Extending Stan for Deep Probabilistic Programming

Stan is a popular declarative probabilistic programming language with a high-level syntax for expressing graphical models and beyond. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to compile any Stan model to a generative language and proves its correctness. This sheds a clearer light on the relative expressiveness of different kinds of probabilistic languages and opens the door to combining their mutual strengths. Specifically, we use our compilation scheme to build a compiler from Stan to Pyro and extend Stan with support for explicit variational inference guides and deep probabilistic models. That way, users familiar with Stan get access to new features without having to learn a fundamentally new language. Overall, our paper clarifies the relationship between declarative and generative probabilistic programming languages and is a step towards making deep probabilistic programming easier

Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging

Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer fine-grained provenance information based on the workflow provenance graph and the availability of data. To broaden the scope of our approach and demonstrate its viability, in this paper we extend it beyond procedural languages, to be used for purely declarative languages such as logic programming under the stable model semantics. For experiments and validation, we use the Answer Set Programming solver oClingo, which makes it possible to formulate and solve stream reasoning problems in a purely declarative fashion. We demonstrate how the benefits of the provenance inference over the explicit provenance still holds in a declarative setting, and we briefly discuss the potential impact for declarative programming, in particular for instance-driven debugging of the model in declarative problem solving

Human-Centric Program Synthesis

Program synthesis techniques offer significant new capabilities in searching for programs that satisfy high-level specifications. While synthesis has been thoroughly explored for input/output pair specifications (programming-by-example), this paper asks: what does program synthesis look like beyond examples? What actual issues in day-to-day development would stand to benefit the most from synthesis? How can a human-centric perspective inform the exploration of alternative specification languages for synthesis? I sketch a human-centric vision for program synthesis where programmers explore and learn languages and APIs aided by a synthesis tool

Frame-Based Editing: Easing the Transition from Blocks to Text-Based Programming

Block-based programming systems, such as Scratch or Alice, are the most popular environments for introducing young children to programming. However, mastery of text-based programming continues to be the educational goal for stu- dents who continue to program into their teenage years and beyond. Transitioning across the significant gap between the two editing styles presents a difficult challenge in school- level teaching of programming. We propose a new style of program manipulation to bridge the gap: frame-based edit- ing. Frame-based editing has the resistance to errors and approachability of block-based programming while retaining the flexibility and more conventional programming seman- tics of text-based programming languages. In this paper, we analyse the issues involved in the transition from blocks to text and argue that they can be overcome by using frame- based editing as an intermediate step. A design and imple- mentation of a frame-based editor is provided

A Theory of the Relationships between Cognitive Requirements of Computer Programming Languages and Programmers’ Cognitive Characteristics

This paper formulates a theory that investigates the possible effects of two human cognitive characteristics, on the difficulties of learning specific programming languages. The two human cognitive characteristics are Piaget’s cognitive development and McCarthy’s cognitive hemispheric style. This paper consolidates prior research and accepted cognitive theory. It then presents a formulation of a theory that relates cognitive requirements of different computer programming languages and programmers’ cognitive characteristics. If the cognitive requirements for a programming language are beyond the cognitive characteristics of a programming student, the student may burn out. If the cognitive requirements are below the student’s cognitive characteristics the student may be bored. If they are similar to them, the student is able to meet the challenges. Motivation, interest, self-esteem and success may thus be optimized. Different programming languages are more suited for different cognitive characteristics. This theory extends prior research in cognitive theory and cognitive requirements of computer programming

A small sociology of programming languages; beyond Guy Steele.

Extension of a sociology of programming languages started by Guy Steele in the introduction to Scheme and the Art of Programming (1989)