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Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods: The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of

Context: As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective: This study investigates the effects of the pandemic on developers’ wellbeing and productivity. Method: A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results: The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers’ wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions: To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees’ home offices. Women, parents and disabled persons may require extra support.

Background: Cost avoidance through reuse shows the benefits gained by the software organisations when reusing an artefact. Cost avoidance captures benefits that are not captured by cost savings e.g. spending that would have increased in the absence of the cost avoidance activity. This type of benefit can be combined with quality aspects of the product e.g. costs avoided because of defect prevention. Cost avoidance is a key driver for software reuse. Objectives: The main objectives of this study are: (1) To assess the status of capturing cost avoidance through reuse in the academia; (2) Based on the first objective, propose improvements in capturing of reuse cost avoidance, integrate these into an instrument, and evaluate the instrument in the software industry. Method: The study starts with a systematic literature review (SLR) on capturing of cost avoidance through reuse. Later, a solution is proposed and evaluated in the industry to address the shortcomings identified during the systematic literature review. Results: The results of a systematic literature review describe three previous studies on reuse cost avoidance and show that no solution, to capture reuse cost avoidance, was validated in industry. Afterwards, an instrument and a data collection form are proposed that can be used to capture the cost avoided by reusing any type of reuse artefact. The instrument and data collection form (describing guidelines) were demonstrated to a focus group, as part of static evaluation. Based on the feedback, the instrument was updated and evaluated in industry at 6 development sites, in 3 different countries, covering 24 projects in total. Conclusion: The proposed solution performed well in industrial evaluation. With this solution, practitioners were able to do calculations for reuse costs avoidance and use the results as decision support for identifying potential artefacts to reuse.