The continuous shift of various industries towards internet-based services have caused an exponential growth in the amount of data produced over the past few years. On top of this, the increasing need for real-time analytics and the increase in data velocity have made asynchronous, event-driven applications the norm. In this context, the reactive programming paradigm has gained much traction as it focuses on the propagation of change and composing/transforming streams of data. The industry standard reactive programming library for the JVM, .NET and Javascript ecosystems is the Reactive Extensions (Rx) library.
However, despite being well equipped to deal with asynchronous data, it does not offer any way of scaling the computation on multiple machines. In this thesis, we attempt to lay the groundwork for a scalable Rx library by implementing infrastructure and operators for remote execution of Rx streams.

Debuggers are crucial tools for developers to support the process of developing software systems as they provide direct insights into the execution of their code. As software development in the industry is moving towards technology stacks that operate on increasingly higher levels of abstraction, debugging tools have not evolved as quickly. This creates an abstraction gap between the concrete debugging needs of developers and the support that debugging tools offer. To reduce this gap, we propose a generic framework for reactive scriptable debugging. We propose a design and reference implementation of such a scriptable debugging system. Using this framework we aim to liberate developers from rigid debugging tools and give the power of debugging back to the developers.

The aviation industry is vastly growing, as travelling by air is more common today than it ever was. However due too inefficiency and lack of communication of accurate flight information between airports, congestion and delays are occurring on a daily basis. While Collaborative Decision Making (CDM) is developed by Euro control to address this issue, the problem of transmitting accurate flight information near real time is not yet solved. Adecs Airinfra did a first attempt at automatic landing and departure registration by a fixed rule based algorithm to address this issue. However, this algorithm has limitations that cannot be solved with tweaking and tuning. In this work, we aim to create a replacement based on machine learning models. In this thesis we present the complete process, starting from raw real world data, turning this into
labelled data up to the point where we define a validation method and present the final results. We managed to create a machine learning landing / departure detection system with up to 99% precision and recall for arrivals, and for departures we managed to get a precision of 94% against 98% recall.

Reactive Programming is a way of programming designed to provide developers with the right abstractions for creating systems that use streams of data. Traditional debug tools lack support for the abstractions provided, causing developers to fallback to the most rudimentary debug tool available: printf-debugging. In this work, we design a visualization and debugging tool for Reactive Programming, that aids comprehension and debugging of reactive systems, by visualizing the dependencies and structure of the data flow, and the data inside the flow. We present RxFiddle, a platform for the visualization as well as the required instrumentation for RxJS in the ReactiveX-family of Reactive Programming libraries. Evaluation based on an experiment with 111 subjects, shows that RxFiddle can outperform traditional debugging in terms of debug time required.