Comparison of ChatGPT's Programming Code Debugging Performance with PyCharm Traditional Debugging Tool Performance

Fırat Kapar; H. Eray Çelik; Serbest Ziyanak

Article Details

Comparison of ChatGPT's Programming Code Debugging Performance with PyCharm Traditional Debugging Tool Performance

Authors:

Fırat Kapar | H. Eray Çelik | Serbest Ziyanak

Publisher:

Van Yuzuncu Yıl University

Main Topics:

İstatistiksel Veri Bilimi | Uygulamalı İstatistik

Journal:

Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.29 Issue 3, pp.890-895

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001338123

pISSN:

1300-5413

Keywords:

Abstract:

 This study aims to examine the potential of next-generation AI-based debugging approaches by comparing the programming code debugging capabilities of ChatGPT with a traditional debugging tool. For this purpose, programming codes containing common syntax, logic, and type mismatch errors in Python were generated and analyzed using PyCharm and OpenAIs ChatGPT-4. The study found that while ChatGPT-4 showed similar success to PyCharm in identifying syntax errors, it was significantly more successful than PyCharm in detecting logic and type mismatch errors. These results suggest that AI-supported tools could provide an advantage over traditional methods in effectively detecting and correcting programming errors.

 This study aims to examine the potential of next-generation AI-based debugging approaches by comparing the programming code debugging capabilities of ChatGPT with a traditional debugging tool. For this purpose, programming codes containing common syntax, logic, and type mismatch errors in Python were generated and analyzed using PyCharm and OpenAIs ChatGPT-4. The study found that while ChatGPT-4 showed similar success to PyCharm in identifying syntax errors, it was significantly more successful than PyCharm in detecting logic and type mismatch errors. These results suggest that AI-supported tools could provide an advantage over traditional methods in effectively detecting and correcting programming errors.

 This study aims to examine the potential of next-generation AI-based debugging approaches by comparing the programming code debugging capabilities of ChatGPT with a traditional debugging tool. For this purpose, programming codes containing common syntax, logic, and type mismatch errors in Python were generated and analyzed using PyCharm and OpenAIs ChatGPT-4. The study found that while ChatGPT-4 showed similar success to PyCharm in identifying syntax errors, it was significantly more successful than PyCharm in detecting logic and type mismatch errors. These results suggest that AI-supported tools could provide an advantage over traditional methods in effectively detecting and correcting programming errors.

 Bu çalışmanın amacı ChatGPTnin programlama kodu hata ayıklama yeteneklerini geleneksel hata ayıklama aracı ile karşılaştırarak yeni nesil yapay zeka tabanlı hata ayıklama yaklaşımlarının potansiyelini incelemektedir. Bu amaçla, Python programlama dilinde sıkça rastlanan söz dizimi, mantık ve tür uyuşmazlığı hatalarını içeren programlama kodları türetilmiş ve PyCharm ve OpenAI tarafından geliştirilen ChatGPT-4 ile bu programlama kodları analiz edilerek her iki aracın hata ayıklama performansı karşılaştırılmıştır. Çalışma sonucunda ChatGPT-4ün söz dizimi hatalarında PyCharm ile benzer başarı gösterirken mantık ve tür uyuşmazlığı hatalarını belirlemede PyCharma göre çok daha başarılı olduğu saptanmıştır. Bu sonuç doğrultusunda, yapay zekâ destekli araçların, programlama hatalarını etkili bir şekilde tespit etme ve düzeltme konusunda geleneksel yöntemlere kıyasla avantaj sağlayabileceği görülmektedir.

Article Details

Comparison of ChatGPT's Programming Code Debugging Performance with PyCharm Traditional Debugging Tool Performance

Authors:

Fırat Kapar | H. Eray Çelik | Serbest Ziyanak

Publisher:

Van Yuzuncu Yıl University

Main Topics:

İstatistiksel Veri Bilimi | Uygulamalı İstatistik

Journal:

Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.29 Issue 3, pp.890-895

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001338123

pISSN:

1300-5413

ChatGPT

Debugging

PyCharm

PythonChatGPT

Hata Ayıklama

PyCharm

Python

Issues

Article Details

Authors: Fırat Kapar | H. Eray Çelik | Serbest Ziyanak

Publisher: Van Yuzuncu Yıl University

Main Topics: İstatistiksel Veri Bilimi | Uygulamalı İstatistik

Journal: Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.29 Issue 3, pp.890-895

Document Type: Article

Publication Date: 12/2024

Document Language: Turkish

Document ID (Org No.): 2001338123

pISSN: 1300-5413

Issues

Authors:

Fırat Kapar | H. Eray Çelik | Serbest Ziyanak

Publisher:

Van Yuzuncu Yıl University

Main Topics:

İstatistiksel Veri Bilimi | Uygulamalı İstatistik

Journal:

Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.29 Issue 3, pp.890-895

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001338123

pISSN:

1300-5413