An Integrative Human-Centered Architecture for Interactive Programming Assistants

Programming has become a collaboration between human programmers, who drive intent, and interactive assistants that suggest contextually relevant editor actions. There has been considerable work on suggestion synthesis strategies—from semantic autocomplete to modern program synthesis, repair, and machine learning research. This diversity of contextually viable strategies creates a need for an integrative, human-centered perspective on the problem of programming assistant design that (1) confronts the problem of integrating a variety of synthesis strategies, fed by shared semantic analyses capable of operating on program sketches, and (2) centers the needs of the human programmer: comprehending, comparing, ranking, and filtering suggestions generated by various synthesizers, and in some cases participating in a synthesizer’s search by supplying additional expressions of intent. This paper contributes a conceptual architecture and API to guide programming assistant designers as they confront these integration and human-centered design challenges. We then instantiate this architecture with two prototype end-to-end assistant designs, both developed for the Hazel programming environment, that emphasize understudied design aspects, namely continuity, explainability, human-in-the-loop synthesis, and the integration of multiple analyses with multiple synthesis strategies.