Ivan Budiselic

Formal modeling of RESTful systems using finite-state machines

Representational State Transfer (REST), as an architectural style for distributed hypermedia systems, enables scalable operation of the World Wide Web (WWW) and is the foundation for its future evolution. However, although described over 10 years ago, no comprehensive formal model for representing RESTful systems exists. The lack of a formal model has hindered understanding of the REST architectural style and the WWW architecture, consequently limiting Web engineering advancement. In this paper we present a model of RESTful systems based on a finite-state machine formalism. We show that the model enables intuitive formalization of many RESTs constraints, including uniform interface, stateless client-server operation, and code-on-demand execution. We describe the models mapping to a system-level view of operation and apply the model to an example Web application. Finally, we outline benefits of the model, ranging from better understanding of REST to designing frameworks for RESTful system development.

RegExpert: A Tool for Visualization of Regular Expressions

In recent years, tools for computer aided learning have become widespread on all levels of education. These tools are used as replacements or additions to traditional methods of instruction, in order to reduce the time students require to absorb the taught course. On the other hand, these tools ease the burden of course lecturers when preparing complex examples since these can be created and executed in real time and then analyzed in class. To increase the interactivity of computer science education, we have developed RegExpert, a tool for regular language manipulation and visualization. The main goal of the RegExpert tool is to simplify and visually present complex concepts and mathematical models of automata theory fundamentals. RegExpert converts user defined or automatically generated regular expression to an equivalent NFA-epsilon (nondeterministic finite automaton with epsilon transitions) and presents it to the user in a form of state diagram. In this paper, we present the structure and implementation of the tool, as well as the ideas for future development.

Consumerized and peer-tutored service composition

We present peer-tutor recommender system for service composition.Peer-tutoring process is based on socially-intelligent computing.Expert peer tutors are identified by analysis of existing service compositions.The peer tutoring system was implemented and evaluated through a consumer study.Results show significant improvements in terms of consumer performance and QoE. With continued development towards the Internet of Things, services are making their way from enterprise solutions to our offices and homes. This process is a major driving force in consumerization of IT, because sustainable application development at this scale will not be possible without direct involvement and innovation from consumers themselves. In this paper, we present our work on consumerization of service composition tools. First, we describe how consumer-facing services can be presented in a usable and intuitive way. Then, combining social computing with machine intelligence, we define a recommender system that supports consumers in sharing their knowledge and creativity in peer-tutored service composition, thus empowering consumers to create their own applications. This system recommends consumers with the required service composition knowledge based on mining procedural knowledge stored in previously defined compositions. Once such a group of consumers is identified, social computing tools are used to allow them to share this knowledge with their peers. To demonstrate the effectiveness of this peer-tutored service composition model, we performed consumer satisfaction studies on our consumerized service composition tool Geppeto, which we extended with the described recommender system. Results show significant improvements in service composition in terms of performance and quality of experience.

A Methodology for SIP and SOAP Integration Using Application-Specific Protocol Conversion

In recent years, the ubiquitous demands for cross-protocol application access are driving the need for deeper integration between SIP and SOAP. In this article we present a novel methodology for integrating these two protocols. Through an analysis of properties of SIP and SOAP we show that integration between these protocols should be based on application-specific converters. We describe a generic SIP/SOAP gateway that implements message handling and network and storage management while relying on application-specific converters to define session management and message mapping for a specific set of SIP and SOAP communication nodes. In order to ease development of these converters, we introduce an XML-based domain-specific language for describing application-specific conversion processes. We show how conversion processes can be easily specified in the language using message sequence diagrams of the desired interaction. We evaluate the presented methodology through performance analysis of the developed prototype gateway and high-level comparison with other solutions.

Component recommendation for composite application development

We address the problem of component discovery in composition environments.A general method for component recommendation is presented.Component recommendation is based on structural analysis of compositions.A graph-based component recommender algorithm is proposed and evaluated.Results show advantages in recommender quality compared to a CF recommender. Support for component discovery has been identified as a key challenge in various forms of composite application development. In this paper, we describe a general method for component recommendation based on structural similarity of compositions. The method dynamically ranks and recommends components as a composition is incrementally developed. Recommendations are based on structural comparison of the partial composition begin developed with a database of previously completed compositions. Using this method, we define a probabilistic graph edit distance algorithm for component recommendation. We evaluate the accuracy, catalog coverage and response time of the presented algorithm and compare it to a neighborhood-based collaborative filtering approach and two simple statistical algorithms. The evaluation is performed on a Yahoo Pipes dataset and a synthetic dataset that models more complex composite applications. The results show that the proposed algorithm is competitive with the collaborative filtering algorithm in accuracy and outperforms it significantly in coverage. The results on the synthetic dataset suggest that the presented approach can be applied successfully to other composition environments where there is regularity in how components are connected.

Designing the programming assignment for a university compiler design course

This paper analyzes the key properties of a programming assignment for a university compiler design course. After an overview of popular choices used at several universities in the world, we describe the assignment given to students of the Programming Language Translation undergraduate course at the University of Zagreb, Faculty of Electrical Engineering and Computing, and provide the rationale behind several design decisions through an analysis of its evolution in recent years. While most compiler programming assignments are based on tools like lexer and parser generators, our students actually build slightly simplified versions of these tools themselves. We discuss our experiences with this programming assignment over the last three years and qualitative differences in the results between this assignment and the tool based assignment we had been using before.

Developing a text classifier with constrained development and execution time

The aim of this paper is to show that an accurate and efficient text classifier for relatively simple problem domains can be created in only a few hours of development time. The motivating example discussed in the paper is a recent HackerRank competition problem that tasked competitors with creating a classifier for questions from the popular question and answer platform StackExchange. The paper describes the key components of one solution to this problem, and briefly overviews the naive Bayes classifier that is the basis of the solution. The discussion is focused on feature selection and example representation which were the key challenges to be addressed during the development of this classifier. We also analyze the effect of the number of features on accuracy, training and classification time and the size of the resulting classifier and the representation of the training examples which were all important characteristics for the competition. The described classifier achieved slightly over 89% accuracy on the hidden question set, while the winning submission achieved around 92%.