DOĞAL DİL METİNLERİNDEN PROGRAMLAMA DİLİ KODU OLUŞTURMA ÇALIŞMALARI: BİR DERLEME ÇALIŞMASI

Year 2024, Volume: 23 Issue: 45, 209 - 244, 26.06.2024

Ayşegül Hatipoğlu , Turgay Tugay Bilgin

https://doi.org/10.55071/ticaretfbd.1354040

Abstract

Son yıllarda Doğal Dil İşleme (DDİ) alanındaki gelişmelerin hız kazanması, araştırmacıların ve programcıların bu alana olan ilgisini büyük ölçüde artırmıştır. Bilgisayar programlarını doğal dil komutlarıyla yazma konsepti, birçok araştırmacının odak noktası haline gelmiştir. Literatür incelendiğinde, doğal dil ile programlama üzerine yapılan araştırmaların uzun bir geçmişe sahip olduğu açıkça görülmektedir. Bu uzun soluklu araştırmalar, çeşitli çözüm önerilerini beraberinde getirmiş ve kural tabanlı yöntemlerden, olasılık tabanlı yöntemlere, makine öğrenmesi yöntemlerinden derin öğrenme yöntemlerine kadar bir dizi çözüm yaklaşımının ortaya çıkmasına neden olmuştur. Literatürdeki çalışmalar tarihsel olarak kategorize edildiğinde geçmiş tarihli çalışmalarda kural tabanlı ya da istatistik tabanlı modeller üzerine yoğunlaştığı görülürken günümüze yaklaşıldıkça makine öğrenmesi ve derin öğrenme temelli çalışmaların arttığı görülmektedir. Kural tabanlı yöntemler, olasılık tabanlı yöntemler, makine öğrenmesi yöntemleri, derin öğrenme yöntemleri gibi çeşitli yaklaşımların geliştirildiği literatürde, bu çeşitlilik yeni araştırmacıların bu alana giriş yaparken karşılaşabileceği karmaşıklığı artırabilmektedir. Bu çalışma, doğal dil girdileriyle programlama dili kodu oluşturma çalışmalarına yönelik literatürde geliştirilen 32 yöntemin detaylı bir incelenmesini sunmaktadır. Çalışmanın amacı, literatürde tespit edilen çeşitli yöntemlerin zaman içerisindeki değişimlerinin gözden geçirilmesi, çalışmaların geniş bir perspektiften incelenerek genel bir çerçeve içinde toplanması ve bu alanda çalışacak olan araştırmacılara rehberlik edebilmesidir.

Keywords

Derin Öğrenme, Doğal Dil İşleme, Kod Üretimi, Makine Öğrenmesi

NATURAL LANGUAGE TEXT TO PROGRAMMING LANGUAGE CODE GENERATION STUDIES: A REVIEW

Abstract

The recent surge in advancements in Natural Language Processing (NLP) has significantly heightened the interest of researchers and programmers in this field. The concept of writing computer programs using natural language commands has become a focal point for many researchers. Upon reviewing the literature, it is evident that research on natural language programming has a long history. These extensive studies have led to the proposal of various solutions, ranging from rule-based methods to probability-based approaches, machine learning methods, and deep learning techniques. When the studies in the literature are categorized historically, it is seen that the past studies focused on rule-based or statistical-based models, while machine learning and deep learning-based studies have increased as we approach the present. The diversity of approaches, including Rule-based methods, probability-based methods, machine learning methods, deep learning methods, and others, as found in the literature, can potentially confuse newcomers entering this field. This paper presents a detailed review of 32 methods developed in the literature for generating programming language code with natural language input. The goal of this study is to review the changes in various methods identified in the literature over time, to collect the studies in a general framework by examining them from a broad perspective and provide guidance to researchers intending to work in this area.

Keywords

Code Generation, Deep Learning, Machine Learning, Natural Language Processing

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