Neural Program Search: Solving Programming Tasks from Description and Examples (1802.04335v1)

Abstract: We present a Neural Program Search, an algorithm to generate programs from natural language description and a small number of input/output examples. The algorithm combines methods from Deep Learning and Program Synthesis fields by designing rich domain-specific language (DSL) and defining efficient search algorithm guided by a Seq2Tree model on it. To evaluate the quality of the approach we also present a semi-synthetic dataset of descriptions with test examples and corresponding programs. We show that our algorithm significantly outperforms a sequence-to-sequence model with attention baseline.

• The paper presents a neural program search algorithm that synthesizes correct code from natural language descriptions and example test cases.
• It utilizes a Seq2Tree model with attention mechanisms within a DSL to effectively navigate the search space for program synthesis.
• Evaluation on the AlgoLISP dataset demonstrates significant improvements over classical models while highlighting challenges with data diversity.

Introduction to Neural Program Search

The act of developing software involves translating specific requirements into executable code—a process that requires expertise and is often time-consuming. The role of Neural Program Search (NPS) in this context is to accelerate and simplify coding by synthesizing computer programs from natural language descriptions and example test cases. This innovative approach combines advanced techniques from Deep Learning and Program Synthesis to create an algorithm capable of generating correct and efficient code in response to a given task.

Understanding Neural Program Search

At the heart of NPS lies a domain-specific language (DSL), which is tailored to ease the conversion of specifications into code. This language allows for the creation of templates that can be filled with code for specific tasks. An integral component of the NPS is the Seq2Tree model—a neural network architecture that uses attention mechanisms to guide the search for the correct sequence of code symbols—forming an efficient and adaptable tree structure that represents the program syntax.

Through the model’s guidance and a sophisticated search algorithm, the system generates a tree of all possible programs, searching for one that meets the requirements provided by the user's description and sample inputs and outputs.

Evaluation and Results

To assess the success of this model, a new dataset named AlgoLISP was created, which includes problem statements, associated DSL-based solutions, and test cases. Notably, the combination of deep learning models and search techniques greatly outperformed previous methods. This was evidenced by a clear improvement in results compared to classical sequence-to-sequence models, showcasing the potential of leveraging neural networks for program synthesis.

Closing Remarks

The research behind the Neural Program Search algorithm is indicative of major strides within the field of AI-assisted programming. The promise shown in synthesizing programs from descriptions and examples could dramatically transform our approach to coding by reducing the manual effort and expertise required.

However, challenges still lie ahead. The reliance on semi-generated datasets might hinder the application of NPS to a broader spectrum of problems. Additionally, there is the issue of synthesizing programs with no provided examples—a situation often encountered in real-world settings.

In conclusion, NPS sheds light on a promising avenue for future exploration. While there remains room for further development, particularly in terms of data diversity and model generalizability, the achievements to date suggest a future where AI could play a significant role in refining and even revolutionizing the process of software development.