Exploring the Effectiveness of Abstract Syntax Tree Patterns for Algorithm Recognition

2026-05-07

AI Summary

Existing algorithm identification methods often suffer from poor usability, limited scalability, and insufficient evaluation. This work proposes a novel paradigm that integrates domain-specific languages (DSLs) with abstract syntax tree (AST) pattern matching: algorithmic characteristics are formally specified using a DSL to construct a reusable library of AST patterns, enabling automatic recognition of common algorithm implementations in source code. Evaluated on a subset of BigCloneEval, the approach achieves an average F1 score of 0.74, substantially outperforming CodeLlama (0.35) and state-of-the-art code clone detectors, which attain a recall of only 0.20 compared to our method’s 0.62. This advance represents a dual improvement in both precision and practical applicability for algorithm identification.

Abstract

The automated recognition of algorithm implementations can support many software maintenance and re-engineering activities by providing knowledge about the concerns present in the code base. Moreover, recognizing inefficient algorithms like Bubble Sort and suggesting superior alternatives from a library can help in assessing and improving the quality of a system. Approaches from related work suffer from usability as well as scalability issues and their accuracy is not evaluated. In this paper, we investigate how well our approach based on the abstract syntax tree of a program performs for automatic algorithm recognition. To this end, we have implemented a prototype consisting of: A domain-specific language designed to capture the key features of an algorithm and used to express a search pattern on the abstract syntax tree, a matching algorithm to find these features, and an initial catalog of "ready to use" patterns. To create our search patterns we performed a web search using the algorithm name and described key features of the found reference implementations with our domain-specific language. We evaluate our prototype on a subset of the BigCloneEval benchmark containing algorithms like Fibonacci, Bubble Sort, and Binary Search. We achieve an average F1-score of 0.74 outperforming the large language model Codellama which attains 0.35. Additionally, we use multiple code clone detection tools as a baseline for comparison, achieving a recall of 0.62 while the best-performing tool reaches 0.20.

Problem

• algorithm recognition
• abstract syntax tree
• software maintenance
• code quality
• automated analysis

Innovation

• Abstract Syntax Tree
• Algorithm Recognition
• Domain-Specific Language
• Pattern Matching
• Code Clone Detection