Evolutionary computation for software testing

A variety of products undergo a transformation from a pure mechanical design to more and more software and electronic components. A polarized example are watches. Several decades ago they have been purely mechanical. Modern smart watches are almost completely electronic devices which heavily rely on software. Further, a smart watch offers a lot more features than just the information about the current time. This change had a crucial impact on how software is being developed. A first attempt to control the rising complexity was to move to agile development practices such as extreme programming or scrum. This rise in complexity is not only affecting the development process but also quality assurance and software testing. If a product contains more and more features then this leads to a higher number of tests necessary to ensure quality standards. Furthermore agile development practices work in an iterative manner which leads to repetitive testing that puts more effort on the testing team. We aimed within the thesis to ease the pain of testing. Thereby we examined a series of subproblems that arise. A key complexity is the number of test cases. We intended to reduce the number of test cases before they are executed manually or implemented as automated tests. Thereby we examined the test specification and based on the requirements coverage of the individual tests, we were able to identify redundant tests. We relied on a novel metaheuristic called GCAIS which we improved upon iteratively. Another task is to control the remaining complexity. Testing is often time crucial and an appropriate subset of the available tests must be chosen in order to get a quick insight into the status of the device under test. We examined this challenge in two different testing scenarios. The first scenario is located in semi-automated testing where engineers execute a set of automated tests locally and closely observe the behaviour of the system under test. We extended GCAIS to compute test suites that satisfy different criteria if provided with sufficient search time. The second use case is located in fully automated testing in a continuous integration (CI) setting. CI focuses on frequent software build cycles which also include testing. These builds contain a testing stage which greatly emphasizes speed. Thus there we also have to compute crucial tests. However, due to the nature of the process we have to continuously recompute a test suite for each build as the software and maybe even the test cases at hand have changed. Hence it is hard to compute the test suite ahead of time and these tests have to be determined as part of the CI execution. Thus we switched to a computational lightweight learning classifier system (LCS) to prioritize and select test cases. We integrated a series of innovations we made into an LCS known as XCSF such as continuous priorities, experience replay and transfer learning. This enabled us to outperform a state of the art artificial neural network which is used by companies such as Netflix. We further investigated how LCS can be made faster using parallelism. We developed generic approaches which may run on any multicore computing device. This is of interest for our CI use case as the build server's architecture is unknown. However, the methods are also independent of the concrete LCS and are not linked to our testing problem. We identified that many of the challenges that need to be faced in the CI use case have been tackled by Organic Computing (OC), for example the need to adapt to an ever changing environment. Hence we relied on OC design principles to create a system architecture which wraps the LCS developed and integrates it into existing CI processes. The final system is robust and highly autonomous. A side-effect of the high degree of autonomy is a high level of automatization which fits CI well. We also gave insight on the usability and delivery of the full system to our industrial partner. Test engineers can easily integrate it with a few lines of code and need no knowledge about LCS and OC in order to use it. Another implication of the developed system is that OC's ideas and design principles can also be employed outside the field of embedded systems. This shows that OC has a greater level of generality. The process of testing and correcting found errors is still only partially automated. We make a first step into automating the entire process and thereby take an analogy to the concept of self-healing of OC. As a first proof of concept of this school of thought we take a look at touch interfaces. There we can automatically manipulate the software to fulfill the specified behaviour. Thus only a minimalistic amount of manual work is required

XCS Classifier System with Experience Replay

XCS constitutes the most deeply investigated classifier system today. It bears strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks. Besides outstanding successes in various classification and regression tasks, XCS also proved very effective in certain multi-step environments from the domain of reinforcement learning. Especially in the latter domain, recent advances have been mainly driven by algorithms which model their policies based on deep neural networks -- among which the Deep-Q-Network (DQN) is a prominent representative. Experience Replay (ER) constitutes one of the crucial factors for the DQN's successes, since it facilitates stabilized training of the neural network-based Q-function approximators. Surprisingly, XCS barely takes advantage of similar mechanisms that leverage stored raw experiences encountered so far. To bridge this gap, this paper investigates the benefits of extending XCS with ER. On the one hand, we demonstrate that for single-step tasks ER bears massive potential for improvements in terms of sample efficiency. On the shady side, however, we reveal that the use of ER might further aggravate well-studied issues not yet solved for XCS when applied to sequential decision problems demanding for long-action-chains

Incidence, prevalence and care of type 1 diabetes in children and adolescents in Germany: Time trends and regional socioeconomic situation

Background: Trends over time and possible socio-spatial inequalities in the incidence and care of type 1 diabetes mellitus (T1D) in children and adolescents are important parameters for the planning of target-specific treatment structures. Methodology: The incidence and prevalence of type 1 diabetes, diabetic ketoacidosis and severe hypoglycaemia as well as the HbA1c value are presented for under 18-year-olds based on data from the nationwide Diabetes Prospective Follow-up Registry (DPV) and the diabetes registry of North Rhine-Westphalia. Indicators were mapped by sex over time between 2014 and 2020, and stratified by sex, age and regional socioeconomic deprivation for 2020. Results: In 2020, the incidence was 29.2 per 100,000 person-years and the prevalence was 235.5 per 100,000 persons, with the figures being higher in boys than in girls in either case. The median HbA1c value was 7.5%. Ketoacidosis manifested in 3.4% of treated children and adolescents, significantly more often in regions with very high (4.5%) deprivation than in regions with very low deprivation (2.4%). The proportion of severe hypoglycaemia cases was 3.0%. Between 2014 and 2020, the incidence, prevalence and HbA1c levels changed little, while the proportions of ketoacidosis and severe hypoglycaemia decreased. Conclusions: The decrease in acute complications indicates that type 1 diabetes care has improved. Similar to previous studies, the results suggest an inequality in care by regional socioeconomic situation

Incidence, prevalence and care of type 1 diabetes in children and adolescents in Germany: Time trends and regional socioeconomic situation

Background: Trends over time and possible socio-spatial inequalities in the incidence and care of type 1 diabetes mellitus (T1D) in children and adolescents are important parameters for the planning of target-specific treatment structures. Methodology: The incidence and prevalence of type 1 diabetes, diabetic ketoacidosis and severe hypoglycaemia as well as the HbA1c value are presented for under 18-year-olds based on data from the nationwide Diabetes Prospective Follow-up Registry (DPV) and the diabetes registry of North Rhine-Westphalia. Indicators were mapped by sex over time between 2014 and 2020, and stratified by sex, age and regional socioeconomic deprivation for 2020. Results: In 2020, the incidence was 29.2 per 100,000 person-years and the prevalence was 235.5 per 100,000 persons, with the figures being higher in boys than in girls in either case. The median HbA1c value was 7.5%. Ketoacidosis manifested in 3.4% of treated children and adolescents, significantly more often in regions with very high (4.5%) deprivation than in regions with very low deprivation (2.4%). The proportion of severe hypoglycaemia cases was 3.0%. Between 2014 and 2020, the incidence, prevalence and HbA1c levels changed little, while the proportions of ketoacidosis and severe hypoglycaemia decreased. Conclusions: The decrease in acute complications indicates that type 1 diabetes care has improved. Similar to previous studies, the results suggest an inequality in care by regional socioeconomic situation

Inzidenz, Prävalenz und Versorgung von Typ-1-Diabetes bei Kindern und Jugendlichen in Deutschland: Zeittrends und sozialräumliche Lage

Hintergrund: Zeitliche Trends und mögliche sozialräumliche Ungleichheiten hinsichtlich der Häufigkeit und Versorgung von Typ-1-Diabetes mellitus (T1D) bei Kindern und Jugendlichen sind für die Planung von zielgerechten Behandlungsstrukturen von Bedeutung. Methode: Mit Daten der bundesweiten Diabetes-Patienten-Verlaufsdokumentation (DPV) und des Diabetesregisters in Nordrhein-Westfalen wurden für unter 18-Jährige Inzidenz und Prävalenz des Typ-1-Diabetes sowie HbA1c-Wert, diabetische Ketoazidosen und schwere Hypoglykämien dargestellt. Die Indikatoren wurden im Verlauf zwischen 2014 und 2020 nach Geschlecht und für 2020 stratifiziert nach Geschlecht, Alter und regionaler sozioökonomischer Deprivation abgebildet. Ergebnisse: 2020 betrug die Inzidenz 29,2 pro 100.000 Personenjahre und die Prävalenz 235,5 pro 100.000 Personen, mit jeweils höheren Werten bei Jungen als bei Mädchen. Der HbA1c-Wert betrug im Median 7,5 %. Bei 3,4 % der Behandelten trat eine Ketoazidose auf, signifikant häufiger in Regionen mit sehr hoher (4,5 %) als in Regionen mit sehr niedriger Deprivation (2,4 %). Der Anteil schwerer Hypoglykämien lag bei 3,0 %. Zwischen 2014 und 2020 änderten sich Inzidenz, Prävalenz und HbA1c-Wert kaum, während die Anteile von Ketoazidosen und schweren Hypoglykämien abnahmen. Schlussfolgerungen: Die Abnahme von Akutkomplikationen weist auf eine verbesserte Versorgung des Typ-1-Diabetes hin. Ähnlich wie in früheren Studien deuten die Ergebnisse eine Ungleichheit in der Versorgung nach sozialräumlicher Lage an

Inzidenz, Prävalenz und Versorgung von Typ-1-Diabetes bei Kindern und Jugendlichen in Deutschland: Zeittrends und sozialräumliche Lage

Hintergrund: Zeitliche Trends und mögliche sozialräumliche Ungleichheiten hinsichtlich der Häufigkeit und Versorgung von Typ-1-Diabetes mellitus (T1D) bei Kindern und Jugendlichen sind für die Planung von zielgerechten Behandlungsstrukturen von Bedeutung. Methode: Mit Daten der bundesweiten Diabetes-Patienten-Verlaufsdokumentation (DPV) und des Diabetesregisters in Nordrhein-Westfalen wurden für unter 18-Jährige Inzidenz und Prävalenz des Typ-1-Diabetes sowie HbA1c-Wert, diabetische Ketoazidosen und schwere Hypoglykämien dargestellt. Die Indikatoren wurden im Verlauf zwischen 2014 und 2020 nach Geschlecht und für 2020 stratifiziert nach Geschlecht, Alter und regionaler sozioökonomischer Deprivation abgebildet. Ergebnisse: 2020 betrug die Inzidenz 29,2 pro 100.000 Personenjahre und die Prävalenz 235,5 pro 100.000 Personen, mit jeweils höheren Werten bei Jungen als bei Mädchen. Der HbA1c-Wert betrug im Median 7,5 %. Bei 3,4 % der Behandelten trat eine Ketoazidose auf, signifikant häufiger in Regionen mit sehr hoher (4,5 %) als in Regionen mit sehr niedriger Deprivation (2,4 %). Der Anteil schwerer Hypoglykämien lag bei 3,0 %. Zwischen 2014 und 2020 änderten sich Inzidenz, Prävalenz und HbA1c-Wert kaum, während die Anteile von Ketoazidosen und schweren Hypoglykämien abnahmen. Schlussfolgerungen: Die Abnahme von Akutkomplikationen weist auf eine verbesserte Versorgung des Typ-1-Diabetes hin. Ähnlich wie in früheren Studien deuten die Ergebnisse eine Ungleichheit in der Versorgung nach sozialräumlicher Lage an