/pdf/flood-impact-mitigation-and-resilience-enhancement-53jtqflg9t.pdf

Flood Impact Mitigation and Resilience Enhancement

Social network data, utilised as a VGI source, was analysed using the September 2016 flood event in Messinia, South Greece. The flood event led to damage in the urban and rural environment in the general area, and to human deaths. An innovative methodology is based on applying machine learning to classify Twitter content. Tweets were classified into the following ten categories: (i) flood identification, (ii) rain identification, (iii) consequences of the flood, (iv) expressed emotions, (v) ironic attitude to local disaster management authorities, (vi) disaster management information, (vii); volunteer actions, (viii); situation overview, (ix); social effects, and (x); weather information. Some of the categories were divided further, to quantify significant information. The classified output was sequentially geo-referenced by identifying geographic entities within the text of each post (geo-parsing) and replicating each post according to the number of geolocations. The data processing involved various geo-validations and performance metrics. The final output was used to create maps and graphs of different time periods, that provide useful insights into the flood event for disaster management purposes. The applied methodology is an evolution of previous research published by the author, this time providing complete results, based on the analysis of 100 % of the data available, with maps and graphs which demonstrate how the flood event unfolded in different time periods. The methodology is fully automated in terms of data processing, and can be applied using a script developed by the author in the R programming language. This research is a step towards the real-time delivery of advanced information for all disaster management stakeholders, from official authorities and rescue teams, to volunteers and locals who may be situated within the area of a disastrous flood occurrence.

A Methodology for Automatic Acquisition of Flood‐event Management Information From Social Media: the Flood in Messinia, South Greece, 2016

Context: With an increase in Agile, Lean, and DevOps software methodologies over the last years (collectively referred to as Continuous Software Development (CSD)), we have observed that documentation is often poor. Objective: This work aims at collecting studies on documentation challenges, documentation practices, and tools that can support documentation in CSD. Method: A systematic mapping study was conducted to identify and analyze research on documentation in CSD, covering publications between 2001 and 2019. Results: A total of 63 studies were selected. We found 40 studies related to documentation practices and challenges, and 23 studies related to tools used in CSD. The challenges include: informal documentation is hard to understand, documentation is considered as waste, productivity is measured by working software only, documentation is out-of-sync with the software and there is a short-term focus. The practices include: non-written and informal communication, the usage of development artifacts for documentation, and the use of architecture frameworks. We also made an inventory of numerous tools that can be used for documentation purposes in CSD. Overall, we recommend the usage of executable documentation, modern tools and technologies to retrieve information and transform it into documentation, and the practice of minimal documentation upfront combined with detailed design for knowledge transfer afterwards. Conclusion: It is of paramount importance to increase the quantity and quality of documentation in CSD. While this remains challenging, practitioners will benefit from applying the identified practices and tools in order to mitigate the stated challenges.

A mapping study on documentation in Continuous Software Development

Reducing the time taken to discover and fix vulnerabilities in open source software projects is increasingly relevant to technology entrepreneurs and technology managers at all levels of industry. Rigorous research requires access to valid and reliable data on when vulnerabilities were introduced, discovered, and closed. This article offers three contributions about measurement and data availability: (1) an approach to measuring the time to discover and time to fix vulnerabilities in open source software projects, (2) evidence that combining project release histories and metrics from two online databases can provide reliable proxy dates for vulnerability introduction and fix, but not discovery, and (3) possible technical and open collaboration solutions to the data availability limitations of current databases. These results were part of a larger mixed-method study on the relationship between open source project and community attributes and software vulnerabilities with a data set of 1268 vulnerabilities affecting the software produced by 60 open source projects.

Time to Discover and Fix Software Vulnerabilities in Open Source Software Projects: Notes on Measurement and Data Availability

The main purpose of this chapter is to introduce fundamental knowledge regarding the notion of volunteered geographic information (VGI) and its applications in disaster management (DM) of events related to floods. Initially, the meaning of the term is defined along with certain properties and general trends that characterize VGI. A brief literature review unfolds the range of activities that compose that certain term, along with its applications to flood event management. Those applications cover significant aspects of both VGI and DM cycle: from participatory activities of volunteers up to pure data analysis, extracted from social media and other VGI sources, while, in terms of DM cycle, from mitigation up to response and recovery. Finally, a set of four main clusters of open challenges is addressed. Those clusters accumulate the vast majority of open topics on this research field.

/pdf/fundamentals-of-volunteered-geographic-information-in-2tk5wqye5s.pdf

Fundamentals of Volunteered Geographic Information in Disaster Management Related to Floods

Linus' Law reflects on a key characteristic of open source software development: developers' tendency to closely work together in the bug resolution process. In this paper we empirically examine Linus' Law using a data-set of 1,000+ Android bugs, owned by 70+ developers. Our results indicate that encouraging developers to work closely with one another has nuanced implications; while one form of contact may help reduce bug resolution time, another form can have quite the opposite effect. We present statistically significant evidence in support of our results and discuss their relevance at the individual and organizational levels.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6583&context=sis_research

How many eyeballs does a bug need? an empirical validation of Linus' Law

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How many eyeballs does a bug need? an empirical validation of Linus' Law