Towards Automated Performance Bug Identification in Python

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs

"Last-Mile" preparation for a potential disaster

Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of small-scale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socio-economic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity

GIS Application to Support Land Administration Services in Ghana: Institutional Factors and Software Developments

In June 1999, the Ghanaian Government launched a new land policy document that sought to address some fundamental problems associated with land administration and management in the country. The document identified the weak land administration system as a particular problem and recommended the introduction of computer-aided information systems in the ‘lands sector’. In 2001, the Government made further proposals to prepare and implement a Land Administration Programme (LAP) to provide a better platform for evolving an efficient land administration that would translate the ‘National Land Policy’ into action. Thus, an up-to-date land information system (LIS), supporting efficient management of land records, is to be constructed, which provides a context for the research reported in this paper. We document two aspects of our research on the adoption of GIS by the Lands Commission Secretariat (LCS) which form part of a pilot project in GIS diffusion. Part one of the paper mainly outlines the empirical results arising from fieldwork undertaken during 2001 to determine the information and GIS requirements of the LCS in relation to their routine administrative processes and to identify the critical factors that are required to ensure that any new GIS applications are successfully embraced. Part two explains the prototype software system developed using ArcView 3.2 and Access that provides the LCS with a means to automate some of the routine administrative tasks that they are required to fulfil. The software has been modified and upgraded following an initial evaluation by LCS employees also conducted as part of the fieldwork in Accra

EU–originated MOOCs, with focus on multi- and single-institution platforms

No abstract available

Toward high-content/high-throughput imaging and analysis of embryonic morphogenesis

In vivo study of embryonic morphogenesis tremendously benefits from recent advances in live microscopy and computational analyses. Quantitative and automated investigation of morphogenetic processes opens the field to high-content and high-throughput strategies. Following experimental workflow currently developed in cell biology, we identify the key challenges for applying such strategies in developmental biology. We review the recent progress in embryo preparation and manipulation, live imaging, data registration, image segmentation, feature computation, and data mining dedicated to the study of embryonic morphogenesis. We discuss a selection of pioneering studies that tackled the current methodological bottlenecks and illustrated the investigation of morphogenetic processes in vivo using quantitative and automated imaging and analysis of hundreds or thousands of cells simultaneously, paving the way for high-content/high-throughput strategies and systems analysis of embryonic morphogenesis