A Systematic Survey on Large Language Models for Static Code Analysis.
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| Název: | A Systematic Survey on Large Language Models for Static Code Analysis. |
|---|---|
| Autoři: | Mohammed Salih, Hekar A., Sarhan, Qusay I. |
| Zdroj: | ARO: The Scientific Journal of Koya University; 2025, Vol. 13 Issue 1, p251-265, 15p |
| Témata: | LANGUAGE models, ARTIFICIAL intelligence, SOFTWARE measurement, COMPUTER software quality control, EVIDENCE gaps |
| Abstract (English): | Static code analysis plays a pivotal role in improving software quality, security, and maintainability by detecting vulnerabilities, errors, and programming issues in source code without executing it. Recent advancements in artificial intelligence, especially the development of large language models (LLMs), such as ChatGPT, have enabled transformational opportunities in this domain. Thus, it is essential to explore this emerging field of research from many perspectives. This systematic survey focuses on the use of LLMs for static code analysis, detailing their applications, advantages, contexts, limitations, etc. The study examines research papers published on the topic from reputable literature databases to answer several research questions regarding the state-of-the-art use of LLMs in static code analysis. In addition, different research gaps and challenges were identified and discussed alongside many directions. The results of this study demonstrate how LLMs can enhance static code analysis and address existing limitations, paving the way for developers and researchers to employ LLMs for a more affordable and effective software development process. [ABSTRACT FROM AUTHOR] |
| Abstract (Arabic): | المقال يقدم مسحًا منهجيًا حول تطبيق نماذج اللغة الكبيرة (LLMs) في تحليل الشيفرة الثابتة، مع التركيز على إمكانياتها في تعزيز جودة البرمجيات وأمانها وقابليتها للصيانة. يناقش المقال مزايا وقيود نماذج اللغة الكبيرة، مثل قدرتها على فهم دلالات الشيفرة المعقدة والتحديات التي تطرحها معدلات الإيجابيات الكاذبة العالية وتكاليف الحوسبة. يحدد المسح الفجوات البحثية الرئيسية ويقترح اتجاهات مستقبلية لدمج نماذج اللغة الكبيرة في سير عمل تحليل الشيفرة الثابتة، بهدف تحسين الدقة والكفاءة في تطوير البرمجيات. بشكل عام، تسلط النتائج الضوء على الدور التحويلي لنماذج اللغة الكبيرة في معالجة التحديات المستمرة في مجال هندسة البرمجيات. [Extracted from the article] |
| Copyright of ARO: The Scientific Journal of Koya University is the property of Koya University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Databáze: | Complementary Index |
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