Scrum (développement)

Résumé

thumb|Schéma d'évènements du framework Scrum. Scrum est un framework ou cadre de développement de produits complexes. Il est défini par ses créateurs comme un « cadre de travail holistique itératif qui se concentre sur les buts communs en livrant de manière productive et créative des produits de la plus grande valeur possible ». Scrum est considéré comme un groupe de pratiques répondant pour la plupart aux préconisations du manifeste agile. Scrum s'appuie sur le découpage d'un projet en « boîtes de temps », nommées sprints (« pointes de vitesse »). Les sprints peuvent durer entre quelques heures et un mois (avec un sprint médian à deux semaines). Chaque sprint commence par une estimation suivie d'une planification opérationnelle. Le sprint se termine par une démonstration de ce qui a été achevé. Avant de démarrer un nouveau sprint, l'équipe réalise une rétrospective. Cette technique analyse le déroulement du sprint achevé, afin d'améliorer ses pratiques. Le flux de travail de l'équipe

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https://fr.wikipedia.org/wiki/Scrum_(d%C3%A9veloppement)

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Many policies are spatially targeted: they attempt to foster or incent policy actions in areas meeting specific criteria. The identification and delineation of the spatial reality of interest is a major challenge to spatially targeted policies. In many cases, this spatial reality has an ambiguous meaning and no coterminous governing institution, leading to the need for institutional collaboration around policies. This project investigates the discrepancy occurring when the functional space relevant for polices and the institutional space of government produced by historical processes do not share the same extent. The research examines two spatial realities of importance for public policies in the United States: metropolitan areas and disadvantaged communities. Because they are the main social unit of urban life, metropolitan areasâsuch as the San Francisco Bay Areaâare the target of housing or economic development policies. Disadvantaged communities are targeted by programs intending to mitigate inequalities between communities. In both cases, specialists and laymen alike have a sense of what these spatial realities are, but charting a robust, actionable definition is problematic. The three articles of this doctoral project form a coherent path to scrutinize and resolve the spatial reality / institutional space disjunction. The first, Identifying the space of urban life, investigates spatial practices of individuals and how, collectively, they cast a unit of urban life, the metropolitan area. The second paper, On point: designing robust spatial targeting for public policies, analyzes how these practices can be integrated in a robust policy targeting, taking the example of disadvantaged communities in California. The third paper, The place and scale of consent takes a bottom-up approach to show how individuals perceive and express preferences for resolving that discrepancy. I show that neither of the currently used definitions for metropolitan areas and disadvantaged communities is robust because they use a strict threshold on a continuous metric that is not exhibiting clear breakpoints. Furthermore, definitions are not geographically consistent. A single definition does not capture the same reality of a metropolitan area in a coastal region or in the rural west, or of a disadvantaged community in a large urban region or in a rural county. Therefore, these definitions fail to provide a dependable policy target. Moreover, I show that local preferences for policy collaboration to resolve the discrepancy vary geographically, further undermining the relevance of a one-size-fits-all definition. I recommend that definitions of spatial targets for policies use a continuous scale instead of a discrete, binary one. These definitions should also be adapted to the geographical context they are applied in. Randomized vignette experiments can be used to understand residents' preferences.

EPFL2017

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The Model Architecture Frame: Quality Managment in a Multi Method Environment

Most quality management systems (QMS) which integrate software development are based on a single software development method. This particular method is enforced on all projects. The resulting QMS has to fulfil the ISO 9000 standards [13] and enables the control of the projects and the improvement of the development process, supporting it with all necessary guidelines and providing one terminology for the whole company. However, using only one specific method is not necessarily the best solution for a company with very heterogeneous projects and products. Requirements of customers, existing experience and training in the teams, projects still using structured methods and newer ones using object-oriented methods, adaptation of methods for special circumstances, and evolving modelling techniques, all of these factors call for the coexistence of different modelling methods within one company. If a QMS is based on only a single method, many projects might run outside the quality management regulations, unless the QMS excludes the details of the software development process. This problem can be overcome by the use of model architectures and model architecture frames. A model architecture describes the models that are developed within one specific method or project. All the different model architectures are derived from the generic model architecture frame. Such a frame, and not just one specific method, becomes the basis for the company wide regulations. This paper describes reasons for having multiple methods and life cycle models within one QMS, explains the basic concepts of defining a QMS using model architectures and model architecture frames, and shows how this approach may be applied in a project.

1995

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Current Practice in Software Development for Computational Neuroscience and How to Improve It

Marc-Oliver Gewaltig

Almost all research work in computational neuroscience involves software. As researchers try to understand ever more complex systems, there is a continual need for software with new capabilities. Because of the wide range of questions being investigated, new software is often developed rapidly by individuals or small groups. In these cases, it can be hard to demonstrate that the software gives the right results. Software developers are often open about the code they produce and willing to share it, but there is little appreciation among potential users of the great diversity of software development practices and end results, and how this affects the suitability of software tools for use in research projects. To help clarify these issues, we have reviewed a range of software tools and asked how the culture and practice of software development affects their validity and trustworthiness. We identified four key questions that can be used to categorize software projects and correlate them with the type of product that results. The first question addresses what is being produced. The other three concern why, how, and by whom the work is done. The answers to these questions show strong correlations with the nature of the software being produced, and its suitability for particular purposes. Based on our findings, we suggest ways in which current software development practice in computational neuroscience can be improved and propose checklists to help developers, reviewers, and scientists to assess the quality of software and whether particular pieces of software are ready for use in research.

Public Library of Science2014

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EPFL2017