Research Mission To enable domain experts to write the code they need without sacrificing their autonomy.
Hi there! My name is Justin. I love programming languages, human-computer interaction, biology, social justice, music, art, and math—and I’m always happy to grow this list!
I am a PhD candidate in computer science advised by Sarah E. Chasins at UC Berkeley. Previously, I worked with Ravi Chugh as an undergrad at UChicago. I am very grateful for their excellent mentorship and strive to pay it forward!
Research
I co-design programming systems with domain experts. These systems empower them to write the code they need with autonomy.
To make new user interactions possible in these systems, I develop programming language theory informed by what I learn from
- deeply embedding with domain experts and
- my qualitative and quantitative human-computer interaction research.
Currently, I am immersed in the world of biology to foster a substantive, ongoing, and reciprocal relationship between the fields of programming languages and experimental biology.
I have been collaborating most closely with the lovely folks of the Nuñez Lab both to advance research in biology and to answer the following question with them:
What would a programming system look like that empowers experimental biologists without much programming experience to produce the code they need by themselves?
Conference and Journal Publications
For more, please see my CV or Google Scholar! (∗ = equal contribution, † = research mentee)
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PLDI ’24
Equivalence by Canonicalization for Synthesis-Backed Refactoring
Abstract
We present an enumerative program synthesis framework called component-based refactoring that can refactor “direct” style code that does not use library components into equivalent “combinator” style code that does use library components. This framework introduces a sound but incomplete technique to check the equivalence of direct code and combinator code called equivalence by canonicalization that does not rely on input-output examples or logical specifications. Moreover, our approach can repurpose existing compiler optimizations, leveraging decades of research from the programming languages community. We instantiated our new synthesis framework in two contexts: (i) higher-order functional combinators such as
mapandfilterin the statically-typed functional programming language Elm and (ii) high-performance numerical computing combinators provided by the NumPy library for Python. We implemented both instantiations in a tool called Cobbler and evaluated it on thousands of real programs to test the performance of the component-based refactoring framework in terms of execution time and output quality. Our work offers evidence that synthesis-backed refactoring can apply across a range of domains without specification beyond the input program. -
UIST ’22
Exploring the Learnability of Program Synthesizers by Novice Programmers
Abstract
Modern program synthesizers are increasingly delivering on their promise of lightening the burden of programming by automatically generating code, but little research has addressed how we can make such systems learnable to all. In this work, we ask: What aspects of program synthesizers contribute to and detract from their learnability by novice programmers? We conducted a thematic analysis of 22 observations of novice programmers, during which novices worked with existing program synthesizers, then participated in semi-structured interviews. Our findings shed light on how their specific points in the synthesizer design space affect these tools’ learnability by novice programmers, including the type of specification the synthesizer requires, the method of invoking synthesis and receiving feedback, and the size of the specification. We also describe common misconceptions about what constitutes meaningful progress and useful specifications for the synthesizers, as well as participants’ common behaviors and strategies for using these tools. From this analysis, we offer a set of design opportunities to inform the design of future program synthesizers that strive to be learnable by novice programmers. This work serves as a first step toward understanding how we can make program synthesizers more learnable by novices, which opens up the possibility of using program synthesizers in educational settings as well as developer tooling oriented toward novice programmers.
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OOPSLA ’21
How Statically-Typed Functional Programmers Write Code
Abstract
How working statically-typed functional programmers write code is largely understudied. And yet, a better understanding of developer practices could pave the way for the design of more useful and usable tooling, more ergonomic languages, and more effective on-ramps into programming communities. The goal of this work is to address this knowledge gap: to better understand the high-level authoring patterns that statically-typed functional programmers employ. We conducted a grounded theory analysis of 30 programming sessions of practicing statically-typed functional programmers, 15 of which also included a semi-structured interview. The theory we developed gives insight into how the specific affordances of statically-typed functional programming affect domain modeling, type construction, focusing techniques, exploratory and reasoning strategies, and expressions of intent. We conducted a set of quantitative lab experiments to validate our findings, including that statically-typed functional programmers often iterate between editing types and expressions, that they often run their compiler on code even when they know it will not successfully compile, and that they make textual program edits that reliably signal future edits that they intend to make. Lastly, we outline the implications of our findings for language and tool design. The success of this approach in revealing program authorship patterns suggests that the same methodology could be used to study other understudied programmer populations.
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ICFP ’20
Program Sketching with Live Bidirectional Evaluation
Abstract
We present a system called Smyth for program sketching in a typed functional language whereby the concrete evaluation of ordinary assertions gives rise to input-output examples, which are then used to guide the search to complete the holes. The key innovation, called live bidirectional evaluation, propagates examples "backward" through partially evaluated sketches. Live bidirectional evaluation enables Smyth to (a) synthesize recursive functions without trace-complete sets of examples and (b) specify and solve interdependent synthesis goals. Eliminating the trace-completeness requirement resolves a significant limitation faced by prior synthesis techniques when given partial specifications in the form of input-output examples.
To assess the practical implications of our techniques, we ran several experiments on benchmarks used to evaluate Myth, a state-of-the-art example-based synthesis tool. First, given expert examples (and no partial implementations), we find that Smyth requires on average 66% of the number of expert examples required by Myth. Second, we find that Smyth is robust to randomly-generated examples, synthesizing many tasks with relatively few more random examples than those provided by an expert. Third, we create a suite of small sketching tasks by systematically employing a simple sketching strategy to the Myth benchmarks; we find that user-provided sketches in Smyth often further reduce the total specification burden (i.e. the combination of partial implementations and examples). Lastly, we find that Leon and Synquid, two state-of-the-art logic-based synthesis tools, fail to complete several tasks on which Smyth succeeds.
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UIST ’19
Sketch-n-Sketch: Output-Directed Programming for SVG
Abstract
For creative tasks, programmers face a choice: Use a GUI and sacrifice flexibility, or write code and sacrifice ergonomics?
To obtain both flexibility and ease of use, a number of systems have explored a workflow that we call output-directed programming. In this paradigm, direct manipulation of the program's graphical output corresponds to writing code in a general-purpose programming language, and edits not possible with the mouse can still be enacted through ordinary text edits to the program. Such capabilities provide hope for integrating graphical user interfaces into what are currently text-centric programming environments.
To further advance this vision, we present a variety of new output-directed techniques that extend the expressive power of Sketch-n-Sketch, an output-directed programming system for creating programs that generate vector graphics. To enable output-directed interaction at more stages of program construction, we expose intermediate execution products for manipulation and we present a mechanism for contextual drawing. Looking forward to output-directed programming beyond vector graphics, we also offer generic refactorings through the GUI, and our techniques employ a domain-agnostic provenance tracing scheme.
To demonstrate the improved expressiveness, we implement a dozen new parametric designs in Sketch-n-Sketch without text-based edits. Among these is the first demonstration of building a recursive function in an output-directed programming setting.
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ICSE ’18
Deuce: A Lightweight User Interface for Structured Editing
Abstract
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.