Multicore and manycore computers are the norm nowadays, and users have expectations that their programs can do multiple things concurrently. To support that, developers use concur- rency abstractions such as threads, promises, futures, and/or channels to exchange information. All these abstractions introduce trade-offs between the concurrency model and the language guarantees, and developers accept these trade-offs for the benefits of concurrent programming.

Many concurrent languages are multi-paradigm, e.g., mix the functional and object-oriented paradigms. This is beneficial to developers because they can choose the most suitable approach when solving a problem. From the point of view of concurrency, purely functional programming languages are data-race free since they only support immutable data. Object-oriented languages do not get a free lunch, and neither do multi-paradigm languages that have imperative features.

The main problem is uncontrolled concurrent access to shared mutable state, which may inadvertently introduce data-races. A data-race happens when two concurrent memory operations target the same location, at least one of them is a write, and there is no synchronisation operation involved. Data-races make programs to exhibit (unwanted) non-deterministic behaviour.

The contribution of this thesis is two-fold. First, this thesis introduces new concurrent abstractions in a purely functional, statically typed programming language (Paper I – Paper III); these abstractions allow developers to write concurrent control- and delegation-based patterns. Second, this thesis introduces a capability-based dynamic programming model, named Dala, that extends the applicability of the concurrent abstractions to an imperative setting while maintaining data-race freedom (Paper IV). Developers can also use the Dala model to migrate unsafe programs, i.e., programs that may suffer data-races, to data-race free programs.