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DevOps aims to shorten project schedules, boost productivity, and manage quick development-deployment cycles without compromising business or quality. It necessitates good sprint management. Continuous Testing detects integration issues considerably earlier in the development process. It reduces the cost of defect resolution and frees up the tester's time for exploratory testing and value-added activities. Continuous testing allows for more frequent, shorter, and more efficient releases. It ties people, technology, and processes together. Continuous Planning, particularly effort estimation, is closely linked to Continuous Testing. This paper examines the state of the art in DevOps parametric estimate in continuous planning, as well as the difficulties and best practises. Keywords: Project, Testing, Continuous, Planning

International audience; Recent technological developments in robotics and artificial intelligence may enable the deployment of robots in many aspects of our lives. As the complexity of robotic platforms increases, deliberation algorithms need to be improved, in particular to handle an increasing number of agents, to manage complex goals and tasks, and to evolve in more open environments in which unforeseen events should be dealt with autonomously. Among the deliberation functionalities deployed to provide the best level of autonomy: planning, monitoring, learning, observing, we focus here on acting. We present OMPAS, a refinement-based acting engine that executes high-level tasks by refining them into a set of lowerlevel tasks and commands. OMPAS uses a custom Lisp dialect (SOMPAS) to define the behavior of the robotic agent. SOMPAS provides primitives to handle concurrency and resources, and allows the synthesis of planning models, thanks to the restricted core language, and the explicit identification of acting decisions. The engine has been extended to deploy a continuous planning module, using the synthesized models, to look ahead and guide the decisions of the acting system in order to e.g., avoid deadlock, or optimize the completion of several parallel tasks. We provide an evaluation of the overall approach on the control of a fleet of robots in a simulated logistic platform.

Da Logistikflächen für innerstädtische Fabriken nur begrenzt verfügbar sind, ist deren bestmögliche Nutzung bedeutsam. Der Einsatz von Softwarewerkzeugen ist in der Neuplanung von Logistikflächen gängige Praxis. Die effiziente Nutzung bestehender Flächen in Anbetracht geänderter Anforderungen wird aber selten kontinuierlich überprüft. Der Beitrag zeigt, wie eine kontinuierliche planerische Restrukturierung von Logistikflächen durch ein digitales Assistenzsystem unterstützt werden kann. Due to limited available space in urban production plants, the best possible use of logistic and storage areas is very important. The use of software tools is common practice for the planning of new logistics areas. However, continuous monitoring of the efficient use of existing areas due to changing requirements is only rarely implemented. This article describes how a continuous restructuring planning of logistics areas can be supported by a decision support system.

The concept of continuous planning (CP) refers to the periodic revisions of master plans in response to uncertainties faced by long-term urban planning. In this paper, we analyse CP practices in two European cities – Stavanger (Norway) and Belgrade (Serbia) and present how they cater for innovative planning tools which respond to the need to balance between both stability and change in long-term planning. We argue that in spite of its limitations, the CP approach adjusted to the local context, contributes to the certainty and stability of urban communities.

Kонтинуирано планирање је формалан репетитиван процес ритмичког преиспитивања урбанистичког или регионалног плана, са намером да се план ревидира и мења уколико се покажу несагласности са реалношћу или смер развоја који не иде ка стању града које заједница види као пожељно. Прве идеје о континуираном планирању у домаћој средини јавиле су се раних 1970-их, али нису пренете у праксу. Пуна примена овог планерског концепта повезана је са израдом, спровођењем и ревизијама Генералног плана Београда 2021, усвојеног 2003. године. Овај чланак приказује појаву, успон и пад континуираног планирања у урбанистичкој пракси у Србији. Циљ нам је да укажемо на значај примене концепта континуалног планирања у развоју градова и покренемо дебату о његовом враћању у домаћу планерску праксу. Continuous planning is a formal, repetitive process of rhythmic revision of an urban or regional plan, intending to revise and change the plan if there are inconsistencies with reality or the development is not in the direction of the state of the city that the community perceives as desirable. The first ideas about continuous planning in Serbia appeared in the early 1970s but did not come into practice. The full implementation of this planning concept is related to the formulation, implementation and revision of the General Plan of Belgrade 2021, adopted in 2003. This article presents the emergence, rise and fall of continuous planning in urban practice in Serbia. We aim to point out the importance of the concept of continuous planning to city development and to initiate a debate on its return to Serbian planning practice.

Despite the many problems the COVID-19 pandemic creates in the economies worldwide, recent research in academia tries to find new ways to support enterprises and companies counteracting the crisis. This study discusses the question of how an agile project budgeting resp. The Continuous Planning and Forecasting Framework (CPFF) for agile project management can be used to support teams working in an "agilefall" (the in-between state of traditional and agile) environment to reach a certain level of certainty under uncertain conditions. This contribution refines the first drafted Continuous Forecasting Framework, presented on the Software Engineering 2020, incorporating feedback from academia and practitioners using agile methods. For readers who have never worked in a real agile environment, it is often difficult to grasp why getting off the strict path following an iterative beyond budgeting approach could increase certainty. Therefore, the authors depict the framework by applying it to specific problems within traditional project boundaries focusing on elements that could help teams overcoming the "agilefall"-budgeting trap.

The state of the art practice in robotics planning is to script behaviors manually, where each behavior is typically generated using trajectory optimization. However, in order for robots to be able to act robustly and adapt to novel situations, they need to plan these activity sequences autonomously. Since the conditions and effects of these behaviors are tightly coupled through time, state and control variables, many problems require that the tasks of activity planning and trajectory optimization are considered together. There are two key issues underlying effective hybrid activity and trajectory planning: the sufficiently accurate modeling of robot dynamics and the capability of planning over long horizons. Hybrid activity and trajectory planners that employ mixed integer programming within a discrete time formulation are able to accurately model complex dynamics for robot vehicles, but are often restricted to relatively short horizons. On the other hand, current hybrid activity planners that employ continuous time formulations can handle longer horizons but they only allow actions to have continuous effects with constant rate of change, and restrict the allowed state constraints to linear inequalities. This is insufficient for many robotic applications and it greatly limits the expressivity of the problems that these approaches can solve. In this work we present the ScottyActivity planner, that is able to generate practical hybrid activity and motion plans over long horizons by employing recent methods in convex optimization combined with methods for planning with relaxed plan graphs and heuristic forward search. Unlike other continuous time planners, ScottyActivity can solve a broad class of robotic planning problems by supporting convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. In order to support planning over long horizons, ScottyActivity does not resort to time, state or control variable discretization. While straightforward formulations of consistency checks are not convex and do not scale, we present an efficient convex formulation, in the form of a Second Order Cone Program (SOCP), that is very fast to solve. We also introduce several new realistic domains that demonstrate the capabilities and scalability of our approach, and their simplified linear versions, that we use to compare with other state of the art planners. This work demonstrates the power of integrating advanced convex optimization techniques with discrete search methods and paves the way for extensions dealing with non-convex disjoint constraints, such as obstacle avoidance.

This study addresses the topic of continuous planning in the agile and lean software development context. The paper investigates how software companies can achieve various benefits by dividing their product development practices along the three horizons of growth. This approach allows software companies to have different product development and planning practices inside the company, thereby strengthening their positions in well-established businesses as well as creating growth in completely new businesses. Theoretically, the paper draws on the business literature of the three horizon model and on the literature of agile and lean software development. The empirical part is based on a case study with a leading Finnish software company. Altogether, 12 narrative interviews were conducted, involving people with various roles from product manager to chief strategy officer. The research presents a new approach towards continuous planning within multiple business horizons, to address common challenges in managing software product portfolios in different maturity phases.