Automatic classification of web images as UML static diagrams using machine learning techniques

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.This research has received funding from the CRYSTAL project – Critical System Engineering Acceleration (European Union’s Seventh Framework Program, FP7/2007-2013, ARTEMIS Joint Undertaking grant agreement n° 332830); and from the AMASS project – Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems (H2020-ECSEL grant agreement nº 692474; Spain’s MINECO ref. PCIN-2015-262)

Exploring the Applicability of Low‑Shot Learning in Mining Software Repositories

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

Generating collaborative systems for digital libraries: A model-driven approach

This is an open access article shared under a Creative Commons Attribution 3.0 Licence (http://creativecommons.org/licenses/by/3.0/). Copyright @ 2010 The Authors.The design and development of a digital library involves different stakeholders, such as: information architects, librarians, and domain experts, who need to agree on a common language to describe, discuss, and negotiate the services the library has to offer. To this end, high-level, language-neutral models have to be devised. Metamodeling techniques favor the definition of domainspecific visual languages through which stakeholders can share their views and directly manipulate representations of the domain entities. This paper describes CRADLE (Cooperative-Relational Approach to Digital Library Environments), a metamodel-based framework and visual language for the definition of notions and services related to the development of digital libraries. A collection of tools allows the automatic generation of several services, defined with the CRADLE visual language, and of the graphical user interfaces providing access to them for the final user. The effectiveness of the approach is illustrated by presenting digital libraries generated with CRADLE, while the CRADLE environment has been evaluated by using the cognitive dimensions framework

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

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 pre-trained 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

Proceedings of International Workshop "Global Computing: Programming Environments, Languages, Security and Analysis of Systems"

According to the IST/ FET proactive initiative on GLOBAL COMPUTING, the goal is to obtain techniques (models, frameworks, methods, algorithms) for constructing systems that are flexible, dependable, secure, robust and efficient. The dominant concerns are not those of representing and manipulating data efficiently but rather those of handling the co-ordination and interaction, security, reliability, robustness, failure modes, and control of risk of the entities in the system and the overall design, description and performance of the system itself. Completely different paradigms of computer science may have to be developed to tackle these issues effectively. The research should concentrate on systems having the following characteristics: • The systems are composed of autonomous computational entities where activity is not centrally controlled, either because global control is impossible or impractical, or because the entities are created or controlled by different owners. • The computational entities are mobile, due to the movement of the physical platforms or by movement of the entity from one platform to another. • The configuration varies over time. For instance, the system is open to the introduction of new computational entities and likewise their deletion. The behaviour of the entities may vary over time. • The systems operate with incomplete information about the environment. For instance, information becomes rapidly out of date and mobility requires information about the environment to be discovered. The ultimate goal of the research action is to provide a solid scientific foundation for the design of such systems, and to lay the groundwork for achieving effective principles for building and analysing such systems. This workshop covers the aspects related to languages and programming environments as well as analysis of systems and resources involving 9 projects (AGILE , DART, DEGAS , MIKADO, MRG, MYTHS, PEPITO, PROFUNDIS, SECURE) out of the 13 founded under the initiative. After an year from the start of the projects, the goal of the workshop is to fix the state of the art on the topics covered by the two clusters related to programming environments and analysis of systems as well as to devise strategies and new ideas to profitably continue the research effort towards the overall objective of the initiative. We acknowledge the Dipartimento di Informatica and Tlc of the University of Trento, the Comune di Rovereto, the project DEGAS for partially funding the event and the Events and Meetings Office of the University of Trento for the valuable collaboration

Security-Driven Software Evolution Using A Model Driven Approach

High security level must be guaranteed in applications in order to mitigate risks during the deployment of information systems in open network environments. However, a significant number of legacy systems remain in use which poses security risks to the enterprise’ assets due to the poor technologies used and lack of security concerns when they were in design. Software reengineering is a way out to improve their security levels in a systematic way. Model driven is an approach in which model as defined by its type directs the execution of the process. The aim of this research is to explore how model driven approach can facilitate the software reengineering driven by security demand. The research in this thesis involves the following three phases. Firstly, legacy system understanding is performed using reverse engineering techniques. Task of this phase is to reverse engineer legacy system into UML models, partition the legacy system into subsystems with the help of model slicing technique and detect existing security mechanisms to determine whether or not the provided security in the legacy system satisfies the user’s security objectives. Secondly, security requirements are elicited using risk analysis method. It is the process of analysing key aspects of the legacy systems in terms of security. A new risk assessment method, taking consideration of asset, threat and vulnerability, is proposed and used to elicit the security requirements which will generate the detailed security requirements in the specific format to direct the subsequent security enhancement. Finally, security enhancement for the system is performed using the proposed ontology based security pattern approach. It is the stage that security patterns derived from security expertise and fulfilling the elicited security requirements are selected and integrated in the legacy system models with the help of the proposed security ontology. The proposed approach is evaluated by the selected case study. Based on the analysis, conclusions are drawn and future research is discussed at the end of this thesis. The results show this thesis contributes an effective, reusable and suitable evolution approach for software security