Type-Directed Program Synthesis for RESTful APIs

1 Introduction

With the rise of software-as-a-service and microservice architectures, RESTful APIs have become ubiquitous in modern applications. Services like Slack, Stripe, and AWS offer extensive APIs with hundreds of methods, creating significant challenges for developers to find the right combination of methods for their tasks.

APIphany addresses this challenge through component-based synthesis specifically designed for RESTful APIs. The system uses precise semantic types to specify user intent and direct the search process, enabling automated program synthesis from high-level specifications.

2 Background & Related Work

2.1 Component-Based Synthesis

Component-based program synthesis has been successfully applied to navigate APIs in languages like Java, Scala, and Haskell. These synthesizers take type signatures and input-output examples to generate program snippets that compose API calls with desired behavior.

2.2 RESTful API Challenges

Three main challenges complicate applying component-based synthesis to RESTful APIs: (1) lack of precise semantic types in API specifications, (2) need for semi-structured data wrangling, and (3) safety concerns with executing API calls during synthesis.

3 APIphany Architecture

3.1 Semantic Type Inference

APIphany introduces a type inference algorithm that augments REST specifications with semantic types. This enables more precise specification of user intent and guides the synthesis process more effectively.

3.2 Data Wrangling Synthesis

The system includes efficient synthesis techniques for wrangling semi-structured data commonly encountered when working with RESTful APIs, including JSON objects and arrays.

3.3 Simulated Execution

APIphany employs simulated execution to avoid executing actual API calls during synthesis, addressing safety and performance concerns while maintaining synthesis accuracy.

4 Technical Implementation

4.1 Type System Formalism

The type system in APIphany extends standard type systems with semantic annotations. The core type judgment is formalized as:

$\Gamma \vdash e : \tau \Rightarrow \phi$

Where $\Gamma$ is the type environment, $e$ is the expression, $\tau$ is the base type, and $\phi$ is the semantic refinement capturing the expression's behavior.

4.2 Synthesis Algorithm

The synthesis algorithm uses type-directed search with backtracking. The search space is defined by:

$P := \text{apiCall}(p_1, \dots, p_n) \mid \text{map}(P, \lambda x. P) \mid \text{filter}(P, \lambda x. P) \mid \text{compose}(P, P)$

The algorithm prunes invalid candidates early using type constraints and semantic refinements.

5 Experimental Evaluation

5.1 Methodology

APIphany was evaluated on three real-world APIs (Slack, Stripe, GitHub) with 32 tasks extracted from GitHub repositories and StackOverflow. Tasks included common integration scenarios like retrieving member emails from Slack channels and processing payment data from Stripe.

5.2 Results & Performance

APIphany successfully found correct solutions for 29 out of 32 tasks (90.6% success rate). Among these, 23 solutions were reported among the top ten synthesis results, demonstrating the effectiveness of the type-directed approach.

6 Code Examples

Example synthesis task for retrieving Slack channel member emails:

// Input specification
Type: ChannelName -> List[Email]

// Synthesized solution
function getChannelEmails(channelName) {
  const channels = conversations_list();
  const targetChannel = channels.find(c => c.name === channelName);
  const memberIds = conversations_members(targetChannel.id);
  return memberIds.map(id => {
    const user = users_info(id);
    return user.profile.email;
  });
}

7 Future Applications & Directions

APIphany's approach can be extended to other domains including:

• GraphQL API synthesis with type introspection
• Microservice orchestration in cloud-native applications
• Internet of Things (IoT) device integration
• Enterprise system integration and legacy API modernization

Future work includes integrating machine learning for better type inference and expanding support for asynchronous API patterns.

8 Original Analysis

APIphany represents a significant advancement in program synthesis for web APIs, addressing fundamental challenges that have limited previous approaches. The integration of semantic types with component-based synthesis creates a powerful framework that bridges the gap between formal methods and practical API integration tasks.

The type inference mechanism in APIphany shares conceptual similarities with refinement type systems in languages like Liquid Haskell [1], but adapts these concepts for the dynamic, semi-structured world of REST APIs. This adaptation is crucial because, unlike statically-typed languages where types are explicit, REST APIs often rely on JSON schemas that provide structural but not semantic typing information.

The simulated execution technique is particularly innovative, drawing inspiration from symbolic execution in program verification [2] but applying it to API synthesis. This approach addresses the critical safety concern of executing potentially destructive API operations during the synthesis process. Similar techniques have been used in database query optimization [3], but APIphany adapts them for the more complex domain of multi-API program synthesis.

When compared to other synthesis approaches like FlashFill [4] for string transformations or SyPet [5] for component-based synthesis, APIphany demonstrates how domain-specific knowledge (REST API semantics) can dramatically improve synthesis effectiveness. The 90.6% success rate on real-world tasks significantly exceeds what would be expected from general-purpose synthesizers, supporting the hypothesis that domain-aware synthesis is essential for practical applications.

The data wrangling component addresses a fundamental challenge in API integration: the impedance mismatch between API data formats and application needs. This problem is reminiscent of data transformation challenges in ETL (Extract, Transform, Load) processes [6], but APIphany solves it through synthesis rather than manual specification. The approach could potentially influence future API design practices, encouraging more systematic type information in API specifications.

Looking forward, APIphany's techniques could be integrated with large language models for API code generation. While models like GPT-3 [7] show impressive capabilities for code generation, they lack the semantic precision and safety guarantees of type-directed synthesis. A hybrid approach combining neural generation with type-directed verification could represent the next frontier in practical program synthesis.

9 References

1. Vazou, N., et al. "Refinement types for Haskell." ICFP 2014.
2. Baldoni, R., et al. "A survey of symbolic execution techniques." ACM Computing Surveys 2018.
3. Neumann, T. "Efficiently compiling efficient query plans for modern hardware." VLDB 2011.
4. Gulwani, S. "Automating string processing in spreadsheets using input-output examples." POPL 2011.
5. Feng, Y., et al. "Component-based synthesis for complex APIs." OOPSLA 2017.
6. Vassiliadis, P. "A survey of extract-transform-load technology." IJDWM 2009.
7. Brown, T., et al. "Language models are few-shot learners." NeurIPS 2020.
8. Polikarpova, N., et al. "Program synthesis from polymorphic refinement types." PLDI 2016.