Testen van software is een speerpunt in onze opleiding Software Engineering. In de propedeusefase wordt de testgedreven software-ontwikkeling geoefend. De student wordt aangeleerd software met testen at te leveren. Als onderdeel van de toetsing werd een performance-assessment ontwikkeld, dat de mogelijkheid biedt modelleren, programmeren en testen integraal te toetsen. Studenten blijken deze nieuwe toetsvorm positief te waarderen. In het kader van competentiegericht onderwijs is dit performance-assessment een waardevolle toevoeging.
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Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
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Author-supplied abstract: Developing large-scale complex systems in student projects is not common, due to various constraints like available time, student team sizes, or maximal complexity. However, we succeeded to design a project that was of high complexity and comparable to real world projects. The execution of the project and the results were both successful in terms of quality, scope, and student/teacher satisfaction. In this experience report we describe how we combined a variety of principles and properties in the project design and how these have contributed to the success of the project. This might help other educators with setting up student projects of comparable complexity which are similar to real world projects.
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The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.
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In the fall of 1999, we started, the Integrated Product Development- Collaborative Engineering ( IPD-CE) project as a first pilot. We experimented with modern communication technology in order to find useful tools for facilitating the cooperative work and the contacts of all the participants. Teams have been formed with engineering students from Lehigh University in the US, the Fontys University in Eindhoven, The Netherlands and from the Otto-von-Guericke University in Magdeburg, Germany. In the fall of 2000 we continued and also cooperated with the Finnish Oulu Polytechnic. It turned out that group cohesion stayed low (students did not meet in real life), and that Internet is not mature enough yet for desktop video conferencing. Chatting and email were in these projects by far the most important communication media. We also found out that the use of a Computer Support for Cooperative Work (CSCW) server is a possibility for information interchange. The server can also be used as an electronic project archive. Points to optimise are: 1. We didn't fully match the complete assignments of the groups; 2. We allowed the groups to divide the work in such parts that those were developed and prototyped almost locally; 3. We haven't guided the fall 2000 teams strong enough along our learning curve and experiences from previous groups. 4. We didn't stick strong enough to the, by the groups developed, protocols for email and chat sessions. 5. We should facilitate video conferencing via V-span during the project to enhance the group performance and commitment.
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In the fall of 1999, an international integrated product development pilot project based on collaborative engineering was started with team members in two international teams from the United States, The Netherlands and Germany. Team members interacted using various Internet capabilities, including, but not limited to, ICQ (means: I SEEK YOU, an internet feature which immediately detects when somebody comes "on line"), web phones, file servers, chat rooms and Email along with video conferencing. For this study a control group with all members located in the USA only also worked on the same project.
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Agile software development has evolved into an increasingly mature software development approach and has been applied successfully in many software vendors’ development departments. In this position paper, we address the broader agile service development. Based on method engineering principles we define a framework that conceptualizes an operational way of working for the development of services, emphatically taking into account agility. As a first level of agility, the framework contains situational project factors that influence the choice of method fragments; secondly, increased agility is proposed by describing and operationalizing these method fragments not as imperative steps or activities, but instead by means of sets of minimally specified, declarative rules that determine the context and constraints within which goals are to be reached. This approach borrows concepts from rules management, organizational patterns, and game design theory. Keywordsmethod engineering–agile service development–business rules–business rules management–product management–game design
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Author supplied: Teaching software architecture (SA) in a bachelor computer science curriculum can be challenging, as the concepts are on a high abstraction level and not easy to grasp for students. Good techniques and tools that help with addressing the challenging SA aspects in a didactically responsible way are needed. In this tool demo we show how we used the software architecture compliance checking tool HUSACCT for addressing various concepts of SA in our courses on software architecture. The students were introduced to architectural reconstruction and architecture compliance checking, which helped them to gain important insights in aspects such as the relation between architectural models and code and the specification of dependency relations between architecture elements as concrete rules.
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Recently, the job market for Artificial Intelligence (AI) engineers has exploded. Since the role of AI engineer is relatively new, limited research has been done on the requirements as set by the industry. Moreover, the definition of an AI engineer is less established than for a data scientist or a software engineer. In this study we explore, based on job ads, the requirements from the job market for the position of AI engineer in The Netherlands. We retrieved job ad data between April 2018 and April 2021 from a large job ad database, Jobfeed from TextKernel. The job ads were selected with a process similar to the selection of primary studies in a literature review. We characterize the 367 resulting job ads based on meta-data such as publication date, industry/sector, educational background and job titles. To answer our research questions we have further coded 125 job ads manually. The job tasks of AI engineers are concentrated in five categories: business understanding, data engineering, modeling, software development and operations engineering. Companies ask for AI engineers with different profiles: 1) data science engineer with focus on modeling, 2) AI software engineer with focus on software development , 3) generalist AI engineer with focus on both models and software. Furthermore, we present the tools and technologies mentioned in the selected job ads, and the soft skills. Our research helps to understand the expectations companies have for professionals building AI-enabled systems. Understanding these expectations is crucial both for prospective AI engineers and educational institutions in charge of training those prospective engineers. Our research also helps to better define the profession of AI engineering. We do this by proposing an extended AI engineering life-cycle that includes a business understanding phase.
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Nowadays companies need higher educated engineers to develop their competences to enable them to innovate. This innovation competence is seen as a remedy for the minor profitable business they do during the financial crises. Innovation is an element to be developed on the one hand for big companies as well as for small-and-medium sized companies through Europe to overcome this crisis. The higher education can be seen as an institution where youngsters, coming from secondary schools, who choose to learn at higher education to realize their dream, what they like to become in the professional world. The tasks of the Universities of applied Sciences are to prepare these youngsters to become starting engineers doing their job well in the companies. Companies work for a market, trying to manufacture products which customers are willing to pay for. They ask competent employees helping achieving this goal. It is important these companies inform the Universities of applied Sciences in order to modify their educational program in such a way that the graduated engineers are learning the latest knowledge and techniques, which they need to know doing their job well. The Universities of applied Sciences of Oulu (Finland) and Fontys Eindhoven (The Netherlands) are working together to experience possibilities to qualify their students on innovation development in an international setting. In the so-called: ‘Invention Project’, students are motivated to find their own invention, to design it, to prepare this idea for prototyping and to really manufacture it. Organizing the project, special attention is given to communication protocol between students and also between teachers. Students have meetings on Thursday every week through Internet connection with the communication program OPTIMA, which is provided by the Oulu University. Not only the time difference between Finland and the Netherlands is an issue to be organized also effective protocols how to provide each other relevant information and also how to make in an effective way decisions are issues. In the paper the writers will present opinions of students, teachers and also companies in both regions of Oulu and Eindhoven on the effectiveness of this project reaching the goal students get more experienced to set up innovative projects in an international setting. The writers think this is an important and needed competence for nowadays young engineers to be able to create lucrative inventions for companies where they are going to work for. In the paper the writers also present the experiences of the supervising conditions during the project. The information found will lead to success-factors and do’s and don’ts for future projects with international collaboration.
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