The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia. This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care - Blekinge (n=4175). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors. Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms. There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.

Exploratory testing (ET) is a powerful and efficient way of testing software by integrating design, execution, and analysis of tests during a testing session. ET is often contrasted with scripted testing, and seen as a choice of either exploratory testing or not. In contrast, we pose that exploratory testing can be of varying degrees of exploration from fully exploratory to fully scripted. In line with this, we propose a scale for the degree of exploration and define five levels. In our classification, these levels of exploration correspond to the way test charters are defined. We have evaluated this classification through focus groups at four companies and identified factors that influence the choice of exploration level. The results show that the proposed levels of exploration are influenced by different factors such as ease to reproduce defects, better learning, verification of requirements, etc., and that the levels can be used as a guide to structure test charters. Our study also indicates that applying a combination of exploration levels can be beneficial in achieving effective testing.

During exploratory testing sessions the tester simultaneously learns, designs and executes tests. The activity is iterative and utilizes the skills of the tester and provides flexibility and creativity. Test charters are used as a vehicle to support the testers during the testing. The aim of this study is to support practitioners in the design of test charters through checklists. We aimed to identify factors allowing practitioners to critically reflect on their designs and contents of test charters to support practitioners in making informed decisions of what to include in test charters. The factors and contents have been elicited through interviews. Overall, 30 factors and 35 content elements have been elicited.