Walking Through the Method Zoo: Does Higher Education Really Meet Software Industry Demands?

Software engineering educators are continually challenged by rapidly evolving concepts, technologies, and industry demands. Due to the omnipresence of software in a digitalized society, higher education institutions (HEIs) have to educate the students such that they learn how to learn, and that they are equipped with a profound basic knowledge and with latest knowledge about modern software and system development. Since industry demands change constantly, HEIs are challenged in meeting such current and future demands in a timely manner. This paper analyzes the current state of practice in software engineering education. Specifically, we want to compare contemporary education with industrial practice to understand if frameworks, methods and practices for software and system development taught at HEIs reflect industrial practice. For this, we conducted an online survey and collected information about 67 software engineering courses. Our findings show that development approaches taught at HEIs quite closely reflect industrial practice. We also found that the choice of what process to teach is sometimes driven by the wish to make a course successful. Especially when this happens for project courses, it could be beneficial to put more emphasis on building learning sequences with other courses

Defining and Analysing Resource Assignments in Business Processes with RAL

Business process (BP) modelling notations tend to stray their attention from (human) resource management, unlike other aspects such as control flow or even data flow. They not only offer little intuitive languages to assign resources to BP activities, but neither link BPs with the structure of the organization where they are used, so BP models can easily contain errors such as the assignment of resources that do not belong to the organizational model. In this paper we address this problem and define RAL (Resource Assignment Language), a domainspecific language explicitly developed to assign resources to the activities of a BP model. RAL makes BPs aware of organizational structures. Besides, RAL semantics is based on an OWL-DL ontology, which enables the automatic analysis of resource assignment expressions, thus allowing the extraction of information from the resource assignments, and the detection of inconsistencies and assignment conflicts

Catching up with Method and Process Practice: An Industry-Informed Baseline for Researchers

Software development methods are usually not applied by the book.companies are under pressure to continuously deploy software products that meet market needs and stakeholders\u27 requests. To implement efficient and effective development processes, companies utilize multiple frameworks, methods and practices, and combine these into hybrid methods. A common combination contains a rich management framework to organize and steer projects complemented with a number of smaller practices providing the development teams with tools to complete their tasks. In this paper, based on 732 data points collected through an international survey, we study the software development process use in practice. Our results show that 76.8% of the companies implement hybrid methods.company size as well as the strategy in devising and evolving hybrid methods affect the suitability of the chosen process to reach company or project goals. Our findings show that companies that combine planned improvement programs with process evolution can increase their process\u27 suitability by up to 5%

An Infrastructure for Cost-Effective Testing of Operational Support Algorithms Based on Colored Petri Nets

Operational support is a specific type of process mining that assists users while process instances are being executed. Examples are predicting the remaining processing time of a running insurance claim and recommending the action that minimizes the treatment costs of a particular patient. Whereas it is easy to evaluate prediction techniques using cross validation, the evaluation of recommendation techniques is challenging as the recommender influences the execution of the process. It is therefore impossible to simply use historic event data. Therefore, we present an approach where we use a colored Petri net model of user behavior to drive a real workflow system and real implementations of operational support, thereby providing a way of evaluating algorithms for operational support before implementation and a costly test using real users. In this paper, we evaluate algorithms for operational support using different user models. We have implemented our approach using Access/CPN 2.0

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages

Cassava root cross-section images

This dataset contains images of cassava root cross-sections captured by the Makerere University Artificial Intelligence Lab in conjunction with the National Crop Resources Research Institute in Uganda and the Tanzania Agricultural Research Institute in Tanzania. The images were captured during harvests conducted by cassava breeders to assess and score root necrosis for different varieties of cassava. The dataset contains 10052 images from five field trials. The images can be used to develop deep learning algorithms that can help to automate the scoring of root necrosis and to study other aspects of necrosis expression on cassava roots. We have also added a ground truth dataset of 1036 images and corresponding masks that can be used for image analysis experiments

Business Process Simulation: How to get it right

Abstract. Although simulation is typically considered as relevant and highly applicable, in reality the use of simulation is limited. Many organizations have tried to use simulation to analyze their business processes at some stage. However, few are using simulation in a structured and effective manner. This may be caused by a lack of training and limitations of existing tools, but in this paper we will argue that there are also several additional and more fundamental problems. First of all, the focus is mainly on design while managers would also like to use simulation for operational decision making (solving the concrete problem at hand rather than some abstract future problem). Second, there is limited support for using existing artifacts such as historical data and workflow schemas. Third, the behavior of resources is modeled in a rather naive manner. This paper focuses on the latter problem. It proposes a new way of characterizing resource availability. The ideas are described and analyzed using CPN Tools. Experiments show that it is indeed possible to capture human behavior in business processes in a much better way. By incorporating better resource characterizations in contemporary tools, business process simulation can finally deliver on its outstanding promise.