Towards the Automated Extraction and Refactoring of NoSQL Schemas from Application Code

TL;DR

This paper introduces a static analysis method to automatically extract and refactor NoSQL schemas from application code, enabling better schema understanding and optimization.

Contribution

It proposes a platform-independent model-driven approach for extracting and refactoring NoSQL schemas from application code, including detection of join-like patterns and duplication strategies.

Findings

Accurately extracts schemas from NoSQL applications

Identifies refactoring opportunities such as join elimination

Validates approach with a MongoDB application experiment

Abstract

In this paper, we present a static code analysis strategy to extract logical schemas from NoSQL applications. Our solution is based on a model-driven reverse engineering process composed of a chain of platform-independent model transformations. The extracted schema conforms to the U-Schema unified metamodel, which can represent both NoSQL and relational schemas. To support this process, we define a metamodel capable of representing the core elements of object-oriented languages. Application code is first injected into a code model, from which a control flow model is derived. This, in turn, enables the generation of a model representing both data access operations and the structure of stored data. From these models, the U-Schema logical schema is inferred. Additionally, the extracted information can be used to identify refactoring opportunities. We illustrate this capability through the detection of join-like query patterns and the automated application of field duplication strategies to eliminate expensive joins. All stages of the process are described in detail, and the approach is validated through a round-trip experiment in which a application using a MongoDB store is automatically generated from a predefined schema. The inferred schema is then compared to the original to assess the accuracy of the extraction process.