SWIM: Synthesizing What I Mean (1511.08497v2)

Abstract: Modern programming frameworks come with large libraries, with diverse applications such as for matching regular expressions, parsing XML files and sending email. Programmers often use search engines such as Google and Bing to learn about existing APIs. In this paper, we describe SWIM, a tool which suggests code snippets given API-related natural language queries such as "generate md5 hash code". We translate user queries into the APIs of interest using clickthrough data from the Bing search engine. Then, based on patterns learned from open-source code repositories, we synthesize idiomatic code describing the use of these APIs. We introduce \emph{structured call sequences} to capture API-usage patterns. Structured call sequences are a generalized form of method call sequences, with if-branches and while-loops to represent conditional and repeated API usage patterns, and are simple to extract and amenable to synthesis. We evaluated SWIM with 30 common C# API-related queries received by Bing. For 70% of the queries, the first suggested snippet was a relevant solution, and a relevant solution was present in the top 10 results for all benchmarked queries. The online portion of the workflow is also very responsive, at an average of 1.5 seconds per snippet.

• The paper proposes SWIM, a system that synthesizes idiomatic C# code snippets from natural language queries using Bing clickthrough data and structured API call sequences from open-source repositories.
• Evaluation shows SWIM processed 30 frequent C# API queries efficiently, with the first suggested snippet relevant 70% of the time and relevant solutions appearing in the top 10 for all queries.
• SWIM's methodology offers a practical tool for developers tackling API tasks in large frameworks and introduces structured call sequences as a robust way to represent and apply API usage patterns.

Insights on SWIM: Synthesizing What I Mean

The paper "SWIM: Synthesizing What I Mean" by Mukund Raghothaman, Yi Wei, and Youssef Hamadi proposes an innovative approach to code synthesis, facilitating programmers in generating idiomatic C# code snippets through natural language queries. This approach addresses the common challenge developers face when dealing with API-related tasks in large software frameworks without requiring deep knowledge about the framework-specific trivia.

Synopsis of SWIM

SWIM operates by translating natural language queries into relevant APIs using clickthrough data from Bing. It subsequently synthesizes idiomatic code snippets by utilizing structured call sequences extracted from open-source repositories. These structured call sequences generalize method call sequences to include conditional and repeated API usage patterns, which are instrumental in synthesizing user-requested code snippets.

The paper reports evaluation results where SWIM processed 30 frequent C# API queries with remarkable efficiency. In 70% of the cases, the first snippet suggested was a relevant solution, and relevant solutions appeared in the top 10 results for all queries. This demonstrated the responsiveness and practical applicability of the tool, with an overview time averaging about 1.5 seconds per snippet.

Technical Contributions

The authors contribute to the field through several key innovations:

1. Natural Language to API Mapping: Leveraging clickthrough data to map user queries to API names, which allows SWIM to understand and suggest relevant APIs effectively.
2. Structured Call Sequences: Introducing a methodology to capture typical usage patterns for APIs, allowing more nuanced and accurate response to user queries. This concept helps in generating snippets that adhere to common coding conventions.
3. Snippet Synthesis Algorithm: Developing an algorithm that synthesizes code snippets from the patterns found in structured call sequences, enhancing code synthesis from vague natural language queries.
4. Evaluation Protocol: Implementing a prototype to evaluate the practicality and effectiveness of SWIM in generating relevant code snippets, backed by extensive datasets and benchmarks.

Results and Implications

SWIM's ability to map user queries to structured call sequences, followed by synthesizing code snippets, makes it a useful tool for developers needing quick API-related solutions without extensive manual search. This positions SWIM as a promising tool for aiding programming in large-scale software frameworks like .NET and Java SDK.

Moreover, structured call sequences provide a robust mechanism for representing API usage patterns, which could be applied in other applications such as anomaly detection. These findings suggest potential extensions into improving program synthesis technology and understanding API usage patterns better through empirical analysis.

Looking Forward

The paper indicates several areas for future exploration, including refining NLP techniques to better distinguish between similar APIs, handling complex language features like exceptions, and extending techniques to support more joint distributions. These improvements could enhance SWIM's capabilities, making it even more effective in real-world software engineering scenarios.

In conclusion, SWIM provides a pragmatic methodology for automatic synthesis of code snippets from natural language queries, demonstrating impressive efficiency and relevance. Its approach paves the way for further research into program synthesis and API usage assistance, underscoring the synergy between machine learning, natural language processing, and software engineering.