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

Recent years have witnessed a steady growth of time-use research, driven by the increased research and policy interest in population activity patterns and their associations with long-term outcomes. There is recent interest in moving beyond traditional paper-administered time diaries to use new technologies for data collection in order to reduce respondent burden and administration costs, and to improve data quality. This paper presents two novel diary instruments that were employed by a large-scale multi-disciplinary cohort study in order to obtain information on the time allocation of adolescents in the United Kingdom. A web-administered diary and a smartphone app were created, and a mixed-mode data collection approach was followed: cohort members were asked to choose between these two modes, and those who were unable or refused to use the web/app modes were offered a paper diary. Using data from a pilot survey of 86 participants, we examine diary data quality indicators across the three modes. Results suggest that the web and app modes yield an overall better time diary data quality than the paper mode, with a higher proportion of diaries with complete activity and contextual information. Results also show that the web and app modes yield a comparable number of activity episodes to the paper mode. These results suggest that the use of new technologies can improve diary data quality. Future research using larger samples should systematically investigate selection and measurement effects in mixed-mode time-use survey designs.

BackgroundIn the absence of electronic health records, analysis of direct healthcare costs often relies on resource utilisation data collected from patient-reported surveys. This scoping review explored the availability, use and methodological details of self-reported healthcare service utilisation and cost data to assess healthcare costs in Ireland.MethodsPopulation health surveys were identified from Irish data repositories and details were collated in an inventory to inform the literature search. Irish cost studies published in peer-reviewed and grey sources from 2009 to 2019 were included if they used self-reported data on healthcare utilisation or cost. Two independent researchers extracted studies’ details and the PRISMA-ScR guidelines were used for reporting.ResultsIn total, 27 surveys were identified containing varying details of healthcare utilisation/cost, health status, demographic characteristics and health-related risk and behaviour. Of those surveys, 21 were general population surveys and six were study-specific ad-hoc surveys. Furthermore, 14 cost studies were identified which used retrospective self-reported data on healthcare utilisation or cost from ten of the identified surveys. Nine of these cost studies used ad-hoc surveys and five used data from pre-existing population surveys. Compared to population surveys, ad-hoc surveys contained more detailed information on resource use, albeit with smaller sample sizes. Recall periods ranged from 1 week for frequently used services to 1 year for rarer service use, or longer for once-off costs. A range of perspectives (societal, healthcare and public sector) and costing approaches (bottom-up costing and a mix of top-down and bottom-up) were used. The majority of studies (n = 11) determined unit prices using multiple sources, including national healthcare tariffs, literature and expert views. Moreover, most studies (n = 13) reported limitations concerning data availability, risk of bias and generalisability. Various sampling, data collection and analysis strategies were employed to minimise these.ConclusionPopulation surveys can aid cost assessments in jurisdictions that lack electronic health records, unique patient identifiers and data interoperability. To increase utilisation, researchers wanting to conduct cost analyses need to be aware of and have access to existing data sources. Future population surveys should be designed to address reported limitations and capture comprehensive health-related, demographic and resource use data.

The first International Workshop on MAchine Learning and Information Retrieval for Software Evolution (MALIR-SE) was held on the 11th of November 2013. The workshop was held in conjunction with the 28th IEEE/ACM International Conference on Automated Software Engineering (ASE) in Silicon Valley, California, USA. The workshop brought researchers and practitioners that were interested in leveraging machine learning and information retrieval techniques to automate various software evolution tasks. During the workshop, papers on the application of machine learning and information retrieval techniques to bug fix time prediction and anti-pattern detection were presented. There were also discussions on the presented papers and on future direction of research in the area.

Knowledge is typically created within one unit of a company and then transferred through contact persons ("knowledge brokers") to the other units. There are four main types of participants in the knowledge transfer process: sender, broker, broker's boss, and the other unit members. Transferring knowledge and promoting its utilization to all members in a hierarchical organization is not easy. A key element in effective knowledge transfer identified in a previous work is that the broker's knowledge approval (KA) has a positive impact on knowledge utilization by other members. In this study, several factors in creating KA were hypothesized. They were then tested by holding safety-training courses in a large Japanese technology company and surveying the 36 trainees (knowledge brokers) to ascertain their attitudes related to the hypothesize factors. Multiple regression analysis showed that the sender's KA was the factor that contributed most to the broker's KA. The findings were used to identify methods to improve the effectiveness of knowledge transfer in a large company from the perspective of a knowledge broker.

The clinical implementation and evaluation of heroin substitution programmes have been confounded by the lack of objective and validated biomarkers for illicit heroin use in patients prescribed pharmaceutical heroin. This study examined the capacity to detect illicit heroin use by gas chromatography-mass spectrometry (GC-MS) analysis of urine samples for the presence of opium impurities common to illicit, but not pharmaceutical heroin. To characterize the diagnostic properties of the metabolites of noscapine and papaverine in comparison to morphine as a gold-standard marker of illicit heroin use; and to examine the relationships between the self-reported time since most recent heroin use and the detection of these opioids in urine. A cross-sectional study of 52 opioid-dependent patients in treatment (not prescribed heroin), who self-reported illicit heroin use within the preceding 2 weeks. Self-report data regarding recent drug use and a urine sample were collected. GC-MS analyses of urines were conducted and reported by laboratory staff blinded to self-report data. The metabolites of papaverine (hydroxypapaverine and dihydroxypapeverine) were found to have high sensitivity, specificity and negative predictive values as markers for illicit heroin use compared to the 'gold-standard' morphine. Other opioids, including 6-mono-acetylmorphine (6-MAM), codeine and noscapine metabolites (e.g. meconine) were less adequate in detecting heroin use. GC-MS detection of papaverine metabolites in urine appears to be suitable method of identifying illicit heroin use for clinical and research purposes.

Estimation of racial/ethnic disparity in imprisonment rates in the United States is complicated by differences in data collection, definitions, and methods of counting such persons across data sources. This paper describes these sources of differences and their impacts on assessments of imprisonment disparity. Estimates of imprisonment rate disparities derived from state corrections administrative data diverge from those derived from self-report survey data. Changes in how survey data measure race and ethnicity affect inferences about changes in disparity. Administrative data show less Hispanic-White and Black-White disparity than self-report data, and the differences between them are increasing. Measures of disparity based on single-race classifications show larger Black-White disparity than those that are based on multiracial classifications. Various approaches to measuring race and ethnicity can affect conclusions about fairness in imprisonment. Therefore, disparities analysts should consider the multiple data sources and methods available to measure state imprisonment disparities, understand the assumptions behind the measures, and arbitrate the differences and similarities before drawing inferences about the extent of disparity in U.S. penal systems.

Predicting human behavior presents significant challenges in various domains. Chapter 1 adopts a novel methodological approach, utilizing neurophysiological responses measured and utilized machine learning to predict hit songs. Statistical analyses reveal the effectiveness of machine learning techniques, showcasing a 97% accuracy in classifying hit songs. This underscores the potential of machine learning applied to neural data in enhancing market outcome predictions. Chapter 2 focuses on the proactive assessment of mood among the elderly using continuous neurophysiological data. By integrating self-reports with neurophysiological measures, the study demonstrates a high predictive accuracy in identifying low mood and energy levels. Applications of such predictions can enable timely interventions to mitigate the risk of depression in vulnerable populations. In Chapter 3, a proactive approach to mental health assessment is explored through the continuous collection of neurophysiological data in the same population as chapter 2. Statistical analyses unveil the predictive power of neurophysiological troughs and peaks in determining daily mood fluctuations, with machine learning models achieving 90% accuracy. The study underscores the viability of commercially available neuroscientific technologies in assessing mood disorders and promoting well-being. Collectively, this dissertation underscores the transformative potential of neuroscientific methodologies in predicting human behavior and experience. By leveraging machine learning and continuous neurophysiological monitoring, these studies offer insights into proactive interventions and personalized approaches to improve societal well-being and prevent mental health crises.

BackgroundPatient race in the Department of Veterans Affairs (VA) information system was previously recorded based on an administrative or clinical employee's observation. Since 2003, the VA started to collect self-reported race in compliance with a new federal guideline. We investigated the implications of this transition for using race/ethnicity data in multi-year trends in the VA and in other healthcare data systems that make the transition.MethodsAll unique users of VA healthcare services with self-reported race/ethnicity data in 2004 were compared with their prior observer-recorded race/ethnicity data from 1997 – 2002 (N = 988,277).ResultsIn 2004, only about 39% of all VA healthcare users reported race/ethnicity values other than "unknown" or "declined." Females reported race/ethnicity at a lower rate than males (27% vs. 40%; p < 0.001). Over 95% of observer-recorded data agreed with self-reported data. Compared with the patient self-reported data, the observer-recorded White and African American races were accurate for 98% (kappa = 0.89) and 94% (kappa = 0.93) individuals, respectively. Accuracy of observer-recorded races was much worse for other minority groups with kappa coefficients ranging between 0.38 for American Indian or Alaskan Natives and 0.79 for Hispanic Whites. When observer-recorded race/ethnicity values were reclassified into non-African American groups, they agreed with the self-reported data for 98% of all individuals (kappa = 0.93).ConclusionFor overall VA healthcare users, the agreement between observer-recorded and self-reported race/ethnicity was excellent and observer-recorded and self-reported data can be used together for multi-year trends without creating serious bias. However, this study also showed that observation was not a reliable method of race/ethnicity data collection for non-African American minorities and racial disparity might be underestimated if observer-recorded data are used due to systematic patterns of inaccurate race/ethnicity assignments.

Several large companies have conducted initiatives for systematic learning from experience in software engineering. In the international Software Experience Center (SEC), for example, five companies exchanged experiences and collaborated in building experience exchange mechanisms to be used within each company. Many insights were gained and lessons were learned over the years, among them: (1) Written and documented experiences are the exception rather than the rule. (2) Although not documented in detail or controlled by a process, experience needs guidance and support in order to reach the designated person or group. The of experience must be kept in mind. (3) Experience is a delicate material, and any avoidable effort or threshold to participate in systematic experience exploitation may endanger stakeholder participation and success. (4) Tools can effectively be built to support orderly flow of experience, but they must be optimized for cognitive support of their users. These lessons learned from supporting experience exploitation can be applied to software projects more generally: Requirements, rationale, and other information flowing through a software project resemble experience with respect to the above-mentioned characteristics: They are often communicated orally rather than in a document. There are diverse processes and practices designed to channel information flow. Early and vague requirements must be handled with care, and tools need to be optimized to reduce cognitive barriers and thresholds, or they will not be accepted. A focus on information and experience flow takes the above-mentioned lessons into account. Information flows within one project, while experience often cuts across several projects. Requirements of one project are useful only in that same project. Experience in designing a product, however, may be reused in subsequent projects. Information and experience flows need to be modelled explicitly. A simple notation is proposed to capture just the essence of flowing information. What may seem like a subtle shift from processes to flows offers a new perspective: Based on those models, dedicated techniques and tools can be developed for analysing and for improving the flows. A wide range of current trends in software engineering can benefit from a better understanding of --- and support for --- appropriate information flow: Interfaces to the subcontractors, distributed and collaborative teams, Wiki webs, and a variety of new communication channels in global software engineering call for a focus on information flow.

Software Engineering: A Methodical Approach (Second Edition) provides a comprehensive, but concise introduction to software engineering. It adopts a methodical approach to solving software engineering problems, proven over several years of teaching, with outstanding results. The book covers concepts, principles, design, construction, implementation, and management issues of software engineering. Each chapter is organized systematically into brief, reader-friendly sections, with itemization of the important points to be remembered. Diagrams and illustrations also sum up the salient points to enhance learning. Additionally, the book includes the author’s original methodologies that add clarity and creativity to the software engineering experience. New in the Second Edition are chapters on software engineering projects, management support systems, software engineering frameworks and patterns as a significant building block for the design and construction of contemporary software systems, and emerging software engineering frontiers. The text starts with an introduction of software engineering and the role of the software engineer. The following chapters examine in-depth software analysis, design, development, implementation, and management. Covering object-oriented methodologies and the principles of object-oriented information engineering, the book reinforces an object-oriented approach to the early phases of the software development life cycle. It covers various diagramming techniques and emphasizes object classification and object behavior. The text features comprehensive treatments of: Project management aids that are commonly used in software engineering An overview of the software design phase, including a discussion of the software design process, design strategies, architectural design, interface design, database design, and design and development standards User interface design Operations design Design considerations including system catalog, product documentation, user message management, design for real-time software, design for reuse, system security, and the agile effect Human resource management from a software engineering perspective Software economics Software implementation issues that range from operating environments to the marketing of software Software maintenance, legacy systems, and re-engineering This textbook can be used as a one-semester or two-semester course in software engineering, augmented with an appropriate CASE or RAD tool. It emphasizes a practical, methodical approach to software engineering, avoiding an overkill of theoretical calculations where possible. The primary objective is to help students gain a solid grasp of the activities in the software development life cycle to be confident about taking on new software engineering projects.

This book provides essential insights on the adoption of modern software engineering practices at large companies producing software-intensive systems, where hundreds or even thousands of engineers collaborate to deliver on new systems and new versions of already deployed ones. It is based on the findings collected and lessons learned at the Software Center (SC), a unique collaboration between research and industry, with Chalmers University of Technology, Gothenburg University and Malm University as academic partners and Ericsson, AB Volvo, Volvo Car Corporation, Saab Electronic Defense Systems, Grundfos, Axis Communications, Jeppesen (Boeing) and Sony Mobile as industrial partners. The 17 chapters present the Stairway to Heaven model, which represents the typical evolution path companies move through as they develop and mature their software engineering capabilities. The chaptersdescribe theoretical frameworks, conceptual models and, most importantly, the industrial experiences gained by the partner companies in applying novel software engineering techniques. The books structure consists of six parts. Part I describes the model in detail and presents an overview of lessons learned in the collaboration between industry and academia. Part II deals with the first step of the Stairway to Heaven, in which R&D adopts agile work practices. Part III of the book combines the next two phases, i.e., continuous integration (CI) and continuous delivery (CD), as they are closely intertwined. Part IV is concerned with the highest level, referred to as R&D as an innovation system, while Part V addresses a topic that is separate from the Stairway to Heaven and yet critically important in large organizations: organizational performance metrics that capture data, and visualizations of the status of software assets, defects and teams. Lastly, Part VI presents the perspectives of two of the SC partner companies. The book is intended for practitioners and professionals in the software-intensive systems industry, providing concrete models, frameworks and case studies that show the specific challenges that the partner companies encountered, their approaches to overcoming them, and the results. Researchers will gain valuable insights on the problems faced by large software companies, and on how to effectively tackle them in the context of successful cooperation projects.

Harmful software has resulted in loss of life, societal and environmental damage alongside economic losses from fines and sales embargoes. When someone perceives their team or organisation is creating or operating harmful software (e.g., defective, vulnerable, malicious or illegal), one way to attempt to change the situation is to “blow the whistle” and disclose the situation internally or externally. Studying harmful situations and the effectiveness of interventions, up to and including whistleblowing, can help identify technical and human successes and failings in software engineering (SE). The aim of this paper is to explore the extent to which whistleblowing is studied in SE with the objective of identifying themes, research approaches, gaps and concerns, and the implications for future SE research and practice. We find that whistleblowing is an under-explored area of SE research, and where research exists, it often takes the view that reporting harm is a matter of individual moral responsibility; we argue this poorly reflects SE collaborative practice where professional responsibilities are distributed across the software development lifecycle. We conclude by 1) recommending approaches that can help a more timely identification and mitigation of harm in SE; 2) suggesting mechanisms for improving the effectiveness and the personal safety of harm-reporting in SE, and 3) reflecting on the role that professional bodies can have in supporting harm reporting, up to and including whistleblowing.

This report presents findings from an eight-week, open-label study of doxazosin extended-release for nightmares, sleep disturbance, and overall clinical symptoms in posttraumatic stress disorder (PTSD). Recommendations for future studies of doxazosin are provided. Fifteen male and female adults were enrolled. The primary endpoints were change in Clinician-Administered PTSD Scale total, nightmare, and sleep disturbance scores from pretreatment to end of treatment. Self-report data on PTSD, sleep quality, depression, and quality of life collected at three time points and sleep diary data collected daily were analyzed secondarily. Eight participants completed eight weeks of study treatment. Among completers, significant changes in nightmares and overall PTSD symptoms were found. Secondary analyses using data from completers and noncompleters demonstrated improvements in all secondary outcomes. Of the participants who dropped out of the study, four participants discontinued because of side effects, and three participants discontinued because of other barriers to study participation. These findings indicate that doxazosin may be of value for some individuals with PTSD but problematic for others. Alternative formulations of doxazosin, such as the immediate-release formulation, should be studied. Future research should examine moderators of treatment tolerability and treatment effects to best identify those who will or will not benefit from doxazosin.

Software engineers have always been experimenting with various models in an endeavor to improve upon software processes. Recently, a new area of software process improvement through experience management has got attention of the software engineering community. Software developers are experimenting with experience management to improve software process. This chapter explores the role of experience management in mitigating the effects of software crisis and investigates the current state of EM in Indian software engineering environment. The chapter discusses the need and integration of knowledge and experience management practices in software engineering environments. The chapter begins with the discussion of the ills of software engineering, classically referred to as “software crisis,” like late delivery of the systems, ill-functionality, and cost overruns, and justifies how managing the knowledge and experience of software engineers and practitioners can ward off these evils. The chapter then discusses the models and methodologies for knowledge and experience management in software engineering. Finally, the chapter sheds light on the major works carried out in this area and suggests possible future research avenues in this domain.

Time pressure in software engineering: A systematic review