Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria through a lengthy and time-consuming process. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis. In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy and reliability. Therefore, we conducted a systematic review and meta-analysis to summarize the available evidence in the literature so far. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for ANN classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seems promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7% versus 72.8%). Finally, our analysis showed AUC values between acceptable and excellent. Still, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.

One of the reasons for the failure of software projects is the absence of risk management procedures or its improper application. The adoption of Scrum in software projects is increasing. However, such approach does not specify risk management activities. This paper presents the results of a survey conducted using a qualitative approach to analyze how risk management is carried out in Scrum software projects. Consequently, we present risk management practices that achieved greater and lesser agreement among respondents and the literature, respectively. We found that risk management must be applied continuously in a feedback loop. Furthermore, Scrum projects must not have a high formal planning level, even for high‐risk ones. The research verified that risk management in Scrum is performed differently from its application in traditional approaches. The framework has native resources, but classic processes of risk management would be incorporated and adapted.

No abstract

This article presents a bibliometric study of Risk Management in Scrum Projects. It was carried out an analysis involving the ISI Web of Knowledge and Scopus databases, identifying the main authors, countries and periodicals. It also identified the most cited authors by the analyzed articles, in addition to the keywords most frequently cited. These analyzes were performed using the reference maps, which were generated by CiteSpace® software, which offers a set of features to support bibliometrics. The objective was to identify the current scenario research of Risk Management applied in Scrum Projects in order to offer a consistent basis of information to researchers. The research verified that, despite the importance of the research topic, few scientific studies have been identified, which brings the need for new researches on the subject.

Risk management contributes to software projects success, but agile software development methods do not offer specific activities to manage risks. Therefore, this study aims to propose a list of risk management practices for agile projects, aiming to increase their chances of success. We analyzed 129 works on agile methods that afforded 127 risk management practices. We categorized and ranked practices using the AHP multi-criteria method with the participation of experts in the subject. The study presents risk management practices for daily meetings, increment, prototype, product backlog and Sprint planning as the most important for the risk management effectiveness. This study identified specific risk management practices for agile methods, not converging with other studies. Results contribute to the risk management improvement in agile projects and, consequently, increase their chances of success.