Diffing with Open Standards

1. Introduction

Modern software development increasingly depends on automated tools to ensure code quality, maintainability, and adherence to established coding standards. As software systems grow in size and complexity, manual code review becomes insufficient for detecting subtle structural inconsistencies and deviations from coding conventions. As a result, automated diffing and static analysis tools have become essential components of contemporary software engineering workflows.

Despite their widespread use, many existing diffing tools rely primarily on text-based or token-based comparison techniques. While effective for identifying superficial changes, these approaches often fail to capture deeper semantic and structural relationships within source code (Li et al., 2022). This limitation reduces their effectiveness in enforcing consistent coding standards and detecting logically equivalent but structurally inconsistent implementations.

To address these challenges, this study proposes Diffing with Open Standards: A Graph Memory Copy Algorithm for Code Conformance Checking. The proposed system represents source code as an Abstract Syntax Tree (AST) and performs diffing through graph traversal, localized node analysis, and controlled node replication and replacement.

1.1 Background of the Study

Code conformance issues frequently arise in software projects involving multiple developers, inconsistent coding practices, or varying interpretations of style guidelines. Deviations from established standards negatively affect code readability, maintainability, and long-term sustainability, often increasing the likelihood of defects and technical debt.

Recent research has explored graph-based representations of source code, particularly through Abstract Syntax Trees and control-flow graphs, to enable more semantically meaningful analysis. AST-based diffing techniques allow structural comparisons that better reflect the logical organization of programs, offering improved detection of non-trivial code differences. However, many existing graph-based approaches continue to face challenges related to scalability, memory consumption, and adaptability across programming languages.

1.2 Theoretical Framework

The proposed graph memory copy algorithm operates by analyzing each AST node using only its immediate syntactic neighbors. This localized approach allows the system to detect structural deviations efficiently without requiring global knowledge of the entire codebase. When a non-conforming node is identified, the algorithm performs controlled duplication and replacement to align the structure with predefined open coding standards.

1.3 Conceptual Framework

The conceptual framework illustrates the systematic process through which the proposed diffing system analyzes, corrects, and validates a codebase. The framework begins with the input code base, which is passed to the parser subsystem for lexical analysis.

During parsing, dirty bit detection is applied to identify modified or potentially non-conformant sections of the code, while copy-on-write (COW) logic is utilized to preserve the integrity of the original code structure during analysis.

1.4 Statement of the Problem

Specifically, the study seeks to address the following problems:

1. Existing diffing tools are insufficient in identifying logically equivalent but structurally inconsistent code implementations.
2. Graph-based code analysis techniques often suffer from scalability and high memory consumption.
3. Many code conformance tools are tightly coupled to specific programming languages or proprietary rule sets.
4. There is a lack of efficient, localized algorithms that can perform structural conformance checking using limited memory while preserving semantic accuracy.

1.5 Objectives

General Objective: To design and evaluate a graph-based diffing algorithm that automatically detects and corrects non-conforming code structures using localized AST node analysis and limited memory.

Specific Objectives:

• To model source code as an Abstract Syntax Tree and develop a graph memory copy algorithm for detecting and correcting non-conforming nodes.
• To implement the proposed algorithm alongside baseline diffing approaches such as textual diffing.
• To evaluate the performance of the proposed system in terms of detection accuracy, memory usage, and processing time.

1.7 Significance of the Study

• Software Developers and QA Engineers will benefit from improved detection of structural inconsistencies.
• Software Organizations can use the proposed approach to enforce open coding standards efficiently across large development teams.
• Researchers and Educators may leverage the algorithm as a foundation for further studies in program analysis.
• Open-source Communities can adopt the system to maintain consistency and quality in collaborative projects.