الدكتور هيثم حجازي يشارك في نشر دراسة جديدة بعنوان ” لا تفاعل بلا فهم: قياس مدى استيعاب المبرمجين للكود في بيئات العمل البرمجية الحديثة باستخدام الإشارات العصبية-الفسيولوجية”
ملخص البحث
Code comprehension assessment is crucial in modern software engineering contexts, such as the emerging LLM-supported programming paradigm, where evaluating and adjusting LLM-generated code to ensure suitability, correctness, and readability is mandatory. Recent literature offers various code comprehension solutions, ranging from subjective surveys to neurophysiological-based approaches that are more personalized and operational. However, existing proposals often estimate the cognitive load experienced by programmers during code handling, using this measure as a surrogate for code comprehension. This approach has limitations: it is indirect, as other factors influence cognitive load, and a high cognitive load does not necessarily indicate a lack of code understanding. In this paper, we propose a neurophysiological and AI-based solution using a multimodal set of biosensors, including EEG and eyetracking, along with other contextual features to measure the level of code comprehension. Instead of using cognitive load as a surrogate for code comprehension, this work tackles the challenge of assessing code comprehension by employing performance-annotated ground truth. The solution is customizable, allowing adaptation to different industrial requirements, such as stringent safety and reliability needs in mission-critical software or less critical contexts. We analyze various application scenarios to minimize the intrusiveness of the solution while maintaining acceptable performance. Evaluated in a controlled experiment with 50 programmers and 7 code comprehensions tasks, the porposed solution achieved an accuracy of 69% in the prediction of correct code comprehension. This binary modelling achieve an AUC of 75%, demonstrating its viability for measuring code comprehension in modern software development. We believe that such comprehension assessment methods are essential in current “vibe coding” workflows, where AI tools assist programmers interactively, and code understanding levels must be monitored in real-time to ensure effective human-AI collaboration.
كيفية الاستشهاد للبحث:
Saraiva, R., Durães, J., De Carvalho, P., Madeira, H., & Hijazi, H. (2025). No vibe without comprehension: Measuring code understanding in modern coding workflows using neurophysiological signals. In Proceedings of the 2025 IEEE 36th International Symposium on Software Reliability Engineering (ISSRE) (pp. 167–178).
رابط البحث