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26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Urosevic, Vladimir Andric, Marina Vukidcvic, Milan Tatsiopoulos, Christos Belit Ltd Belgrade Serbia AMIS Belgrade Serbia Univ Belgrade Fac Org Sci Belgrade Serbia

ISBN: (纸本)9781538667705;9789532900873

The global human population is aging rapidly, however, living longer does not necessarily mean living healthy, active and independent life. The emerging disruptive technologies like the Internet of Things (IoT) are proving instrumental in addressing this prominent societal challenge. Urban IoT infrastructures, designed to support the Smart City vision, enable capturing of personal data for analyzing behaviour of elderly people. Activities within the Horizon 2020 City4Age project are aimed at showing that behavioral analysis can help detect and mitigate risks of Mild Cognitive Impairment (MCI) and frailty problems of elderly. This paper presents the latest developments in extending the configurability and flexibility of the comprehensive City4Age computational model for risk detection. The proposed model extensions have demonstrated seamless adaptation to specific characteristics of various urban contexts, as well as seamless "pluggable" integration of various combined evolving and extendable parameterized algorithm implementations and methods for behaviour variation and risk recognition, based on relevant statistical and machine learning techniques.

关键词： geriatric model data-driven development flexible modelling configurability temporal analysis

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An Activity and Metric Model for Online Controlled Experiments 19th

An Activity and Metric Model for Online Controlled Experimen...

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19th International Conference on Product-Focused Software Process Improvement (PROFES)

作者： Mattos, David Issa Dmitriev, Pavel Fabijan, Aleksander Bosch, Jan Olsson, Helena Holmstrom Chalmers Univ Technol Dept Comp Sci & Engn Horselgangen 11 S-41296 Gothenburg Sweden Outreach 1441 North 34th St Seattle WA 98103 USA Malmo Univ Dept Comp Sci & Media Technol S-21119 Malmo Sweden

ISBN: (纸本)9783030036737;9783030036720

YAccurate prioritization of efforts in product and services development is critical to the success of every company. Online controlled experiments, also known as A/B tests, enable software companies to establish causal relationships between changes in their systems and the movements in the metrics. By experimenting, product development can be directed towards identifying and delivering value. Previous research stresses the need for data-driven development and experimentation. However, the level of granularity in which existing models explain the experimentation process is neither sufficient, in terms of details, nor scalable, in terms of how to increase number and run different types of experiments, in an online setting. Based on a case study of multiple products running online controlled experiments at Microsoft, we provide an experimentation framework composed of two detailed experimentation models focused on two main aspects;the experimentation activities and the experimentation metrics. This work intends to provide guidelines to companies and practitioners on how to set and organize experimentation activities for running trustworthy online controlled experiments.

关键词： data-driven development A/B tests Online controlled experiments

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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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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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Differentiating Feature Realization in Software Product development 18th

Differentiating Feature Realization in Software Product Deve...

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18th International Conference on Product-Focused Software Process Improvement (PROFES)

作者： Fabijan, Aleksander Olsson, Helena Holmstrom Bosch, Jan 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)9783319699264;9783319699257

Context: Software is no longer only supporting mechanical and electrical products. Today, it is becoming the main competitive advantage and an enabler of innovation. Not all software, however, has an equal impact on customers. Companies still struggle to differentiate between the features that are regularly used, there to be for sale, differentiating and that add value to customers, or which are regarded commodity. Goal: The aim of this paper is to (1) identify the different types of software features that we can find in software products today, and (2) recommend how to prioritize the development activities for each of them. Method: In this paper, we conduct a case study with five large-scale software intensive companies. Results: Our main result is a model in which we differentiate between four fundamentally different types of features (e.g. 'Checkbox', 'Flow', 'Duty' and 'Wow'). Conclusions: Our model helps companies in (1) differentiating between the feature types, and (2) selecting an optimal methodology for their development (e.g. 'Output-driven' vs. 'Outcome-driven').

关键词： data Feedback Outcome-driven development data-driven development Goal-oriented development

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data-driven Continuous Evolution of Smart Systems 11

Data-Driven Continuous Evolution of Smart Systems

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11th IEEE/ACM International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

作者： Bosch, Jan Olsson, Helena Holmstrom Chalmers Gothenburg Sweden Malmo Univ Malmo Sweden

ISBN: (纸本)9781450341875

As Marc Andreessen said in his Wall Street Journal OpEd, software is eating the world. The systems that we are building today and in the near future will exhibit levels of autonomy that will put new demands on the engineering of such systems. Although promising examples of autonomous systems exist, there is no established methodology for systematically building autonomous systems that employ modern software engineering technology such as continuous deployment and data-driven engineering. The contribution of this paper is twofold. First, it identifies and presents the challenge of continuous evolution of autonomous systems as a well-defined problem that needs to be addressed by software engineering research. Second, it presents a conceptual solution to this problem that integrates the development of new software for autonomous systems by R&D teams with systematic experimentation by autonomous systems.

关键词： data-driven development split testing autonomous systems continuous deployment

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Towards Continuous Customer Validation: A Conceptual Model for Combining Qualitative Customer Feedback with Quantitative Customer Observation 6

Towards Continuous Customer Validation: A Conceptual Model f...

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6th International Conference on Software Business (ICSOB)

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

ISBN: (纸本)9783319195933;9783319195926

Software-intensive product companies are becoming increasingly data-driven as can be witnessed by the big data and Internet of Things trends. However, optimally prioritizing customer needs in a mass-market context is notoriously difficult. While most companies use product owners or managers to represent the customer, research shows that the prioritization made is far from optimal. In earlier research, we have coined the term 'the open loop problem' to characterize this challenge. For instance, research shows that up to half of all the features in products are never used. This paper presents a conceptual model that emphasizes the need for combining qualitative feedback in early stages of development with quantitative customer observation in later stages of development. Our model is inductively derived from an 18 months close collaboration with six large global software-intensive companies.

关键词： Customer feedback techniques Qualitative feedback Quantitative observation Continuous customer validation data-driven development

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Quantitative Requirements Prioritization from a Pre-development Perspective 15

Quantitative Requirements Prioritization from a Pre-developm...

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15th International Conference on Software Process Improvement and Capability dEtermination (SPICE)

作者： Johansson, Enrico Bergdahl, Daniel Bosch, Jan Olsson, Helena Holmstrom Sony Mobile Commun Inc Lund Sweden Chalmers Univ Technol Dept Comp Sci & Engn S-41296 Gothenburg Sweden Malmo Univ Dept Comp Sci Malmo Sweden

ISBN: (纸本)9783319198606;9783319198590

Feature content in system releases tends to be prioritized using limited amounts of qualitative user input and based on the opinions of those in product management. This leads to several problems due to the wasteful allocation of R&D resources. In this paper, we present the results of our efforts to collect quantitative customer input before the start of development using mock-ups and surveys for a mobile application developed by Sony Mobile. Our research shows that (1) collecting quantitative feedback before development is feasible, (2) the data collected deviates from the original feature prioritization, i.e. it is beneficial and (3) the data gives further insight in requirement prioritization than a qualitative method could have provided.

关键词： Requirements engineering Customer data Survey Mock-up data-driven development Case study

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The Evolution of E-Learning Platforms from Content to Activity Based Learning The Case of Learn@WU

The Evolution of E-Learning Platforms from Content to Activi...

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Proceedings of 2015 International Conference on Interactive Collaborative Learning

作者： Andergassen, Monika Ernst, Guenter Guerra, Victor Moedritscher, Felix Moser, Markus Neumann, Gustaf Renner, Thomas Vienna Univ Econ & Business WU Informat Syst & New Media Vienna Austria

ISBN: (纸本)9781479987078

Over the last decades e-learning platforms evolved from tools for distributing materials and providing quiz-based assessments to complex software systems that enable didactically enriched learning experiences. This paper describes the metamorphosis of the Learn@WU system, one of the most intensely used e-learning platforms. The platform is in use since 2001, when it was designed for learning content management, complemented by community management functions over the years. One of the major usages in the beginning was the support of blended learning in large classes up to 1000 students and the provision of self-assessment in the initial phase of the study programs. From the start, the platform supported all beginner courses of all subjects without much instructional design, to give students complete freedom for self-directed learning. Today, the platform contains more than 160,000 learning materials used in the 4,000 courses offered each year. However, studies showed indications that most students prefer to use the self-assessments in order to memorize questions instead of deep learning of content, i.e. by carelessly submitting results for many exercises to receive the correct solutions provided as a feedback from the system. As a consequence, the success rates in self-assessments did not serve as good predictors for knowledge and exam success. It was also found that spaced learning and overall learning time have a positive influence on final exam results [1]. Based on these findings, the objectives for the further development of the platform included efforts to increase the validity of the self-assessment data and to better support instructional designs. The new implementation allows to assign different learning activities to resources and to organize activities in thematic modules in order to support, e.g., flipped-classroom designs. This focus on learning activities was achieved through the implementation of learning design functionality and the possibility to con

关键词： Learning Management System data-driven development Learning Activities Content Flows Learning Progress Learning Workflows

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Early Value Argumentation and Prediction: An Iterative Approach to Quantifying Feature Value 16th

Early Value Argumentation and Prediction: An Iterative Appro...

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16th International Conference on Product-Focused Software Process Improvement (PROFES)

作者： Fabijan, Aleksander Olsson, Helena Holmstrom Bosch, Jan Malmo Univ Fac Technol & Soc S-20506 Malmo Sweden Chalmers Univ Technol Dept Comp Sci & Engn S-41296 Gothenburg Sweden

ISBN: (纸本)9783319268446;9783319268439

Companies are continuously improving their practices and ways of working in order to fulfill always-changing market requirements. As an example of building a better understanding of their customers, organizations are collecting user feedback and trying to direct their R&D efforts by e.g. continuing to develop features that deliver value to the customer. We (1) develop an actionable technique that practitioners in organizations can use to validate feature value early in the development cycle, (2) validate if and when the expected value reflects on the customers, (3) know when to stop developing it, and (4) identity unexpected business value early during development and redirect R&D effort to capture this value. The technique has been validated in three experiments in two cases companies. Our findings show that predicting value for features under development helps product management in large organizations to correctly re-prioritize R&D investments.

关键词： Continuous experimentation EVAP QCD data-driven development Customer-driven development

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