5 research outputs found
Toward Semantic Foundations for Program Editors
Programming language definitions assign formal meaning to complete programs. Programmers, however, spend a substantial amount of time interacting with incomplete programs - programs with holes, type inconsistencies and binding inconsistencies - using tools like program editors and live programming environments (which interleave editing and evaluation). Semanticists have done comparatively little to formally characterize (1) the static and dynamic semantics of incomplete programs; (2) the actions available to programmers as they edit and inspect incomplete programs; and (3) the behavior of editor services that suggest likely edit actions to the programmer based on semantic information extracted from the incomplete program being edited, and from programs that the system has encountered in the past.
This paper serves as a vision statement for a research program that seeks to develop these "missing" semantic foundations. Our hope is that these contributions, which will take the form of a series of simple formal calculi equipped with a tractable metatheory, will guide the design of a variety of current and future interactive programming tools, much as various lambda calculi have guided modern language designs. Our own research will apply these principles in the design of Hazel, an experimental live lab notebook programming environment designed for data science tasks. We plan to co-design the Hazel language with the editor so that we can explore concepts such as edit-time semantic conflict resolution mechanisms and mechanisms that allow library providers to install library-specific editor services
Deuce: A Lightweight User Interface for Structured Editing
We present a structure-aware code editor, called Deuce, that is equipped with
direct manipulation capabilities for invoking automated program
transformations. Compared to traditional refactoring environments, Deuce
employs a direct manipulation interface that is tightly integrated within a
text-based editing workflow. In particular, Deuce draws (i) clickable widgets
atop the source code that allow the user to structurally select the
unstructured text for subexpressions and other relevant features, and (ii) a
lightweight, interactive menu of potential transformations based on the current
selections. We implement and evaluate our design with mostly standard
transformations in the context of a small functional programming language. A
controlled user study with 21 participants demonstrates that structural
selection is preferred to a more traditional text-selection interface and may
be faster overall once users gain experience with the tool. These results
accord with Deuce's aim to provide human-friendly structural interactions on
top of familiar text-based editing.Comment: ICSE 2018 Paper + Supplementary Appendice
A Survey of Machine Learning for Big Code and Naturalness
Research at the intersection of machine learning, programming languages, and
software engineering has recently taken important steps in proposing learnable
probabilistic models of source code that exploit code's abundance of patterns.
In this article, we survey this work. We contrast programming languages against
natural languages and discuss how these similarities and differences drive the
design of probabilistic models. We present a taxonomy based on the underlying
design principles of each model and use it to navigate the literature. Then, we
review how researchers have adapted these models to application areas and
discuss cross-cutting and application-specific challenges and opportunities.Comment: Website accompanying this survey paper can be found at
https://ml4code.github.i