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14th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE)

作者： Alahmadi, Mohammad Hassel, Jonathan Parajuli, Biswas Haiduc, Sonia Kumar, Piyush Florida State Univ Dept Comp Sci Tallahassee FL 32306 USA

ISBN: (纸本)9781450365932

Background: video programming tutorials are becoming a popular resource for developers looking for quick answers to a specific programming problem or trying to learn a programming topic in more depth. Since the most important source of information for developers in many such videos is source code, it is important to be able to accurately extract this code from the screen, such that developers can easily integrate it into their programs. Aims: Our main goal is to facilitate the accurate and noise-free extraction of code appearing in programming video tutorials. In particular, in this paper we aim to accurately predict the location of source code in video frames. This will allow for the dramatic reduction of noise when using extraction techniques such as Optical Character Recognition, which could otherwise extract a large amount of irrelevant text (e.g., text found in menu items, package hierarchy, etc.). Method: We propose an approach using a deep Convolutional Neural Network (CNN) to predict the bounding box of fully-visible code sections in video frames. To evaluate our approach, we collected a set of 150 Java programming tutorials, having more than 82K frames in total. A sample of 4,000 frames from these videos were then manually annotated with the code bounding box location and used as the ground truth in an experiment evaluating our approach. Results: The results of the evaluation show that our approach is able to successfully predict the code bounding box in a given frame with 92% accuracy. Conclusions: Our CNN-based approach is able to accurately predict the location of source code within the frames of programming video tutorials.

关键词： programming video tutorials Software documentation Source code Deep learning video mining

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Code Localization in programming Screencasts

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EMPIRICAL SOFTWARE ENGINEERING 2020年第2期25卷 1536-1572页

作者： Alahmadi, Mohammad Khormi, Abdulkarim Parajuli, Biswas Hassel, Jonathan Haiduc, Sonia Kumar, Piyush Florida State Univ 600 W Coll Ave Tallahassee FL 32306 USA

programming screencasts are growing in popularity and are often used by developers as a learning source. The source code shown in these screencasts is often not available for download or copy-pasting. Without having the code readily available, developers have to frequently pause a video to transcribe the code. This is time-consuming and reduces the effectiveness of learning from videos. Recent approaches have applied Optical Character Recognition (OCR) techniques to automatically extract source code from programming screencasts. One of their major limitations, however, is the extraction of noise such as the text information in the menu, package hierarchy, etc. due to the imprecise approximation of the code location on the screen. This leads to incorrect, unusable code. We aim to address this limitation and propose an approach to significantly improve the accuracy of code localization in programming screencasts, leading to a more precise code extraction. Our approach uses a Convolutional Neural Network to automatically predict the exact location of code in an image. We evaluated our approach on a set of frames extracted from 450 screencasts covering Java, C#, and Python programming topics. The results show that our approach is able to detect the area containing the code with 94% accuracy and that our approach significantly outperforms previous work. We also show that applying OCR on the code area identified by our approach leads to a 97% match with the ground truth on average, compared to only 31% when OCR is applied to the entire frame.

关键词： programming video tutorials Software documentation Source code Deep learning video mining

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VID2XML: Automatic Extraction of a Complete XML Data From Mobile programming Screencasts

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第4期49卷 1726-1740页

作者： Alahmadi, Mohammad D. Univ Jeddah Coll Comp Sci & Engn Dept Software Engn Jeddah 21959 Saudi Arabia

Developers often refer to video-hosting online platforms to find screencasts that provide a step-by-step guide to help them solve a programming task at hand or learn a new concept. More specifically, developers search for resources that help them design and implement effective mobile graphical user interfaces (GUI) using XML. Although mobile programming screencasts contain a vast amount of XML data at developers' disposal, they cannot be easily found and copied-pasted due to the image nature of videos. Given that the most common task developers perform online is copy-pasting, mobile programming screencasts must support that and be complemented with XML data in a textual format. To overcome this challenge and aid developers, this paper presents vid2XML, which is a three-phase approach that leverages both visual and textual information of video frames to locate XML region in video frames, locate the currently opened file, and extract XML data for each file presented in video frames. We evaluated each phase of vid2XML in a comprehensive empirical evaluation on videos collected from YouTube. The results reveal that vid2XML is able to accurately (i) locate XML regions, outperforming four previous work, (ii) locate the bounding box of the selected file, and (iii) extract, fix, and merge XML data for each file opened/created in a video.

关键词： videos XML Data mining programming Codes Feature extraction Optical character recognition software programming screencasts computer vision Android programming programming video tutorials video mining

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GUI-focused Overviews of Mobile Development videos 20

GUI-focused Overviews of Mobile Development Videos

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42nd ACM/IEEE International Conference on Software Engineering - Companion Proceedings (ICSE-Companion) / 42nd ACM/IEEE International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Alahmadi, Mohammad Malkadi, Abdulkarim Haiduc, Sonia Florida State Univ Tallahassee FL 32306 USA Univ Jeddah Jeddah Saudi Arabia Jazan Univ Jizan Saudi Arabia

ISBN: (纸本)9781450371223

The need for mobile applications and mobile programming is increasing due to the continuous rise in the pervasiveness of mobile devices. Developers often refer to video programming tutorials to learn more about mobile programming topics. To find the right video to watch, developers typically skim over several videos, looking at their title, description, and video content in order to determine if they are relevant to their information needs. Unfortunately, the title and description do not always provide an accurate overview, and skimming over videos is time-consuming and can lead to missing important information. We propose a novel approach that locates and extracts the GUI screens showcased in a video tutorial, then selects and displays the most representative ones to provide a GUI-focused overview of the video. We believe this overview can be used by developers as an additional source of information for determining if a video contains the information they need. To evaluate our approach, we performed an empirical study on iOS and Android programming screencasts which investigates the accuracy of our automated GUI extraction. The results reveal that our approach can detect and extract GUI screens with an accuracy of 94%.

关键词： programming video tutorials Mobile development Software documentation Deep learning video mining

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UI Screens Identification and Extraction from Mobile programming Screencasts 28

UI Screens Identification and Extraction from Mobile Program...

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28th IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Alahmadi, Mohammad Khormi, Abdulkarim Haiduc, Sonia Florida State Univ Tallahassee FL 32306 USA Univ Jeddah Jeddah Saudi Arabia Jazan Univ Jizan Saudi Arabia

ISBN: (纸本)9781450379588

Mobile applications demand is on the rise, leading to more programmers learning to develop or having to maintain this kind of programs. Developers often refer to online resources to find inspiration or answers to questions they have about mobile programming topics and screencasts are a popular resource. However, given the multitude of screencasts available, it can be difficult to quickly comprehend which of the many videos is relevant to one's needs. We propose a novel approach, called UIScreens, which detects, extracts, and presents the most representative user interface (UI) screens embedded in mobile development screencasts. This could help developers quickly comprehend what an app displayed in a video is about, therefore saving time searching for useful videos. UIScreens has been evaluated in two empirical studies on iOS and Android programming screencasts. The first study investigates the accuracy of our UI extraction and shows that our approach is able to detect and extract UI screens with an accuracy of 94%. The second is a user study with mobile app developers, who evaluated both the accuracy and the usefulness of UIScreens. They agreed that UIScreens is accurate and extracts representative UI screens from videos. They considered that the extracted UI screens are useful for understanding what a video is about and if it is relevant to a search. Our approach has been implemented as a free online tool.

关键词： Program comprehension programming video tutorials Mobile development Software documentation Deep learning video mining

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A Study on the Accuracy of OCR Engines for Source Code Transcription from programming Screencasts 17

A Study on the Accuracy of OCR Engines for Source Code Trans...

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IEEE/ACM 17th International Conference on Mining Software Repositories (MSR)

作者： Khormi, Abdulkarim Alahmadi, Mohammad Haiduc, Sonia Florida State Univ Tallahassee FL 32306 USA Jazan Univ Jizan Saudi Arabia Univ Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9781450375177

programming screencasts can be a rich source of documentation for developers. However, despite the availability of such videos, the information available in them, and especially the source code being displayed is not easy to find, search, or reuse by programmers. Recent work has identified this challenge and proposed solutions that identify and extract source code from video tutorials in order to make it readily available to developers or other tools. A crucial component in these approaches is the Optical Character Recognition (OCR) engine used to transcribe the source code shown on screen. Previous work has simply chosen one OCR engine, without consideration for its accuracy or that of other engines on source code recognition. In this paper, we present an empirical study on the accuracy of six OCR engines for the extraction of source code from screencasts and code images. Our results show that the transcription accuracy varies greatly from one OCR engine to another and that the most widely chosen OCR engine in previous studies is by far not the best choice. We also show how other factors, such as font type and size can impact the results of some of the engines. We conclude by offering guidelines for programming screencast creators on which fonts to use to enable a better OCR recognition of their source code, as well as advice on OCR choice for researchers aiming to analyze source code in screencasts.

关键词： Optical Character Recognition Code Extraction programming video tutorials Software documentation video mining

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