Mining developer communication data streams

This paper explores the concepts of modelling a software development project as a process that results in the creation of a continuous stream of data. In terms of the Jazz repository used in this research, one aspect of that stream of data would be developer communication. Such data can be used to create an evolving social network characterized by a range of metrics. This paper presents the application of data stream mining techniques to identify the most useful metrics for predicting build outcomes. Results are presented from applying the Hoeffding Tree classification method used in conjunction with the Adaptive Sliding Window (ADWIN) method for detecting concept drift. The results indicate that only a small number of the available metrics considered have any significance for predicting the outcome of a build

21st Century Simulation: Exploiting High Performance Computing and Data Analysis

This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success

Process of designing robust, dependable, safe and secure software for medical devices: Point of care testing device as a case study

This article has been made available through the Brunel Open Access Publishing Fund.Copyright © 2013 Sivanesan Tulasidas et al. This paper presents a holistic methodology for the design of medical device software, which encompasses of a new way of eliciting requirements, system design process, security design guideline, cloud architecture design, combinatorial testing process and agile project management. The paper uses point of care diagnostics as a case study where the software and hardware must be robust, reliable to provide accurate diagnosis of diseases. As software and software intensive systems are becoming increasingly complex, the impact of failures can lead to significant property damage, or damage to the environment. Within the medical diagnostic device software domain such failures can result in misdiagnosis leading to clinical complications and in some cases death. Software faults can arise due to the interaction among the software, the hardware, third party software and the operating environment. Unanticipated environmental changes and latent coding errors lead to operation faults despite of the fact that usually a significant effort has been expended in the design, verification and validation of the software system. It is becoming increasingly more apparent that one needs to adopt different approaches, which will guarantee that a complex software system meets all safety, security, and reliability requirements, in addition to complying with standards such as IEC 62304. There are many initiatives taken to develop safety and security critical systems, at different development phases and in different contexts, ranging from infrastructure design to device design. Different approaches are implemented to design error free software for safety critical systems. By adopting the strategies and processes presented in this paper one can overcome the challenges in developing error free software for medical devices (or safety critical systems).Brunel Open Access Publishing Fund