Improving Code Extraction from Coding Screencasts Using a Code-Aware Encoder-Decoder Model

Malkadi, Abdulkarim; Tayeb, Ahmad; Haiduc, Sonia

doi:10.1109/ase56229.2023.00184

2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) 2023

DOI: 10.1109/ase56229.2023.00184

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Improving Code Extraction from Coding Screencasts Using a Code-Aware Encoder-Decoder Model

Abdulkarim Malkadi,

Ahmad Tayeb,

Sonia Haiduc

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2024

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Optimizing OCR Performance for Programming Videos: The Role of Image Super-Resolution and Large Language Models

Alahmadi,

Alshangiti

2024

Mathematics

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The rapid evolution of video programming tutorials as a key educational resource has highlighted the need for effective code extraction methods. These tutorials, varying widely in video quality, present a challenge for accurately transcribing the embedded source code, crucial for learning and software development. This study investigates the impact of video quality on the performance of optical character recognition (OCR) engines and the potential of large language models (LLMs) to enhance code extraction accuracy. Our comprehensive empirical analysis utilizes a rich dataset of programming screencasts, involving manual transcription of source code and the application of both traditional OCR engines, like Tesseract and Google Vision, and advanced LLMs, including GPT-4V and Gemini. We investigate the efficacy of image super-resolution (SR) techniques, namely, enhanced deep super-resolution (EDSR) and multi-scale deep super-resolution (MDSR), in improving the quality of low-resolution video frames. The findings reveal significant improvements in OCR accuracy with the use of SR, particularly at lower resolutions such as 360p. LLMs demonstrate superior performance across all video qualities, indicating their robustness and advanced capabilities in diverse scenarios. This research contributes to the field of software engineering by offering a benchmark for code extraction from video tutorials and demonstrating the substantial impact of SR techniques and LLMs in enhancing the readability and reusability of code from these educational resources.

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Optimizing OCR Performance for Programming Videos: The Role of Image Super-Resolution and Large Language Models

Alahmadi,

Alshangiti

2024

Mathematics

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Improving Code Extraction from Coding Screencasts Using a Code-Aware Encoder-Decoder Model

Cited by 1 publication

References 31 publications

Optimizing OCR Performance for Programming Videos: The Role of Image Super-Resolution and Large Language Models

Optimizing OCR Performance for Programming Videos: The Role of Image Super-Resolution and Large Language Models

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