions. Several library layers help programmers to smoothly migrate existing code from callbacks to a more declarative programming model. Our central high-level API layer embeds an extensible higher-order data-flow DSL into our host language. This embedding is enabled by a continuation passing style transformation.

This report describes an implementation of a functional reactive programming extension to Java implemented using the feature-oriented Jakarta Tool Suite. I explore the connection between feature-oriented programming and domain specific languages by demonstrating how this domain-specific language is both implemented and used in a feature-oriented system. 1