PyExamine: A Comprehensive, Un-Opinionated Smell Detection Tool for Python

The growth of Python adoption across diverse domains has led to increasingly complex codebases, presenting challenges in maintaining code quality. While numerous tools attempt to address these challenges, they often fall short in providing comprehensive analysis capabilities or fail to consider Pyth...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Proceedings (IEEE/ACM International Conference on Mining Software Repositories. Online) s. 763 - 774
Hlavní autori: Shivashankar, Karthik, Martini, Antonio
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 28.04.2025
Predmet:
Accuracy
Anti-patterns
Best practices
Code Smells
Codes
Data mining
Distance measurement
Organizations
Python
Quality assessment
Software
Software design
ISSN:2574-3864
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The growth of Python adoption across diverse domains has led to increasingly complex codebases, presenting challenges in maintaining code quality. While numerous tools attempt to address these challenges, they often fall short in providing comprehensive analysis capabilities or fail to consider Pythonspecific contexts. PyExamine addresses these critical limitations through an approach to code smell detection that operates across multiple levels of analysis. PyExamine architecture enables detailed examination of code quality through three distinct but interconnected layers: architectural patterns, structural relationships, and code-level implementations. This approach allows for the detection and analysis of 49 distinct metrics, providing developers with an understanding of their codebase's health. The metrics span across all levels of code organization, from high-level architectural concerns to granular implementation details. Through evaluation on 7 diverse projects, PyExamine achieved detection accuracy rates: 91.4 \% for code-level smells, 89.3 \% for structural smells, and 80.6 \% for architectural smells. These results were further validated through extensive user feedback and expert evaluations, confirming PyExamine's capability to identify potential issues across all levels of code organization with high recall accuracy. In additional to this, we have also used PyExamine to analysis the prevalence of different type of smells, across 183 diverse Python projects ranging from small utilities to large-scale enterprise applications. PyExamine's distinctive combination of comprehensive analysis, Python-specific detection, and high customizability makes it a valuable asset for both individual developers and large teams seeking to enhance their code quality practices.
ISSN:2574-3864
DOI:10.1109/MSR66628.2025.00114