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arXiv · 2501.02338

Evaluation of the Code Generation Capabilities of ChatGPT 4: A Comparative Analysis in 19 Programming Languages

L. C. Gilbert
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Abstract

This bachelor's thesis examines the capabilities of ChatGPT 4 in code generation across 19 programming languages. The study analyzed solution rates across three difficulty levels, types of errors encountered, and code quality in terms of runtime and memory efficiency through a quantitative experiment. A total of 188 programming problems were selected from the LeetCode platform, and ChatGPT 4 was given three attempts to produce a correct solution with feedback. ChatGPT 4 successfully solved 39.67% of all tasks, with success rates decreasing significantly as problem complexity increased. Notably, the model faced considerable challenges with hard problems across all languages. ChatGPT 4 demonstrated higher competence in widely used languages, likely due to a larger volume and higher quality of training data. The solution rates also revealed a preference for languages with low abstraction levels and static typing. For popular languages, the most frequent error was "Wrong Answer," whereas for less popular languages, compiler and runtime errors prevailed, suggesting frequent misunderstandings and confusion regarding the structural characteristics of these languages. The model exhibited above-average runtime efficiency in all programming languages, showing a tendency toward statically typed and low-abstraction languages. Memory efficiency results varied significantly, with above-average performance in 14 languages and below-average performance in five languages. A slight preference for low-abstraction languages and a leaning toward dynamically typed languages in terms of memory efficiency were observed. Future research should include a larger number of tasks, iterations, and less popular languages. Additionally, ChatGPT 4's abilities in code interpretation and summarization, debugging, and the development of complex, practical code could be analyzed further. ---- Diese Bachelorarbeit untersucht die F\"ahigkeiten von ChatGPT 4 zur Code-Generierung in 19 Programmiersprachen. Betrachtet wurden die L\"osungsraten zwischen drei Schwierigkeitsgraden, die aufgetretenen Fehlerarten und die Qualit\"at des Codes hinsichtlich der Laufzeit- und Speichereffizienz in einem quantitativen Experiment. Dabei wurden 188 Programmierprobleme der Plattform LeetCode entnommen, wobei ChatGPT 4 jeweils drei Versuche hatte, mittels Feedback eine korrekte L\"osung zu generieren. ChatGPT 4 l\"oste 39,67 % aller Aufgaben erfolgreich, wobei die Erfolgsrate mit zunehmendem Schwierigkeitsgrad deutlich abnahm und bei komplexen Problemen in allen Sprachen signifikante Schwierigkeiten auftraten. Das Modell zeigte eine h\"ohere Kompetenz in weit verbreiteten Sprachen, was wahrscheinlich auf eine gr\"o{\ss}ere Menge und h\"ohere Qualit\"at der Trainingsdaten zur\"uckzuf\"uhren ist. Bez\"uglich der L\"osungsraten zeigte das Modell zudem eine Pr\"aferenz f\"ur Sprachen mit niedrigem Abstraktionsniveau und statischer Typisierung. Bei Sprachen hoher Popularit\"at trat der Fehler Wrong Answer am h\"aufigsten auf, w\"ahrend bei weniger popul\"aren Sprachen Compiler- und Laufzeitfehler \"uberwogen, was auf h\"aufige Missverst\"andnisse und Verwechslungen bez\"uglich der spezifischen strukturellen Eigenschaften dieser Sprachen zur\"uckzuf\"uhren ist. ChatGPT 4 demonstrierte in allen Programmiersprachen eine \"uberdurchschnittliche Laufzeiteffizienz und tendierte diesbez\"uglich erneut zu statisch typisierten und niedrig abstrahierten Sprachen. Die Werte zur Speichereffizienz variierten erheblich, wobei in 14 Sprachen \"uberdurchschnittliche und in f\"unf Sprachen unterdurchschnittliche Werte erzielt wurden. Es zeigte sich diesbez\"uglich eine leichte Tendenz zugunsten von niedrig abstrahierten sowie eine Pr\"aferenz zu dynamisch typisierten Sprachen. Zuk\"unftige Forschung sollte eine h\"ohere Anzahl an Aufgaben, Iterationen und unpopul\"aren Sprachen einbeziehen. Dar\"uber hinaus k\"onnten die F\"ahigkeiten von ChatGPT 4 in der Code-Interpretation und -Zusammenfassung, im Debugging und in der Entwicklung komplexer, praxisbezogener Codes analysiert werden.

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L. C. Gilbert. 2025-01-04. Evaluation of the Code Generation Capabilities of ChatGPT 4: A Comparative Analysis in 19 Programming Languages. https://doi.org/10.5281/zenodo.14599318

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