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16th IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA)

作者： Schinle, Markus Erler, Christina Schneider, Timon Plewnia, Joana Stork, Wilhelm FZI Res Ctr Informat Technol Embedded Syst & Sensors Engn Karlsruhe Germany Karlsruhe Inst Technol Inst Informat Proc Technol Karlsruhe Germany

ISBN: (纸本)9781665419147

Following the paradigm of precision medicine, the combination of health data and Machine Learning (ML) is promising to improve the quality of healthcare services e.g. by making diagnoses and therapeutic interventions as early and precise as possible. The implementation of this approach requires sufficient amounts of data with a high quality along the data life cycle. This goal seems recently achievable through the implementation of several national digital health strategies and the hope of a growing societal acceptance of digital health applications due to the implications of the COVID-19 pandemic. But, a collection of tools and methods is missing, which supports developers to use data as driving force of the development process. Due to the iterative nature of software application development, it allows the continuous improvement through the integration of collected digital data. We refer to this as a data-driven approach and identify steps to take and tools for its implementation. Associated challenges and opportunities of this translational approach are outlined on the example of a self-developed dementia screening application. Using our methodology, we compared multiple ML algorithms based on the data of an observational study (n=55) and achieved models with sensitivity up to 89% for unhealthy participants within this use case.

关键词： data-driven development Machine Learning Digital Psychometrics Dementia Mobile Screening Ambulatory Assessment

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Usability and Adoption of Graphical Tools for data-driven development 24

Usability and Adoption of Graphical Tools for Data-Driven De...

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Conference on Mensch und Computer (MuC)

作者： Weber, Thomas Mayer, Sven Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9798400709982

Software development of modern, data-driven applications still relies on tools that use interaction paradigms that have remained mostly unchanged for decades. While rich forms of interactions exist as an alternative to textual command input, they find little adoption in professional software creation. In this work, we compare graphical programming using direct manipulation to the traditional, textual way of creating data-driven applications to determine the benefits and drawbacks of each. In a between-subjects user study (N=18), we compared developing a machine learning architecture with a graphical editor to traditional code-based development. While qualitative and quantitative measures show general benefits of graphical direct manipulation, the user's subjective perception does not always match this. Participants were aware of the possible benefits of such tools but were still biased in their perception. Our findings highlight that alternative software creation tools cannot just rely on good usability but must emphasize the demands of their specific target group, e.g., user control and flexibility, if they want long-term benefits and adoption.

关键词： data-driven software data-driven development graphical programming interaction paradigms neural network machine learning

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The Potential and Issues in data-driven development of Web Personas 41

The Potential and Issues in Data-Driven Development of Web P...

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41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Mijac, Tea Jadric, Mario Cukusic, Maja Univ Split Fac Econ Cvite Fiskovica 5 Split 21000 Croatia

ISBN: (纸本)9789532330953

The concept of personas that represent the key audience segments of a service, product, site, or a brand has been widely used in marketing. As of lately, personas are often used in digital services design and development. However, there are some frequent errors and issues that designers and developers come across in the process of developing realistic personas. Namely, personas are sometimes developed based on irrelevant data (e.g. statistically insignificant dataset), or have been developed based on assumptions, inadequate analyses or, most often intuitively. Another issue is that once created, personas cannot be easily updated. There is great potential in using big data technologies to tackle these issues since it enables analyses of large amounts of data to gain insights into real user behavior patterns that lead to better business decisions. This paper explores the possibilities for developing data-driven web personas based on real user-data with the aim to save time in comparison with current data collection methods and also to enable easier updates. The preliminary study is presented using a dataset from an e-business site. The research results highlight the great potential of developing data-driven web personas based entirely on interactions between real users and a webshop, but also a number of identified issues in the process.

关键词： data-driven development web persona development big data

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A data-driven approach for recommending UI element layout

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Science China(Information Sciences) 2020年第9期63卷 61-63页

作者： Yonghao LONG Xiangping CHEN Yuting XIE School of Information Science and Technology National Engineering Research Center of Digital LifeSun Yat-sen University Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and Design Sun Yat-sen University

Dear editor,Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. GUI design plays an important role in offering a smooth user experience. The complexity of the apps often depends on the user interface (UI)[1]with minor data processing or data processing delegates to the backend component.

关键词： UI development Software mining Layout recommendation data-driven development Intelligent Software development

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The Online Controlled Experiment Lifecycle

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IEEE SOFTWARE 2020年第2期37卷 60-67页

作者： Fabijan, Aleksander Dmitriev, Pavel Olsson, Helena Bosch, Jan Malmo Univ Malmo Sweden Outreach Data Sci Seattle WA USA Malmo Univ Dept Comp Sci & Media Technol Malmo Sweden Chalmers Univ Technol Software Engn Gothenburg Sweden

Unlike other techniques for learning from customers, online controlled experiments (OCEs) establish an accurate and causal relationship between a change and the impact observed. We show that OCEs help optimize infrastructure needs and aid in project planning and measuring team efforts. We conclude that product development should fully integrate the experiment lifecycle to benefit from the OCEs.

关键词： Measurement Companies Software Computer science Product development Media Planning data-driven development A B tests online controlled experiments experiment lifecycle

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Improving official statistics in emerging markets using machine learning and mobile phone data

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EPJ data SCIENCE 2017年第1期6卷 3页

作者： Jahani, Eaman Sundsoy, Pal Bjelland, Johannes Bengtsson, Linus Pentland, Alex 'Sandy' de Montjoye, Yves-Alexandre MIT Inst Data Syst & Soc 50 Ames StE18-407A Cambridge MA 02142 USA Telenor Res Snaroyveien 30 N-1331 Oslo Norway Flowminder Roslagsgatan Stockholm Sweden Karolinska Inst Dept Publ Hlth Sci SE-17177 Stockholm Sweden MIT Media Lab 20 Ames StE14 Cambridge MA 02142 USA Imperial Coll London Dept Comp 180 Queens Gate London SW7 2RH England Imperial Coll London Data Sci Inst William Penney Lab London SW7 2RH England

Mobile phones are one of the fastest growing technologies in the developing world with global penetration rates reaching 90%. Mobile phone data, also called CDR, are generated everytime phones are used and recorded by carriers at scale. CDR have generated groundbreaking insights in public health, official statistics, and logistics. However, the fact that most phones in developing countries are prepaid means that the data lacks key information about the user, including gender and other demographic variables. This precludes numerous uses of this data in social science and development economic research. It furthermore severely prevents the development of humanitarian applications such as the use of mobile phone data to target aid towards the most vulnerable groups during crisis. We developed a framework to extract more than 1400 features from standard mobile phone data and used them to predict useful individual characteristics and group estimates. We here present a systematic cross-country study of the applicability of machine learning for dataset augmentation at low cost. We validate our framework by showing how it can be used to reliably predict gender and other information for more than half a million people in two countries. We show how standard machine learning algorithms trained on only 10,000 users are sufficient to predict individual's gender with an accuracy ranging from 74.3 to 88.4% in a developed country and from 74.5 to 79.7% in a developing country using only metadata. This is significantly higher than previous approaches and, once calibrated, gives highly accurate estimates of gender balance in groups. Performance suffers only marginally if we reduce the training size to 5,000, but significantly decreases in a smaller training set. We finally show that our indicators capture a large range of behavioral traits using factor analysis and that the framework can be used to predict other indicators of vulnerability such as age or socio-economic status. Mobile

关键词： mobile phone metadata machine learning data-driven development vulnerable populations

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A theory of factors affecting continuous experimentation (FACE)

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EMPIRICAL SOFTWARE ENGINEERING 2024年第1期29卷 21-21页

作者： Ros, Rasmus Bjarnason, Elizabeth Runeson, Per Lund Univ Lund Sweden

ContextContinuous experimentation (CE) is used by many companies with internet-facing products to improve their business models and software solutions based on user data. Some companies deliberately adopt a systematic experiment-driven approach to software development while some companies use CE in a more ad-hoc *** goal of this study is to identify factors for success in CE that explain the variations in the utility and efficacy of CE between different *** conducted a multi-case study of 12 companies involved with CE and performed 27 interviews with practitioners at these companies. Based on that empirical data, we then built a theory of factors at play in *** introduce a theory of Factors Affecting Continuous Experimentation (FACE). The theory includes three factors, namely 1) processes and infrastructure for CE, 2) the user problem complexity of the product offering, and 3) incentive structures for CE. The theory explains how these factors affect the effectiveness of CE and its ability to achieve problem-solution and product-market *** theory may inspire practitioners to assess an organisation's potential for adopting CE and to identify factors that pose challenges in gaining value from CE practices. Our results also provide a basis for defining practitioner guidelines and a starting point for further research on how contextual factors affect CE and how these may be mitigated.

关键词： Continuous experimentation data-driven development A/B testing Theory building Multi-case study Empirical research

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Experimentation that Matters: A Multi-case Study on the Challenges with A/B Testing 8th

Experimentation that Matters: A Multi-case Study on the Chal...

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8th International Conference on Software Business (ICSOB)

作者： Olsson, Helena Holmstrom Bosch, Jan Fabijan, Aleksander Malmo Univ Fac Technol & Soc Nordenskioldsgatan 1 S-21119 Malmo Sweden Chalmers Univ Technol Dept Comp Sci & Engn Horselgangen 11 S-41296 Gothenburg Sweden

ISBN: (纸本)9783319691916;9783319691909

From having been exclusive for companies in the online domain, feature experiments are becoming increasingly important for software-intensive companies also in other domains. Today, companies run experiments, such as e.g. A/B tests, to optimize product performance and to learn about user behaviors, as well as to guide product development and innovation. However, although experimentation with customers has become an effective mechanism to improve products and increase revenue, companies struggle with how to leverage the results of the experiments they run. In this paper, we study the reasons for this and we identify three key challenges that make feature experimentation a difficult task. Our research reveals the following challenges: (1) the impact of experiments doesn't scale, (2) business KPIs and team level metrics are not aligned and (3) it is unclear if the available solutions are applicable across domains.

关键词： data-driven development Feature experimentation A/B testing

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From Efficiency to Effectiveness: Delivering Business Value Through Software 1

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10th International Conference on Software Business (ICSOB)

作者： Bosch, Jan Chalmers Univ Technol Dept Comp Sci & Engn S-41296 Gothenburg Sweden

ISBN: (数字)9783030337421

ISBN: (纸本)9783030337421;9783030337414

Connected products and DevOps allow for a fundamentally different way of working in R&D. Rather than focusing on efficiency of teams, often expressed in terms of flow and number of features per sprint, we are now able to focus on the effectiveness of R&D as expressed in the amount of value created per unit of R&D. We have developed several solutions, such as HYPEX, HoliDev and hierarchical value models, but companies still experience challenges. In this paper, we provide an overview of the trends driving the transition to focusing on effectiveness, discuss the challenges that companies experience as well as the requirements for a successful transformation.

关键词： Efficiency Effectiveness data-driven development AI-driven development

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Dealing with data: Bringing Order to Chaos 50

Dealing with Data: Bringing Order to Chaos

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50th Euromicro Conference on Software Engineering and Advanced Applications

作者： Olsson, Helena Holmstrom Bosch, Jan Malmo Univ Comp Sci & Media Technol Malmo Sweden Chalmers Univ Technol Comp Sci & Engn Gothenburg Sweden

ISBN: (纸本)9798350380279;9798350380262

data is key for rapid and continuous delivery of customer value. By collecting data from products in the field, companies in the embedded systems domain can measure and monitor product performance and they get the opportunity to provide customers with insights and data-driven services. However, while the notion of data-driven development is not new, embedded systems companies are facing a situation in which data volumes are growing exponentially and this is not without its challenges. Suddenly, the cost of collecting, storing and processing data becomes a concern and while there is prominent research on different aspects of data-driven development, there is little guidance for how to reason about business value versus costs of data. In this paper, we present findings from case study research conducted in close collaboration with four companies in the embedded systems domain. The contribution of this paper is a framework that provides a holistic understanding of the multiple dimensions that need to be considered when reasoning about business value versus cost of collecting, storing and processing data.

关键词： data-driven development data practices data collection storage and processing business value

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