DeepCoder: Learning to Write Programs (1611.01989v2)

Abstract: We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs. We use the neural network's predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver. Empirically, we show that our approach leads to an order of magnitude speedup over the strong non-augmented baselines and a Recurrent Neural Network approach, and that we are able to solve problems of difficulty comparable to the simplest problems on programming competition websites.

• The paper introduces a framework that combines deep learning with program synthesis to predict program properties from examples.
• It employs a domain-specific language and automated data generation to train neural networks that guide search algorithms efficiently.
• Experiments demonstrate significant speedups, with some search methods achieving over 900x improvement in solving synthesis tasks.

An Overview of "DeepCoder: Learning to Write Programs"

The paper "DeepCoder: Learning to Write Programs" introduces a novel approach to solve programming competition-style problems using a combination of deep learning techniques and program synthesis strategies. This method focuses on learning to predict program properties from input-output examples, which is then used to augment traditional search techniques from the programming languages community.

The authors propose a framework called Learning Inductive Program Synthesis (LIPS), which consists of a domain-specific language (DSL), a data generation process, a machine learning model for predicting program attributes, and a search procedure guided by these predictions. The paper presents significant empirical results, showcasing speedups of several orders of magnitude over baseline methods.

Key Contributions and Methodology

1. DSL and Program Representation: The chosen DSL is inspired by query languages like SQL and LINQ, providing a high-level abstraction that is expressive for real-world programming tasks yet constrained enough to allow accurate predictions from examples. This includes constructs like map, filter, and reduce functions, commonly found in functional programming.
2. Data Generation: A training dataset is automatically generated by enumerating programs within the DSL, allowing the authors to leverage a large corpus of examples to train high-capacity neural networks.
3. Machine Learning Model: The authors utilize a feed-forward neural network to map input-output examples to program attributes. This model is trained to predict the presence of certain high-level functions in the program that generated the examples, effectively transforming the generation of source code into a supervised learning task.
4. Search Integration: The method uses a predicted probability distribution over program attributes to inform and guide program synthesis search methods like depth-first search (DFS) and SMT-based solvers, thereby speeding up the search process.

Numerical Results and Findings

The paper reports considerable improvements in solving program synthesis tasks concerning the test cases derived from programming competition websites. The experiments demonstrate:

• Speedups: Depending on the search method used, the results indicate speedups ranging from multiples to orders of magnitudes when using the neural network's predictions over standard unaugmented search methods. For instance, in some configurations, Sort and Add enumeration achieved a speedup of over 900 times.
• Generalization: The method displays robustness in terms of generalizing across different program lengths, as demonstrated by solving tasks of unseen program lengths during training with still significant speedups.

Implications and Future Directions

The implications of this work are considerable for the fields of artificial intelligence and programming language design. Practically, it provides a more efficient method for automated program synthesis, which can be beneficial for educational tools, software engineering, and systems programming. Theoretically, it advances the understanding of how machine learning can be used to model and predict complex abstract structures like programs.

For future research, the integration of natural language descriptions into the learning process could reduce the reliance on input-output examples and enhance performance. Also, expanding the DSL to encompass more complex constructs like loops and advanced control flow might extend the framework’s applicability to broader categories of programming problems.

In conclusion, the paper represents a significant step in using deep learning to support program synthesis, indicating promising avenues for reducing the computational complexity of generating human-readable code automatically. The methodology and results set a strong foundation for future exploration into more comprehensive and flexible systems in this domain.