Rule-Aligned Multilingual Code Functions for Software Quality Analysis

LLM-Assisted Construction of Rule-Aligned Cross-Language Datasets for Software Quality Analysis is a replication package built from the Python Software Quality Dataset. It provides a pipeline to extract, clean, and translate Python functions into Ruby and JavaScript, then validates translations and measures issue preservation. The package, authored by Patcas Rares and last updated on 2026-05-19, includes scripts and cached outputs for reproducibility.

Use Cases

• Evaluating the consistency of static analysis rules across programming languages based on the translated code functions.
• Training or benchmarking models for semantic code translation based on the aligned Python, Ruby, and JavaScript functions.
• Studying the introduction of new code issues during automated translation based on the SonarQube analysis results.
• Analyzing cross-language code similarity based on the embedding-based cosine similarity and rule classification signals.

Strengths

• Includes a 565.0 MB package with resumable scripts and cached outputs supporting reproducible regeneration.
• Provides an end-to-end pipeline for extracting, translating, validating, and analyzing code across three languages.
• Uses multiple validation methods, including syntactic checks (ruby -c, Esprima) and semantic analysis (SonarQube, embedding similarity).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect source bias inherent to the original Python Software Quality Dataset from figshare.

Provenance

Source

Built from the raw data of the Python Software Quality Dataset.

Collection Method

Pipeline extracts SonarQube issue-linked code, translates functions, validates syntax, and analyzes issue preservation.

Freshness

Last updated 2026-05-19 05:36:37; freshness should be verified.