Support for collaborative component-based software engineering

Collaborative system composition during design has been poorly supported by traditional CASE tools (which have usually concentrated on supporting individual projects) and almost exclusively focused on static composition. Little support for maintaining large distributed collections of heterogeneous software components across a number of projects has been developed. The CoDEEDS project addresses the collaborative determination, elaboration, and evolution of design spaces that describe both static and dynamic compositions of software components from sources such as component libraries, software service directories, and reuse repositories. The GENESIS project has focussed, in the development of OSCAR, on the creation and maintenance of large software artefact repositories. The most recent extensions are explicitly addressing the provision of cross-project global views of large software collections and historical views of individual artefacts within a collection. The long-term benefits of such support can only be realised if OSCAR and CoDEEDS are widely adopted and steps to facilitate this are described. This book continues to provide a forum, which a recent book, Software Evolution with UML and XML, started, where expert insights are presented on the subject. In that book, initial efforts were made to link together three current phenomena: software evolution, UML, and XML. In this book, focus will be on the practical side of linking them, that is, how UML and XML and their related methods/tools can assist software evolution in practice. Considering that nowadays software starts evolving before it is delivered, an apparent feature for software evolution is that it happens over all stages and over all aspects. Therefore, all possible techniques should be explored. This book explores techniques based on UML/XML and a combination of them with other techniques (i.e., over all techniques from theory to tools). Software evolution happens at all stages. Chapters in this book describe that software evolution issues present at stages of software architecturing, modeling/specifying, assessing, coding, validating, design recovering, program understanding, and reusing. Software evolution happens in all aspects. Chapters in this book illustrate that software evolution issues are involved in Web application, embedded system, software repository, component-based development, object model, development environment, software metrics, UML use case diagram, system model, Legacy system, safety critical system, user interface, software reuse, evolution management, and variability modeling. Software evolution needs to be facilitated with all possible techniques. Chapters in this book demonstrate techniques, such as formal methods, program transformation, empirical study, tool development, standardisation, visualisation, to control system changes to meet organisational and business objectives in a cost-effective way. On the journey of the grand challenge posed by software evolution, the journey that we have to make, the contributory authors of this book have already made further advances

Applying Process-Oriented Data Science to Dentistry

Background: Healthcare services now often follow evidence-based principles, so technologies such as process and data mining will help inform their drive towards optimal service delivery. Process mining (PM) can help the monitoring and reporting of this service delivery, measure compliance with guidelines, and assess effectiveness. In this research, PM extracts information about clinical activity recorded in dental electronic health records (EHRs) converts this into process-models providing stakeholders with unique insights to the dental treatment process. This thesis addresses a gap in prior research by demonstrating how process analytics can enhance our understanding of these processes and the effects of changes in strategy and policy over time. It also emphasises the importance of a rigorous and documented methodological approach often missing from the published literature. Aim: Apply the emerging technology of PM to an oral health dataset, illustrating the value of the data in the dental repository, and demonstrating how it can be presented in a useful and actionable manner to address public health questions. A subsidiary aim is to present the methodology used in this research in a way that provides useful guidance to future applications of dental PM. Objectives: Review dental and healthcare PM literature establishing state-of-the-art. Evaluate existing PM methods and their applicability to this research’s dataset. Extend existing PM methods achieving the aims of this research. Apply PM methods to the research dataset addressing public health questions. Document and present this research’s methodology. Apply data-mining, PM, and data-visualisation to provide insights into the variable pathways leading to different outcomes. Identify the data needed for PM of a dental EHR. Identify challenges to PM of dental EHR data. Methods: Extend existing PM methods to facilitate PM research in public health by detailing how data extracts from a dental EHR can be effectively managed, prepared, and used for PM. Use existing dental EHR and PM standards to generate a data reference model for effective PM. Develop a data-quality management framework. Results: Comparing the outputs of PM to established care-pathways showed that the dataset facilitated generation of high-level pathways but was less suitable for detailed guidelines. Used PM to identify the care pathway preceding a dental extraction under general anaesthetic and provided unique insights into this and the effects of policy decisions around school dental screenings. Conclusions: Research showed that PM and data-mining techniques can be applied to dental EHR data leading to fresh insights about dental treatment processes. This emerging technology along with established data mining techniques, should provide valuable insights to policy makers such as principal and chief dental officers to inform care pathways and policy decisions

Web-based support for managing large collections of software artefacts

There has been a long history of CASE tool development, with an underlying software repository at the heart of most systems. Usually such tools, even the more recently web-based systems, are focused on supporting individual projects within an enterprise or across a number of distributed sites. Little support for maintaining large heterogeneous collections of software artefacts across a number of projects has been developed. Within the GENESIS project, this has been a key consideration in the development of the Open Source Component Artefact Repository (OSCAR). Its most recent extensions are explicitly addressing the provision of cross project global views of large software collections as well as historical views of individual artefacts within a collection. The long-term benefits of such support can only be realised if OSCAR is widely adopted and various steps to facilitate this are described

Developing a log file analysis tool:a machine learning approach for anomaly detection

Abstract. Log files, which record information about all events during the execution of a software, are important in troubleshooting tasks. However, modern software systems produce large quantities of complex logs, and their manual inspection is laborious and time-consuming. Therefore, technologies such as machine learning have been used to automate log file analysis. Anomaly detection is an especially popular approach, since anomalies in the log files are typically caused by erroneous behaviour of the software. In this study, open source data mining and machine learning solutions are utilized to process log files collected from devices running embedded Linux. Following the Design Science Research methodology, a Python program called sgologs is developed. The tool uses components from logparser and loglizer toolkits to pre-process the input log file, train an unsupervised machine learning model, and detect anomalies on the input file. The loglizer tools have not been used with Linux logs in previous research, possibly because they are rather difficult for automated processing. This finding is verified in this study as well, as the measured anomaly detection accuracy scores are quite modest. Nevertheless, sgologs is able to detect anomalies in the log files, with swift processing times, at least when certain things are taken into consideration. If the user is aware of these factors, sgologs can definitely point towards real anomalies in the Linux log files. Thus, the tool could be used in real-life settings to simplify debugging tasks, whenever logs are used as a source of information

NPC AI System Based on Gameplay Recordings

Hästi optimeeritud mitte-mängija tegelased (MMT) on vastaste või meeskonna kaaslastena üheks peamiseks osaks mitme mängija mängudes. Enamus mänguroboteid on ehitatud jäikade süsteemide peal, mis võimaldavad vaid loetud arvu otsuseid ja animatsioone. Kogenud mängijad suudavad eristada mänguroboteid inimmängijatest ning ette ennustada nende liigutusi ja strateegiaid. See alandab mängukogemuse kvaliteeti. Seetõttu, eelistavad mitme mängijaga mängude mängijad mängida pigem inimmängijate kui MMTde vastu. Virtuaalreaalsuse (VR) mängud ja VR mängijad on siiani veel väike osa mängutööstusest ja mitme mängija VR mängud kannatavad mängijabaasi kaotusest, kui mänguomanikud ei suuda leida teisi mängijaid, kellega mängida. See uurimus demonstreerib mängulindistustel põhineva tehisintellekt (TI) süsteemi rakendatavust VR esimese isiku vaates tulistamismängule Vrena. Teemamäng kasutab ebatavalist liikumisesüsteemi, milles mängijad liiguvad otsiankrute abil. VR mängijate liigutuste imiteerimiseks loodi AI süsteem, mis kasutab mängulindistusi navigeerimisandmetena. Süsteem koosneb kolmest peamisest funktsionaalsusest. Need funktsionaalsused on mängutegevuse lindistamine, andmete töötlemine ja navigeerimine. Mängu keskkond on tükeldatud kuubikujulisteks sektoriteks, et vähendada erinevate asukohal põhinevate olekute arvu ning mängutegevus on lindistatud ajaintervallide ja tegevuste põhjal. Loodud mängulogid on segmenteeritud logilõikudeks ning logilõikude abil on loodud otsingutabel. Otsingutabelit kasutatakse MMT agentide navigeerimiseks ning MMTde otsuste langetamise mehanism jäljendab olek-tegevus-tasu kontseptsiooni. Loodud töövahendi kvaliteeti hinnati uuringu põhjal, millest saadi märkimisväärset tagasisidet süsteemi täiustamiseks.A well optimized Non-Player Character (NPC) as an opponent or a teammate is a major part of the multiplayer games. Most of the game bots are built upon a rigid system with numbered decisions and animations. Experienced players can distinguish bots from hu-man players and they can predict bot movements and strategies. This reduces the quality of the gameplay experience. Therefore, multiplayer game players favour playing against human players rather than NPCs. VR game market and VR gamers are still a small frac-tion of the game industry and multiplayer VR games suffer from loss of their player base if the game owners cannot find other players to play with. This study demonstrates the applicability of an Artificial Intelligence (AI) system based on gameplay recordings for a Virtual Reality (VR) First-person Shooter (FPS) game called Vrena. The subject game has an uncommon way of movement, in which the players use grappling hooks to navigate. To imitate VR players’ movements and gestures an AI system is developed which uses gameplay recordings as navigation data. The system contains three major functionality. These functionalities are gameplay recording, data refinement, and navigation. The game environment is sliced into cubic sectors to reduce the number of positional states and gameplay is recorded by time intervals and actions. Produced game logs are segmented into log sections and these log sections are used for creating a look-up table. The lookup table is used for navigating the NPC agent and the decision mechanism followed a way similar to the state-action-reward concept. The success of the developed tool is tested via a survey, which provided substantial feedback for improving the system