Variability in Multi-Tenant Enterprise Software

Enterprise software applications have changed significantly over the last decades. Increasingly, software is deployed in a central location to be accessed through the internet, instead of installing software at end-users. Having software in a central location enables multi-tenancy, where multiple customers transparently share a system’s resources. Currently, multi-tenancy is a popular way to offer functionality of a software product through the internet to numerous customers, offering many advantages to both software vendors and customers. A challenge in this domain is offering variable features to multiple customers. This dissertation provides software architecture patterns for the realization of variability in multi-tenancy settings, in the domain of online enterprise software. The results support software architects on structuring the decision making process by providing a collection of multi-tenant architecture and variability patterns, guidelines for pattern selection, and a model to setup pattern evaluation and comparison sessions. The results reported have been gathered from case studies at software companies and evaluated by experts from the software industry. With these artifacts in hand, software architects can make well-informed decisions and find appropriate patterns for their specific situation, solving the challenges involved in selecting an architecture that supports multi-tenant online enterprise software. Also, these research results contribute to academia by reporting on numerous case studies in an emerging domain and presenting a vocabulary for further and more extensive research

Understanding User Stories: Computational Linguistics in Agile Requirements Engineering

Contemporary movies like The Social Network would lead you to believe that multi-billion software companies such as Facebook are built on individual genius. In reality, complex software is created by teams of software professionals that each have their own personality profile and expertise: from highly technical software engineers to business-minded salespeople and artistic user experience experts. The challenge? The entire team needs to talk about and agree on what piece of the software puzzle to create next. To facilitate and capture discussion on new software to be built, 50% of software companies have adopted a lightweight requirements approach called user stories. Despite this recent and substantial transition by industry, academic studies on user stories were few and far between at the start of Garm Lucassen’s PhD research. With this in mind, his research investigates the topic of user stories from the inside out. In the first three chapters, Garm Lucassen seeks to answer why user stories are popular and how to help practitioners in creating high-quality user stories. Prompted by the discovery that 56% of user stories made by practitioners include preventable errors and that guidelines for user stories quality significantly increase practitioner’s productivity and work deliverable quality, Garm proposes the Quality User Story framework and accompanying natural language processing tool Automatic Quality User Story Artisan (https://aqusa.nl/). By taking advantage of the concise and well-structured nature of high-quality user stories, AQUSA detects a subset of QUS’ quality defects with 92% recall and 77% precision. Thanks to this state-of-the-art accuracy, This accuracy has prompted software companies and universities in the Netherlands, Switzerland, Portugal and the United States to adopt this new approach to user story quality in their day-to-day work and teaching. The next three chapters focus on how to help practitioners in fully achieving the title of the dissertation: “Understanding User Stories’”. The research shows that it is possible to take advantage of computational linguistics techniques to extract and visualize the most important concepts from a collection of user stories with up to 96% recall and precision. Initial application by practitioners has shown that the freely accessible Interactive Narrator tool (https://interactivenarrator.science.uu.nl/) supports quickly analyzing and discussing new software to be built

Analyzing Social Influence through Social Media: A Structured Literature Review

The emergence of social media enables billions of people to share their content and in doing so they influence others and are being influenced themselves. This virtual environment provides a new perspective for the current social influence theories. In this study, the state-of-the-art literature on social influence through social media is reviewed. We find that social influence metrics, influence maximization, mobilization, Word-Of-Mouth and Online Reputation Management are important trends in this field of research. Social influence is shown to have a big impact in social media, but the best way to measure, maximize and coordinate this influence is still to be found. Building on the analyzed literature, we present the Online Social Influence Model, which shows the steps that are necessary to manage social influence through social media. The current study can be valuable to both researchers and practitioners, by providing a starting point for further research and identifying opportunities to improve marketing practices

ITMViz: Interactive Topic Modeling for Source Code Analysis

Topic modeling has seen a surge in use for software comprehension. Although the models inferred from the source code are a great source of knowledge, they fail to fully capture the conceptual relationships between the topics. Here we investigate the use of interactive topic modeling for source code analysis by feeding-in information from the end-users, including developers and architects, to refine the inferred topic models. We have implemented a web-based toolkit called ITMViz to provide support to interpret the topic models, and use the results to cluster modules together. A medium-sized Java project is used to evaluate our approach in understanding the software system

Towards healthcare business intelligence in long-term care: an explorative case study in the Netherlands

This research contributes to the domain of long-term care by exploring knowledge discovery techniques based on a large dataset and guided by representative information needs to better manage both quality of care and financial spendings, as a next step towards more mature healthcare business intelligence in long-term care. We structure this exploratory research according to the steps of the CRoss Industry Standard Process for Data Mining (CRISP-DM) process. Firstly, we interview 22 experts to determine the information needs in long-term care which we, secondly, translate into 25 data mining goals. Thirdly, we perform a single case study at a Dutch long-term care institution with around 850 clients in five locations. We analyze the institution‟s database which contains information from April 2008 to April 2012 to identify patterns in incident information, patterns in risk assessment information, the relationship between risk assessments and incident information, patterns in the average duration of stay, and we identify and predict Care Intensity Package (ZZP) combinations. Fourth and finally, we position all data mining goals in a two-by-two matrix to visualize the relative importance of each goal in relation to both quality of care and financial state of care institutions

A federated information architecture for multinational clinical trials: STRIPA revisited

The Systematic Tool to Reduce Inappropriate Prescribing (STRIP) is a clinical intervention method crafted to deal with polypharmacy problems which are incurred by the concurrent use of multiple drugs. STRIP has been proven to be effective and is included in the Dutch national guideline for polypharmacy. To boost the usage of STRIP in clinical practices, a web application called the STRIP Assistant (STRIPA) was developed and further evaluated as user-friendly, efficient and effective by Dutch physicians. STRIPA has now evolved into a software tool that supports a large multinational randomized clinical trial (RCT). However, in order to successfully implement and use the application in such an RCT, several issues, including multilingual support, clinical data security, data accessibility and consistency, need to be addressed. In this paper, we present an overhauled STRIPA prototype with an lightweight data integration component that supports multinational implementations, ensures data consistency across countries, and maintains data accessibility and security. The component includes a high-level information architecture, data models redesigned to generalize data entities from all countries, and the ETL processes that integrate diverse data sources and transfer data between databases. Technical features of the application are tested during the implementation, and it also is about to be evaluated empirically as part of the RCT across four European countries

Transitioning to a data driven mental health practice: collaborative expert sessions for knowledge and hypothesis finding

The surge in the amount of available data in health care enables a novel, exploratory research approach that revolves around finding new knowledge and unexpected hypotheses from data instead of carrying out well-defined data analysis tasks. We propose a specification of the Cross Industry Standard Process for Data Mining (CRISP-DM), suitable for conducting expert sessions that focus on finding new knowledge and hypotheses in collaboration with local workforce. Our proposed specification that we name CRISP-IDM is evaluated in a case study at the psychiatry department of the University Medical Center Utrecht. Expert interviews were conducted to identify seven research themes in the psychiatry department, which were researched in cooperation with local health care professionals using data visualization as a modeling tool. During 19 expert sessions, two results that were directly implemented and 29 hypotheses for further research were found, of which 24 were not imagined during the initial expert interviews. Our work demonstrates the viability and benefits of involving work floor people in the analyses and the possibility to effectively find new knowledge and hypotheses using our CRISP-IDM method

Smart Tales: An Awareness Game for Ambient Assisted Living

Despite the progress in ambient assisted living (AAL), the general audience is still mostly unaware of this term as well as of its purpose, enabling technologies, and potential. As a consequence, there are often misconceptions about AAL and smart homes, and the acceptance of AAL technologies is still too low. To cope with these problems, this paper presents a publicly available awareness game called Smart Tales, whose goal is to enhance the familiarity of its players with the notion and core concepts of AAL. In Smart Tales, the player has the role of an assisted patient in a smart home, and gets to learn about AAL and its technologies while trying to cheat the sensors that are placed in the house. In addition to presenting the design of the game following the Serious Games Design Assessment framework from the literature, we present results on engagement and learning that we obtained through a formative evaluation with ten users