Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond

We present a graphical and dynamic framework for binding and execution of business) process models. It is tailored to integrate 1) ad hoc processes modeled graphically, 2) third party services discovered in the (Inter)net, and 3) (dynamically) synthesized process chains that solve situation-specific tasks, with the synthesis taking place not only at design time, but also at runtime. Key to our approach is the introduction of type-safe stacked second-order execution contexts that allow for higher-order process modeling. Tamed by our underlying strict service-oriented notion of abstraction, this approach is tailored also to be used by application experts with little technical knowledge: users can select, modify, construct and then pass (component) processes during process execution as if they were data. We illustrate the impact and essence of our framework along a concrete, realistic (business) process modeling scenario: the development of Springer's browser-based Online Conference Service (OCS). The most advanced feature of our new framework allows one to combine online synthesis with the integration of the synthesized process into the running application. This ability leads to a particularly flexible way of implementing self-adaption, and to a particularly concise and powerful way of achieving variability not only at design time, but also at runtime.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455

Using the Internet of Things to Teach Good Software Engineering Practice to High School Students

This paper describes a course to introduce high school students to software engineering in practice using the Internet Of Things (IoT). IoT devices allow students to get quick, visible results without watering down technical aspects of programming and networking. The course has three broad goals: (1) to make software engineering fun and applicable, with the aim of recruiting traditionally underrepresented groups into computing; (2) to make young students begin to approach problems with a design mindset; and (3) to show students that computer science, generally, and software engineering, specifically, is about much more than programming. The course unfolds in three segments. The first is a whirlwind introduction to a subset of IoT technologies. Students complete a specific task (or set of tasks) using each technology. This segment culminates in a “do-it-yourself” project, in which the students implement a simple IoT application using their basic knowledge of the technologies. The course’s second segment introduces software engineering practices, again primarily via hands-on practical tutorials. In the third segment of the course, the students conceive of, design, and implement a project that uses the technologies introduced in the first segment, all while being attentive to the good software engineering practices acquired in the second segment. In addition to presenting the course curriculum, the paper also discusses a first offering of the course in a threeweek summer intensive program in 2017, including assessments done to evaluate the curriculum.Cockrell School of Engineerin

EXTREME PROGRAMMING AND RATIONAL UNIFIED PROCESS – CONTRASTS OR SYNONYMS?

The agile movement has received much attention in software engineering recently. Established methodologies try to surf on the wave and present their methodologies a being agile, among those Rational Unified Process (RUP). In order to evaluate the statements we evaluate the RUP against eXtreme Programming (XP) to find out to what extent they are similar end where they are different. We use a qualitative approach, utilizing a framework for comparison. RUP is a top-down solution and XP is a bottom-up approach. Which of the two is really best in different situations has to be investigated in new empirical studies.extreme programming

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Mutation Testing as a Safety Net for Test Code Refactoring

Refactoring is an activity that improves the internal structure of the code without altering its external behavior. When performed on the production code, the tests can be used to verify that the external behavior of the production code is preserved. However, when the refactoring is performed on test code, there is no safety net that assures that the external behavior of the test code is preserved. In this paper, we propose to adopt mutation testing as a means to verify if the behavior of the test code is preserved after refactoring. Moreover, we also show how this approach can be used to identify the part of the test code which is improperly refactored