A mapping study on documentation in Continuous Software Development

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

Capturing variability in Model Based Systems Engineering

International audienceAutomotive model-based systems engineering needs to be dapted to the industry specific needs, in particular by implementing appropriate means of representing and operating with variability. We rely on existing modeling techniques as an opportunity to provide a description of variability adapted to a systems en- gineering model. However, we also need to take into account requirements related to backwards compatibility with current practices, given the industry experience in mass customization. We propose to adopt the product line paradigm in model-based systems engineering by extending the orthogonal variability model, and adapting it to our specific needs. This brings us to an expression closer to a description of constraints, related to both orthogonal variability, and to SysML system models. We introduce our approach through a discussion on the different aspects that need to be covered for expressing variability in systems engineering. We explore these aspects by observing an automotive case study, and relate them to a list of contextual requirements for variability management

Geospatial crowdsourced data fitness analysis for spatial data infrastructure based disaster management actions

The reporting of disasters has changed from official media reports to citizen reporters who are at the disaster scene. This kind of crowd based reporting, related to disasters or any other events, is often identified as 'Crowdsourced Data' (CSD). CSD are freely and widely available thanks to the current technological advancements. The quality of CSD is often problematic as it is often created by the citizens of varying skills and backgrounds. CSD is considered unstructured in general, and its quality remains poorly defined. Moreover, the CSD's location availability and the quality of any available locations may be incomplete. The traditional data quality assessment methods and parameters are also often incompatible with the unstructured nature of CSD due to its undocumented nature and missing metadata. Although other research has identified credibility and relevance as possible CSD quality assessment indicators, the available assessment methods for these indicators are still immature. In the 2011 Australian floods, the citizens and disaster management administrators used the Ushahidi Crowd-mapping platform and the Twitter social media platform to extensively communicate flood related information including hazards, evacuations, help services, road closures and property damage. This research designed a CSD quality assessment framework and tested the quality of the 2011 Australian floods' Ushahidi Crowdmap and Twitter data. In particular, it explored a number of aspects namely, location availability and location quality assessment, semantic extraction of hidden location toponyms and the analysis of the credibility and relevance of reports. This research was conducted based on a Design Science (DS) research method which is often utilised in Information Science (IS) based research. Location availability of the Ushahidi Crowdmap and the Twitter data assessed the quality of available locations by comparing three different datasets i.e. Google Maps, OpenStreetMap (OSM) and Queensland Department of Natural Resources and Mines' (QDNRM) road data. Missing locations were semantically extracted using Natural Language Processing (NLP) and gazetteer lookup techniques. The Credibility of Ushahidi Crowdmap dataset was assessed using a naive Bayesian Network (BN) model commonly utilised in spam email detection. CSD relevance was assessed by adapting Geographic Information Retrieval (GIR) relevance assessment techniques which are also utilised in the IT sector. Thematic and geographic relevance were assessed using Term Frequency – Inverse Document Frequency Vector Space Model (TF-IDF VSM) and NLP based on semantic gazetteers. Results of the CSD location comparison showed that the combined use of non-authoritative and authoritative data improved location determination. The semantic location analysis results indicated some improvements of the location availability of the tweets and Crowdmap data; however, the quality of new locations was still uncertain. The results of the credibility analysis revealed that the spam email detection approaches are feasible for CSD credibility detection. However, it was critical to train the model in a controlled environment using structured training including modified training samples. The use of GIR techniques for CSD relevance analysis provided promising results. A separate relevance ranked list of the same CSD data was prepared through manual analysis. The results revealed that the two lists generally agreed which indicated the system's potential to analyse relevance in a similar way to humans. This research showed that the CSD fitness analysis can potentially improve the accuracy, reliability and currency of CSD and may be utilised to fill information gaps available in authoritative sources. The integrated and autonomous CSD qualification framework presented provides a guide for flood disaster first responders and could be adapted to support other forms of emergencies

Toward timely, predictable and cost-effective data analytics

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to extract deep insights is now a key ingredient for success. Traditional database systems (DBMS) are, however, not the first choice for servicing these modern applications, despite 40 years of database research. This is due to the fact that modern applications exhibit different behavior from the one assumed by DBMS: a) timely data exploration as a new trend is characterized by ad-hoc queries and a short user interaction period, leaving little time for DBMS to do good performance tuning, b) accurate statistics representing relevant summary information about distributions of ever increasing data are frequently missing, resulting in suboptimal plan decisions and consequently poor and unpredictable query execution performance, and c) cloud service providers - a major winner in the data analytics game due to the low cost of (shared) storage - have shifted the control over data storage from DBMS to the cloud providers, making it harder for DBMS to optimize data access. This thesis demonstrates that database systems can still provide timely, predictable and cost-effective analytical processing, if they use an agile and adaptive approach. In particular, DBMS need to adapt at three levels (to workload, data and hardware characteristics) in order to stabilize and optimize performance and cost when faced with requirements posed by modern data analytics applications. Workload-driven data ingestion is introduced with NoDB as a means to enable efficient data exploration and reduce the data-to-insight time (i.e., the time to load the data and tune the system) by doing these steps lazily and incrementally as a side-effect of posed queries as opposed to mandatory first steps. Data-driven runtime access path decision making introduced with Smooth Scan alleviates suboptimal query execution, postponing the decision on access paths from query optimization, where statistics are heavily exploited, to query execution, where the system can obtain more details about data distributions. Smooth Scan uses access path morphing from one physical alternative to another to fit the observed data distributions, which removes the need for a priori access path decisions and substantially improves the predictability of DBMS. Hardware-driven query execution introduced with Skipper enables the usage of cold storage devices (CSD) as a cost-effective solution for storing the ever increasing customer data. Skipper uses an out-of-order CSD-driven query execution model based on multi-way joins coupled with efficient cache and I/O scheduling policies to hide the non-uniform access latencies of CSD. This thesis advocates runtime adaptivity as a key to dealing with raising uncertainty about workload characteristics that modern data analytics applications exhibit. Overall, the techniques introduced in this thesis through the three levels of adaptivity (workload, data and hardware-driven adaptivity) increase the usability of database systems and the user satisfaction in the case of big data exploration, making low-cost data analytics reality

New challenges in wireless and free space optical communications

AbstractThis manuscript presents a survey on new challenges in wireless communication systems and discusses recent approaches to address some recently raised problems by the wireless community. At first a historical background is briefly introduced. Challenges based on modern and real life applications are then described. Up to date research fields to solve limitations of existing systems and emerging new technologies are discussed. Theoretical and experimental results based on several research projects or studies are briefly provided. Essential, basic and many self references are cited. Future researcher axes are briefly introduced

The Institutionalisation of European Spatial Planning

The Institutionalisation of European Spatial Planning aims to clarify the enterprise of European spatial planning. The emphasis of the book lies on the need for a better understanding of the process of European integration in general. It particularly points at the emerging middle range theories that used concepts that were showing similarity to those that academics –those writing about planning– were accustomed to, such as networks, discourses and governance. The focus of this book is mostly on the post-European Spatial Development Perspective (ESDP)– the Committee of Spatial Development – from 1999 until now. As it is collection of articles, it has a different gestation process and does not tell a story from A to Z. What this book is about, however, is merely the issue concerning the institutional capacity of the ESDP and whether this has evaporated or not. The fact that this book exists at all suggests it has not

An Analytic Hierarchy Process Approach for Prioritisation of Strategic Objectives of Sustainable Development

Sustainability is one of the world’s fundamental objectives, and a wide variety of information types, parameters, and uncertainties need to be appraised and managed to assess it. In the present paper, Multi-Criteria Decision Analysis (MCDA) is used to prioritise the criteria of sustainable development based on regularly published indicators. In line with most approaches in the literature, the main criteria are Economy, Society and Environment. Complex criteria are decomposed into subcriteria until the performance with respect to them can be measured directly. Weights of importance are calculated by the Analytic Hierarchy Process (AHP), in decision support system PriEsT. The model is flexible to both the modification of criteria and re-weighting, and the PriEsT file is supplemented to the paper. Moreover, the results can also be applied in decisions on resource allocation. The proposed methodology has the potential of resulting in a new composite index to measure, compare or rank countries and regions regarding sustainable development or one of its subcriteria, as well as to track, year by year, the improvements or the impact of the policies introduced

sustainability and resilience:

Sustainability and resilience have become indispensable parts of the contemporary debate over the built environment. Although recognised as imperatives, the complexity and the variety of interpretations of sustainability and resilience have raised the necessity to again rethink their notion in the context of the built environment and to reframe the state-of-the-art body of knowledge. The purpose of this book is to present ongoing research from the universities involved in the project Creating the Network of Knowledge Labs for Sustainable and Resilient Environments (KLABS). The book Sustainability and Resilience: Socio-Spatial Perspective so begins with the exploration of the broadest conceptual frame-of-reference of issues related to sustainability, and the re-establishment of the connection between the built environment and the conditions that are vital to its functioning, primarily in relation to energy, land use, climate, and economy. Subsequent discussion on resilience as a term, approach, and philosophy aims to conceptualise an interpretation of key resilience concepts, explain relationships and links among them, and propose the classification of resilience as applicable to the context of urban studies. By studying the processes of transition of the built environment, the book then reveals a coherent formula of ‘thinking sustainability + resilience’ aimed at improving the ability to respond to disruptions and hazards while enhancing human and environmental welfare. The necessity to integrate the two approaches is further accented as a result of a deliberative discourse on the notions of ‘social sustainability’, ‘sustainable community’, and ‘socio-cultural resilience’. The potential of measuring sustainable development and urban sustainability on the basis of defined social, human, and, additionally, natural and economic values is presented through an overview of different well-known indicators and the identification of a currently relevant tangible framework of sustainable development. Correspondingly, the role of policies and governance is demonstrated in the case of climate-proof cities. In this way, the consideration of approaches to sustainability and resilience of the urban environment is rounded, and the focus of the book is shifted towards an urban/rural dichotomy and the sustainability prospects of identified forms-in-between, and, subsequently, towards the exploration of values, challenges, and the socio-cultural role in achieving sustainability for rural areas. In the final chapters, the book offers several peculiarized socio-spatial perspectives, from defining the path towards more resilient communities and sustainable spaces based on a shared well-being to proposing the approach to define community resilience as an intentional action that aims to respond to, and influence, the course of social and economic change, to deliberating the notion of a ’healthy place’ and questioning its optimal scale in the built environment. The study of sustainability and resilience in this book is concluded by drawing a parallel between environmental, economic, and social determinants of the built environment and the determinants that are relevant to human health and well-being

Food and sustainability: the sustainable food system index

Sustainable transitions have become the guiding principles of today’s century with food systems at the core of it. Food and nutrition illustrate a basic human need, inevitable for any living organism, and deeply entangled within the ecosystem. Thus, food systems represent predominant endeavours when aiming towards Agenda 2030. In line with displaying complex socio-ecological processes, heavily affected by ongoing interrelations among human and natural components, three categories identify as crucial for sustainable food systems – food security, ecosystem stability and resilience and sociocultural wellbeing. In combination with the research aim of assessing the sustainability degree of performing food systems in place, a Sustainable Food System Index (SFSI) has been established. The SFSI measures food system sustainability across 33 countries among 3 categories and 9 dimensions by employing 39 indicators, 65 sub-indicators and 38 sub-sub-indicators. The results display the varying degree of sustainable food systems among performing countries across selected indicators. The overall index score highlights Sweden as the front runner, closely followed by France and the United Kingdom, while laggards illustrate Ethiopia, Nigeria and India. With food systems being caught in a vicious circle with the ecosystem and the environment, economically poor regions are particularly vulnerable due to its smallholder dependency on agricultural systems. The SFSI outcomes provide more insights into the sustainability’s state of analysed food systems in the categories of food safety, ecosystem stability and resilience, as well as sociocultural wellbeing and might serve as foundation for future sustainable food system research.Transições sustentáveis têm se tornado os princípios orientadores do século de hoje, com sistemas alimentares em seu núcleo. Alimentação e nutrição ilustram uma necessidade humana básica, inevitável para qualquer organismo vivo e, profundamente entrelaçada com o ecossistema. Assim, sistemas alimentares representam esforços predominantes ao focar na Agenda 2030. Em consonância com a apresentação de processos socio-ecológicos complexos, fortemente afetados por interrelações entre componentes naturais e humanos em curso, três categorias identificam-se como essenciais aos sistemas alimentares sustentáveis: segurança alimentar, estabilidade e resiliência do ecossistema, e bem-estar sociocultural. Em combinação com o objetivo da pesquisa de avaliar o grau de desempenho de sustentabilidade de sistemas alimentares decorrentes, foi criado um Índice de Sistema Alimentar Sustentável (SAS). O SAS mede a sustentabilidade do sistema alimentar em 33 países dentro de 3 categorias e 9 dimensões ao empregar 39 indicadores, 65 sub-indicadores e 38 sub-sub indicadores. Os resultados mostram o grau variável de sistemas alimentares sustentáveis entre países nos indicadores selecionados. O resultado geral do índice destaca a Suécia como líder, seguida de perto por França e Reino Unido, enquanto como retardatários ilustram Etiópia, Nigéria e Índia. Com sistemas alimentares sendo apanhados em círculos viciosos com o ecossistema e meio-ambiente, regiões economicamente pobres são particularmente vulneráveis devido a suas baixas dependências em sistemas de agricultura. Os resultados do SAS fornecem mais insights no estado da sustentabilidade dos sistemas alimentares analisados nas categorias de segurança alimentar, estabilidade e resiliência do ecossistema, tanto quanto de bem-estar sociocultural e, deve servir como fundação para futuras pesquisas sobre sistema alimentar sustentável