In the European research project CESAR, requirements can be specified using templates called boilerplates. Each statement of a requirement consists of a boilerplate with inserted values for the attributes of the boilerplate. By choosing attribute values from a domain ontology, a consistent language can be achieved. This thesis seeks to use the combination of boilerplates and a domain ontology in a semi-automatic test generation process.There are multiple ways to automate the test generation process, with various degrees of automation involved. One option is to use the boilerplates and the domain ontology to create a test model that can be used to generate tests. Another option is to use the information from the domain ontology to assist the user when he creates tests. In this thesis, the latter option is investigated and a tool named WikiTest is developed. WikiTest uses Semantic MediaWiki and Semantic Forms to utilize the ontology and assist the user in the test creation process. Using a Cucumber syntax, the tests can be specified in a relatively free format that does not sacrifice the ability to automate test execution. An experiment is conducted, where the results show that WikiTest is easier to use and leads to a higher test case quality than the alternatives can do. Being able to inspect the domain ontology while creating tests did not give the same results as when the ontology was integrated directly in the tool.

As software systems increasingly are used to control critical infrastructure, transportation systems and factory equipment, the use of proper testing methods has become more important. Systems that can cause harm to people, equipment or the environment they operate in are called safety critical systems.The suppliers of safety critical systems makes use of safety analysis methods to investigate possible hazards. The ouput from the analysis are possible causes and effects of the hazards found. These results are a large part of the basis for writing safety requirements for the system.The safety requirements should be tested thoroughly to avoid accidents. It is important that the right testing technique is applied to test these systems. The consequences of a system failure can be very high, so it is crucial to make use of a testing technique that has an approach that fits safety testing best. This thesis presents an experiment that looks into these questions. Also, the experiment investigates how the barrier model and safety analysis results helps in writing test cases for these systems.

hACME game is a game based learning tool for teaching software security. The game is intended to help raising awareness and interest in the subject of software security. The purpose of the game is to make future software developers aware of how important security is. A key feature of the game is how it measures the participant's progress and the use of hints. This thesis focus on implementing an administrative interface for hACME game enabling easier access to statistics and results by admin users. The foundation for the thesis is a project written in the autumn semester of 2010 by Christian Berrum and Morten Weel Johnsen. The project thesis contained a conceptual design for the administrative interface.The result is a fully functional implementation of the administrative interface. The administrative interface consists of functionality for doing management tasks and getting statistics about the game and user progress. The thesis also focuses on the implementation of questionnaires in order to get information about the player's learning process.

Artificial Evolution has shown great potential in the musical domain. One task in which Evolutionary techniques have shown special promise is in the automatic creation or composition of music. However, a major challenge faced when constructing evolutionary music composition systems is finding a suitable fitness function.Several approaches to fitness have been tried. The most common is interactive evaluation. However, major efficiency challenges with such an approach have inspired the search for <i>automatic</i> alternatives.In this thesis, a music composition system is presented for the evolution of novel melodies. Motivated by the repetitive nature of music, a <i>quantitative</i> approach to automatic fitness is pursued. Two techniques are explored that both operate on frequency distributions of musical events. The first builds on <i>Zipf's Law</i>, which captures the scaling properties of music. Statistical <i>similarity</i> governs the second fitness function and incorporates additional domain knowledge learned from existing music pieces.Promising results show that pleasant melodies can emerge through the application of these techniques. The melodies are found to exhibit several favourable musical properties, including rhythm, melodic locality and motifs.

Association rules mining is one of the the most relevant techniques of data mining. It has been also applied in the domain of text mining, but the results are hard to interpret. In this matter, an Association Network is an structure to represent as a graph the relationships mined as association rules. The goal of this project was to provide a methodology to build association networks from concepts extracted from a collection of documents, as well as the study of the mathematical properties of the association networks to prove that they are not random graphs and that they exhibit small-world properties.

Open-Source software has become increasingly more common in IT-organisations. Despite this the focus of studies on open-source has largely been focused on large system software. In our thesis we have worked on a software development project in Shimla, India, to create a hospital management system for the district hospitals in the state of Himachal Pradesh. Through our studies we have looked at the challenges of developing and implementing an open-source IS system in low-resource environment. Our results show that such an undertaking can be successful, but that distributed development poses a lot of challenges, and that the use of open-source software, while free, still necessitates a lot of work and close communication with the community.

Smartphones are rapidly evolving, making them powerful devices with many features. Location awareness is one of the hot topics, aiding applications to provide better services to users. A challenge is to combine the large amount of tourist information with the limited display sizes of smartphones. Also, tourists spend a lot of time nding information with little knowledge of their probable enjoyment of these tourist relevant locations. Recommender systems attempt to solve this by using information about users and points of interest. We will investigate several studies that discuss tourist applications.This project presents the Mobile Tourist Service Recommender which is a personalized tourist application introduced by [Wium, 2010]. We have further developed this system, performed thorough usability testing, applied the Mobile Services Acceptance Model, and analyzed the results.The results indicate that the system has potential and is encouraged by the positive feedback from many users. Users especially found the system to be benecial to them as tourists, and that they could use the system during vacations. Unfortunately, the achieved responses were not completely satisfactory to our goals, but further iterations with the suggested improvements implemented will raise the user experience.

In this thesis, I present an approach to a case-based reasoning system with explanation capabilities in the Warhammer Fantasy Battle domain. This product is meant to support Warhammer gamers in their initial army lineup, by providing suggestions based on previously successful games against an opposing horde. Explanations will be used in order to convey the reasoning behind the solution, to present the data the solution is based upon and why certain changes were made.The created product is capable of creating the army lineup and give partially satisfactory explanations, based on the goals set both for the application as a whole and explanations. Although a full domain model is not implemented, are the results promising; with the inclusion of more domain knowledge and cases, will a fully competent and accurate system be achievable.

When tasks traditionally performed by humans are automated it is important thatthe machines are able to communicate how these tasks are solved and why. Whena user is surprised by the point of time where the task is executed, there is a needto be able to get an explanation to why this point in time was chosen.This project aims at investigating how intelligent systems in general, and case-based reasoning systems in particular can become explanation-aware. Our aim isprimarily to investigate existing case-based reasoning systems to see if explanation-awareness is achievable. Secondary, our aim is to develop a simple case-based rea-soning engine that complies with our theoretical work on explanation-awareness.

Neurons communicate through electrophysiological signals, which may be recorded using electrodes inserted into living tissue.When a neuron emits a signal, it is referred to as a spike, and an electrode can detect these from multiple neurons.Neuronal spike sorting is the process of classifying the spike activity based on which neuron each spike signal is emitted from.Advances in technology have introduced better recording equipment, which allows the recording of many neurons at the same time.However, clustering software is lagging behind.Currently, spike sorting is often performed semi-manually by experts, with computer assistance, in a drastically reduced feature space.This makes the clustering prone to subjectivity.Automating the process will make classification much more efficient, and may produce better results.Implementing accurate and efficient spike sorting algorithms is therefore increasingly important.We have developed parallel implementations of superparamagnetic clustering, a novel clustering algorithm, as well as k-means clustering, serving as a useful comparison.Several feature extraction methods have been implemented to test various input distributions with the clustering algorithms. To assess the quality of the results from the algorithms, we have also implemented different cluster quality algorithms.Our implementations have been benchmarked, and found to scale well both with increased problem sizes and when run on multi-core processors.The results from our cluster quality measurements are inconclusive, and we identify this as a problem related to the subjectivity in the manually classified datasets.To better assess the utility of the algorithms, comparisons with intracellular recordings should be performed.