Change-Enabled Software Systems

Few real software systems are built completely from scratch nowadays. Instead, systems are built iteratively and incrementally, while integrating and interacting with components from many other systems. Adaptation, reconfiguration and evolution are normal, ongoing processes throughout the lifecycle of a software system. Nevertheless the platforms, tools and environments we use to develop software are still largely based on an outmoded model that presupposes that software systems are closed and will not significantly evolve after deployment. We claim that in order to enable effective and graceful evolution of modern software systems, we must make these systems more amenable to change by (i) providing explicit, first-class models of software artifacts, change, and history at the level of the platform, (ii) continuously analysing static and dynamic evolution to track emergent properties, and (iii) closing the gap between the domain model and the developers' view of the evolving system. We outline our vision of dynamic, evolving software systems and identify the research challenges to realizing this vision.

Comparative Analysis of Evolving Software Systems Using the Gini Coefficient

Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics --- for example, in terms of "average" values --- can be highly misleading. Many metrics, it turns out, are distributed like wealth --- with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.

Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

An Exploratory Study of Data Quality Management Practices in the ERP Software Systems Context

Quality data are not only relevant for successful Data Warehousing or Business Intelligence applications; they are also a precondition for efficient and effective use of Enterprise Resource Planning (ERP) systems. ERP professionals in all kinds of businesses are concerned with data quality issues, as a survey, conducted by the Institute of Information Systems at the University of Bern, has shown. This paper demonstrates, by using results of this survey, why data quality problems in modern ERP systems can occur and suggests how ERP researchers and practitioners can handle issues around the quality of data in an ERP software Environment.

An architectural model for software testing lesson learned systems

Software testing is a key aspect of software reliability and quality assurance in a context where software development constantly has to overcome mammoth challenges in a continuously changing environment. One of the characteristics of software testing is that it has a large intellectual capital component and can thus benefit from the use of the experience gained from past projects. Software testing can, then, potentially benefit from solutions provided by the knowledge management discipline. There are in fact a number of proposals concerning effective knowledge management related to several software engineering processes. Objective: We defend the use of a lesson learned system for software testing. The reason is that such a system is an effective knowledge management resource enabling testers and managers to take advantage of the experience locked away in the brains of the testers. To do this, the experience has to be gathered, disseminated and reused. Method: After analyzing the proposals for managing software testing experience, significant weaknesses have been detected in the current systems of this type. The architectural model proposed here for lesson learned systems is designed to try to avoid these weaknesses. This model (i) defines the structure of the software testing lessons learned; (ii) sets up procedures for lesson learned management; and (iii) supports the design of software tools to manage the lessons learned. Results: A different approach, based on the management of the lessons learned that software testing engineers gather from everyday experience, with two basic goals: usefulness and applicability. Conclusion: The architectural model proposed here lays the groundwork to overcome the obstacles to sharing and reusing experience gained in the software testing and test management. As such, it provides guidance for developing software testing lesson learned systems.

Caracterización de Sistemas intensivos en Software desde un punto de vista de innovación=Characterizing Software-intensive Systems from the innovation point of view

La innovación en Sistemas Intesivos en Software está alcanzando relevancia por múltiples razones: el software está presente en sectores como automóvil, teléfonos móviles o salud. Las empresas necesitan conocer aquellos factores que afectan a la innovación para incrementar las probabilidades de éxito en el desarrollo de sus productos y, la evaluación de productos sofware es un mecanismo potente para capturar este conocimiento. En consecuencia, las empresas necesitan evaluar sus productos desde la perpectiva de innovación para reducir la distancia entre los productos desarrollados y el mercado. Esto es incluso más relevante en el caso de los productos intensivos en software, donde el tiempo real, la oportunidad, complejidad, interoperabilidad, capacidad de respuesta y compartción de recursos son características críticas de los nuevos sistemas. La evaluación de la innovación de productos ya ha sido estudiada y se han definido algunos esquemas de evaluación pero no son específicos para Sistemas intensivos en Sofwtare; además, no se ha alcanzado consenso en los factores ni el procedimiento de evaluación. Por lo tanto, tiene sentido trabajar en la definición de un marco de evaluación de innovación enfocado a Sistemas intesivos en Software. Esta tesis identifica los elementos necesarios para construir in marco para la evaluación de de Sistemas intensivos en Software desde el punto de vista de la innovación. Se han identificado dos componentes como partes del marco de evaluación: un modelo de referencia y una herramienta adaptativa y personalizable para la realización de la evaluación y posicionamiento de la innovación. El modelo de referencia está compuesto por cuatro elementos principales que caracterizan la evaluación de innovación de productos: los conceptos, modelos de innovación, cuestionarios de evaluación y la evaluación de productos. El modelo de referencia aporta las bases para definir instancias de los modelos de evaluación de innovación de productos que pueden se evaluados y posicionados en la herramienta a través de cuestionarios y que de forma automatizada aporta los resultados de la evaluación y el posicionamiento respecto a la innovación de producto. El modelo de referencia ha sido rigurosamente construido aplicando modelado conceptual e integración de vistas junto con la aplicación de métodos cualitativos de investigación. La herramienta ha sido utilizada para evaluar productos como Skype a través de la instanciación del modelo de referencia. ABSTRACT Innovation in Software intensive Systems is becoming relevant for several reasons: software is present embedded in many sectors like automotive, robotics, mobile phones or heath care. Firms need to have knowledge about factors affecting the innovation to increase the probability of success in their product development and the assessment of innovation in software products is a powerful mechanism to capture this knowledge. Therefore, companies need to assess products from an innovation perspective to reduce the gap between their developed products and the market. This is even more relevant in the case of SiSs, where real time, timeliness, complexity, interoperability, reactivity, and resource sharing are critical features of a new system. Many authors have analysed product innovation assessment and some schemas have been developed but they are not specific to SiSs; in addition, there is no consensus about the factors or the procedures for performing an assessment. Therefore, it has sense to work in the definition of a customized software product innovation evaluation framework. This thesis identifies the elements needed to build a framework to assess software products from the innovation perspective. Two components have been identified as part of the framework to assess Software intensive Systems from the innovation perspective: a reference-model and an adaptive and customizable tool to perform the assessment and to position product innovation. The reference-model is composed by four main elements characterizing product innovation assessment: concepts, innovation models, assessment questionnaires and product assessment. The reference model provides the umbrella to define instances of product innovation assessment models that can be assessed and positioned through questionnaires in the proposed tool that also provides automation in the assessment and positioning of innovation. The reference-model has been rigorously built by applying conceptual modelling and view integration integrated with qualitative research methods. The tool has been used to assess products like Skype through models instantiated from the reference-model.

Modelling spatial decisión support systems focused on the development of a data warehouse: applying software engineering

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning médium and long-term ?the modus operandi? of these organizations. Despite the growing importance of these systems, most proposals do not include its total evelopment, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

An application of fuzzy logic for hardware/software partitioning in embedded systems

Hardware/Software partitioning (HSP) is a key task for embedded system co-design. The main goal of this task is to decide which components of an application are to be executed in a general purpose processor (software) and which ones, on a specific hardware, taking into account a set of restrictions expressed by metrics. In last years, several approaches have been proposed for solving the HSP problem, directed by metaheuristic algorithms. However, due to diversity of models and metrics used, the choice of the best suited algorithm is an open problem yet. This article presents the results of applying a fuzzy approach to the HSP problem. This approach is more flexible than many others due to the fact that it is possible to accept quite good solutions or to reject other ones which do not seem good. In this work we compare six metaheuristic algorithms: Random Search, Tabu Search, Simulated Annealing, Hill Climbing, Genetic Algorithm and Evolutionary Strategy. The presented model is aimed to simultaneously minimize the hardware area and the execution time. The obtained results show that Restart Hill Climbing is the best performing algorithm in most cases.

Selective SWIFT-R. A Flexible Software-Based Technique for Soft Error Mitigation in Low-Cost Embedded Systems

Commercial off-the-shelf microprocessors are the core of low-cost embedded systems due to their programmability and cost-effectiveness. Recent advances in electronic technologies have allowed remarkable improvements in their performance. However, they have also made microprocessors more susceptible to transient faults induced by radiation. These non-destructive events (soft errors), may cause a microprocessor to produce a wrong computation result or lose control of a system with catastrophic consequences. Therefore, soft error mitigation has become a compulsory requirement for an increasing number of applications, which operate from the space to the ground level. In this context, this paper uses the concept of selective hardening, which is aimed to design reduced-overhead and flexible mitigation techniques. Following this concept, a novel flexible version of the software-based fault recovery technique known as SWIFT-R is proposed. Our approach makes possible to select different registers subsets from the microprocessor register file to be protected on software. Thus, design space is enriched with a wide spectrum of new partially protected versions, which offer more flexibility to designers. This permits to find the best trade-offs between performance, code size, and fault coverage. Three case studies have been developed to show the applicability and flexibility of the proposal.

An Exploration of the challenges associated with software logging in large systems

Thesis (Master, Computing) -- Queen's University, 2016-05-29 18:11:34.114

Standardization, certification, maintenance, and dissemination of large scale engineering software systems : a panel discussion held as a part of the Second National Symposium on Computerized Structural Analysis and Design /

Includes bibliographical references