Write, Execute, Assess: Program Synthesis with a REPL (1906.04604v1)

Abstract: We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs. We equip the search process with an interpreter or a read-eval-print-loop (REPL), which immediately executes partially written programs, exposing their semantics. The REPL addresses a basic challenge of program synthesis: tiny changes in syntax can lead to huge changes in semantics. We train a pair of models, a policy that proposes the new piece of code to write, and a value function that assesses the prospects of the code written so-far. At test time we can combine these models with a Sequential Monte Carlo algorithm. We apply our approach to two domains: synthesizing text editing programs and inferring 2D and 3D graphics programs.

Overview of "Write, Execute, Assess: Program Synthesis with a REPL"

The paper "Write, Execute, Assess: Program Synthesis with a REPL" presents an innovative approach to program synthesis by integrating a Read-Eval-Print Loop (REPL) into the synthesis process. The paper's focus is on utilizing a neural framework equipped with REPL for writing, executing, and assessing programs—a methodology inspired by how human programmers typically develop code through iterative trial and error. The REPL serves to immediately execute partially written code, thereby exposing the semantic information that results from tiny syntactic changes. This approach addresses the complex challenge of mapping syntax to semantics within program synthesis.

Core Contributions

The key contributions of the paper are as follows:

1. Integration of REPL in Program Synthesis: The paper proposes a model using a REPL to continually execute partially-written programs, allowing the synthesis to operate in the semantic space rather than the syntactic space. This methodology helps manage the intricate task of translating syntactic changes into their corresponding semantic effects.
2. Policy and Value Functions: The authors introduce two neural components: a policy that suggests the next code segment to write and a value function that evaluates the likelihood of the partially written code achieving the desired functionality. These components are integrated with a Sequential Monte Carlo (SMC) algorithm for efficient synthesis.
3. Application in Text Editing and Graphics Domains: The framework is applied and validated in two separate domains: synthesizing text editing programs and inferring 2D and 3D graphics programs. The results show that this hybrid approach of execution guidance through a REPL and semantic search provides faster and more reliable program synthesis compared to existing methods.

Methodology

The paper casts the problem of program synthesis as a stochastic search over possible programs facilitated by REPL. By continually evaluating intermediate execution states, it allows the model to condition the search entirely in the semantic space. The REPL mechanism leverages human-like iterative development strategies, enhancing the robustness and reliability of synthesis.

The utilization of SMC search strategy during inference consolidates the capabilities of the policy and value networks. By sampling next actions, executing partial programs, and reweighting the candidates based on the value function, the algorithm explores diverse program candidates effectively.

Numerical Results and Implications

The approach is empirically validated in text editing and graphics, where it consistently outperforms prior methods in terms of speed and accuracy. For example, in the text-editing domain, the REPL-guided model effectively synthesizes complex programs with high accuracy, something that was previously challenging using purely neural or structured search methods.

Implications and Future Directions

The integration of REPL into program synthesis signifies a notable advancement in ensuring the synthesized code aligns with the intended semantics. The introduction of neural models that can execute, assess, and adjust based on execution feedback paves the way for more sophisticated synthesis systems that closely emulate human programming behaviors.

Future work might explore incorporating more complex control flows and the adoption of additional human programming tools such as debuggers. Expanding these models to handle larger and more complex domains, as well as exploring point-free programming styles, may offer promising avenues for further research in program synthesis.

In conclusion, this paper presents a novel methodology that harnesses the strengths of neural networks, execution-guided learning, and symbolic search to advance the field of program synthesis, providing a robust framework that enhances both theoretical understanding and practical capabilities.