Automatic classification of web images as UML diagrams

Moreno, Valentín Molina; Génova, Gonzalo; Alejandres, Manuela; Fraga, Anabel

doi:10.1145/2934732.2934739

Cited by 10 publications

(10 citation statements)

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“…First, we obtained a sample of nearly 19,000 images from the web (exactly 18,899 items) that resulted from queries in Google Images involving the terms "uml diagram"; then a team of experts manually classified the images as UML static diagrams (Yes/No). The reader is referred to our previous conference paper [9] for the details regarding the criteria followed in this manual classification. Second, we analyzed the main graphical characteristics of images that represent diagrams, such as grayscale histogram, color histogram, elementary geometric forms detected with image recognition techniques (especially rectangles), and so on.…”

Section: Motivation and Outline Of Methodsmentioning

confidence: 99%

“…In our previous conference paper [9], we explained with many examples how a small set of simple graphical features that are very easy to compute can be effectively and efficiently used to classify a given image as a diagram, so that we can avoid the more time consuming identification of other features, such as text or complex geometrical forms. These simple features include the number of different gray tones and colors (histograms), as well as the number of vertical and horizontal straight polylines (solid or dashed), a special case of which is the rectangle, by far the most common geometrical form in UML diagrams.…”

Section: Analysis Of Graphical Characteristics Of Diagrammatic Imagesmentioning

confidence: 99%

“…This paper is an extended version of a previous paper presented at the 4th Spanish Conference in Information Retrieval [9]. That version was constrained due to its rather short length, and the authors could not explain in sufficient detail the grounds of our contribution.…”

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Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques

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Our purpose in this research is to develop a method to automatically and efficiently classify web images as Unified Modeling Language (UML) static diagrams, and to produce a computer tool that implements this function. The tool receives a bitmap file (in different formats) as an input and communicates whether the image corresponds to a diagram. For pragmatic reasons, we restricted ourselves to the simplest kinds of diagrams that are more useful for automated software reuse: computer-edited 2D representations of static diagrams. The tool does not require that the images are explicitly or implicitly tagged as UML diagrams. The tool extracts graphical characteristics from each image (such as grayscale histogram, color histogram and elementary geometric forms) and uses a combination of rules to classify it. The rules are obtained with machine learning techniques (rule induction) from a sample of 19,000 web images manually classified by experts. In this work, we do not consider the textual contents of the images. Our tool reaches nearly 95% of agreement with manually classified instances, improving the effectiveness of related research works. Moreover, using a training dataset 15 times bigger, the time required to process each image and extract its graphical features (0.680 s) is seven times lower.

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Section: Motivation and Outline Of Methodsmentioning

confidence: 99%

Section: Analysis Of Graphical Characteristics Of Diagrammatic Imagesmentioning

confidence: 99%

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Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques

et al. 2020

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“…Moreno et al conducted a similar study to classify web images as UML and non-UML class diagrams using a rule based approach. By extracting features from the images, in a corpus of 19000 web images, their algorithm reached an accuracy of 95% [9].…”

Section: Related Workmentioning

confidence: 99%

Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts

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Linstead

2020

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Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for another task.Here we explore the applicability of transfer learning utilizing models pretrained on non-software engineering data applied to the problem of classifying software UML diagrams. Our experimental results show training reacts positively to transfer learning as related to sample size, even though the pre-trained model was not exposed to training instances from the software domain. We contrast the transferred network with other networks to show its advantage on different sized training sets, which indicates that transfer learning is equally effective to custom deep architectures when large amounts of training data is not available.

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“…In 2015 Hjaltason et al [19] utilized support vector machines (SVMs) trained on a corpus of 1300 UML and non-UML images, producing an average classification accuracy of 92.05%. Moreno et al addressed the high processing times in these previous approaches by proposing a rule based approach [20]. These rules were extracted from a corpus of nearly 19,000 web images (UML and non-UML), and used a training set of 715 images to identify UML images with a 95% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Exploring the applicability of low-shot learning in mining software repositories

2019

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IntroductionIn the past couple of years, applications of deep learning to mining software repositories have grown in number and diversity of methods [1][2][3][4][5]. Fueled in part by easy-touse libraries and graphics processing unit (GPU) computing, deep architectures have facilitated new avenues for research, often producing results that far surpass previous techniques. However, despite their advantages, the huge amount of labeled truth data traditionally required to train deep architectures for classification tasks, as well as the computational time required to iteratively improve such models, remains a substantial bottleneck [6]. As a result, some researchers are forced to turn away from deep architectures, despite the fact that for certain tasks (like image analysis and computer vision), deep learning consistently outperforms alternative algorithms and methodologies.Low-shot learning refers to the practice of training machine learning models, including deep neural networks, using far fewer samples of each classification category than what is typically standard practice. In the extreme case, training data consists of only one instance for each target class, which is known as one-shot learning [7]. These approaches Abstract Background: Despite the well-documented and numerous recent successes of deep learning, the application of standard deep architectures to many classification problems within empirical software engineering remains problematic due to the large volumes of labeled data required for training. Here we make the argument that, for some problems, this hurdle can be overcome by taking advantage of low-shot learning in combination with simpler deep architectures that reduce the total number of parameters that need to be learned. Findings:We apply low-shot learning to the task of classifying UML class and sequence diagrams from Github, and demonstrate that surprisingly good performance can be achieved by using only tens or hundreds of examples for each category when paired with an appropriate architecture. Using a large, off-the-shelf architecture, on the other hand, doesn't perform beyond random guessing even when trained on thousands of samples. Conclusion:Our findings suggest that identifying problems within empirical software engineering that lend themselves to low-shot learning could accelerate the adoption of deep learning algorithms within the empirical software engineering community. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Automatic classification of web images as UML diagrams

Cited by 10 publications

References 14 publications

Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques

Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques

Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts

Exploring the applicability of low-shot learning in mining software repositories

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