44 research outputs found

    h1551574/interruptibility-prediction-rp: Interruptibility Prediction - Replication Package V1.0

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    Interruptibility of software developers and its prediction using psycho-physiological sensors: A replication - Replication Package V1.0.

    Interruption science as a research field: Towards a taxonomy of interruptions as a foundation for the field

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    Interruptions have become ubiquitous in both our personal and professional lives. Accordingly, research on interruptions has also increased steadily over time, and research published in various scientific disciplines has produced different perspectives, fundamental ideas, and conceptualizations of interruptions. However, the current state of research hampers a comprehensive overview of the concept of interruption, predominantly due to the fragmented nature of the existing literature. Reflecting on its genesis in the 1920s and the longstanding research on interruptions, along with recent technological, behavioral, and organizational developments, this paper provides a comprehensive interdisciplinary overview of the various attributes of an interruption, which facilitates the establishment of interruption science as an interdisciplinary research field in the scientific landscape. To obtain an overview of the different interruption attributes, we conducted a systematic literature review with the goal of classifying interruptions. The outcome of our research process is a taxonomy of interruptions, constituting an important foundation for the field. Based on the taxonomy, we also present possible avenues for future research

    Using Logs Data to Identify When Software Engineers Experience Flow or Focused Work

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    Beyond self-report data, we lack reliable and non-intrusive methods for identifying flow. However, taking a step back and acknowledging that flow occurs during periods of focus gives us the opportunity to make progress towards measuring flow by isolating focused work. Here, we take a mixed-methods approach to design a logs-based metric that leverages machine learning and a comprehensive collection of logs data to identify periods of related actions (indicating focus), and validate this metric against self-reported time in focus or flow using diary data and quarterly survey data. Our results indicate that we can determine when software engineers at a large technology company experience focused work which includes instances of flow. This metric speaks to engineering work, but can be leveraged in other domains to non-disruptively measure when people experience focus. Future research can build upon this work to identify signals associated with other facets of flow

    Quantifying Quality of Life

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    Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

    Finding the Hidden: Detecting Atypical Affective States from Physiological Signals

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    In cognitive science, intuition is described as a strategy of processing information that relies on people's instinctive and emotional criteria. When compared with the deliberate choices made after conscious reasoning, the quick and intuitive decision making strategies can be more effective. The intuitive thinking provokes changes in human physiological responses which can be measured by sensors. Utilising physiological reactions, previous work shows that atypical patterns such as emotion expressions and image manipulations can be identified. This thesis expands the exploration to examine whether more atypical human behaviour can be recognised from physiological signals. The examined subtly atypical behaviour includes depression, doubt and deception, Depression is a serious chronic mental disease and is considered as an atypical health condition in people. Doubt is defined as a non-deliberate attempt to mislead others and is a passive form of deception, representing an atypicality from honest behaviours. Deception is a more purposeful attempt to deceive, and thus is a distinct type of atypicality than honest communication. Through examining physiological reactions from presenters who have a particular atypical behaviour or condition, and observers who view behaviours of presenters, this research aims to recognise atypicality in human behaviour. A collection of six user studies are conducted. In two user studies, presenters are asked to conduct doubting and deceiving behaviours, while the remaining user studies involve observers watching behaviours of presenters who suffer from depression, have doubt, or have conducted deception. Physiological reactions of both presenters and observers are collected, including Blood Volume Pulse, Electrodermal Activity, Skin Temperature and Pupillary Responses. Observers are also asked to explicitly evaluate whether the viewed presenters were being depressed, doubting, or deceiving. Investigations upon physiological data in this thesis finds that detectable cues corresponding with depression, doubt and deception can be found. Viewing depression provokes visceral physiological reactions in observers that can be measured. Such physiological responses can be used to derive features for machine learning models to accurately distinguish between healthy individuals and people with depression. By contrast, depression does not provoke strong conscious recognition in observers, resulting in a conscious evaluation accuracy slightly above chance level. Similar results are also found in detecting doubt and deception. People with doubt and deceit elicit consistent physiological reactions within themselves. These bodily responses can be utilised by machine learning models or deep learning models to recognise doubt or deception. The doubt and deceit in presenters can also be recognised using physiological signals in observers, with excellent recognition rates which are higher when compared with the conscious judgments from the same group of observers. The results indicate that atypicality in presenters can both be captured by physiological signals of presenters and observers. Presenters' physiological reactions contribute to higher recognition of atypicality, but observers' physiological responses can serve as a comparable alternative. The awareness of atypicality among observers happens physiologically, so can be used by machine learning models, even when they do not reach the consciousness of the person. The research findings lead to a further discussion around the implications of observers' physiological responses. Decision support applications which utilise a quantifiable measure of people's unconscious and intuitive 'gut feeling' can be developed based on the work reported here to assist people with medical diagnosis, information credibility evaluation, and criminal detection. Further research suggests exploring more atypical behaviours in the wild

    Rethinking Productivity in Software Engineering

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    Get the most out of this foundational reference and improve the productivity of your software teams. This open access book collects the wisdom of the 2017 "Dagstuhl" seminar on productivity in software engineering, a meeting of community leaders, who came together with the goal of rethinking traditional definitions and measures of productivity. The results of their work, Rethinking Productivity in Software Engineering, includes chapters covering definitions and core concepts related to productivity, guidelines for measuring productivity in specific contexts, best practices and pitfalls, and theories and open questions on productivity. You'll benefit from the many short chapters, each offering a focused discussion on one aspect of productivity in software engineering. Readers in many fields and industries will benefit from their collected work. Developers wanting to improve their personal productivity, will learn effective strategies for overcoming common issues that interfere with progress. Organizations thinking about building internal programs for measuring productivity of programmers and teams will learn best practices from industry and researchers in measuring productivity. And researchers can leverage the conceptual frameworks and rich body of literature in the book to effectively pursue new research directions. What You'll Learn Review the definitions and dimensions of software productivity See how time management is having the opposite of the intended effect Develop valuable dashboards Understand the impact of sensors on productivity Avoid software development waste Work with human-centered methods to measure productivity Look at the intersection of neuroscience and productivity Manage interruptions and context-switching Who Book Is For Industry developers and those responsible for seminar-style courses that include a segment on software developer productivity. Chapters are written for a generalist audience, without excessive use of technical terminology. ; Collects the wisdom of software engineering thought leaders in a form digestible for any developer Shares hard-won best practices and pitfalls to avoid An up to date look at current practices in software engineering productivit
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