For practical reasons, most scheduling problems are an abstraction of the real problem being solved. For example, when you plan your day, you schedule the activities which are critical;that is you schedule the activit...
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ISBN:
(纸本)3540292381
For practical reasons, most scheduling problems are an abstraction of the real problem being solved. For example, when you plan your day, you schedule the activities which are critical;that is you schedule the activities which are essential to the success of your day. So you may plan what time to leave the house to get to work, when to have meetings, how you share your vehicle with your spouse and so on. On the other hand, you probably do not consider the activities that are easy to arrange like brushing your teeth, going to the shops, making photocopies and other such tasks that can usually be accomplished whenever you have the time available. Scheduling all of these activities at once is often too complicated. Instead, a simpler schedule is produced by considering only the critical activities. However, if a schedule goes wrong, it is often because an activity turned out to be critical but was not scheduled. We typically learn which activities are critical by experience and create an abstract scheduling problem which includes all known critical activities. Instead of scheduling the non-critical activities we estimate their effects in the abstract scheduling problem. We are interested in automating this abstraction process for scheduling problems. In our approach, given a set of activities A to be scheduled1, we choose a subset of activities, critical(A), and create a simplified scheduling model which approximates the other activities non-critical(A) instead of scheduling them. We then search this abstract model for a good, if not optimal solution. A solution is a partial order schedule for activities in critical(A). this abstract solution is then extended to a solution the entire problem by inserting the remaining activities non-critical(A) into the schedule. While the approach reduces complexity by solving the problem in two stages it does so at a price. there is a risk that the good abstract solution will not produce a good solution to the entire problem. We know
Software process improvement holds a significant promise to reduce cycle times and provide greater value to all development activities involved in the software process development. While these methods appear to be wel...
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Software process improvement holds a significant promise to reduce cycle times and provide greater value to all development activities involved in the software process development. While these methods appear to be well suited for embedded systems development, their use has not become an organized practice. In the same way as that of software development, the embedded systems development could be failing due a bad management in the development process. CMMI-DEV v1.2 is a process improvement maturity model that has been developed by the Software Engineering Institute at Carnegie Mellon. CMMI-DEV v1.2 defines “what” processes and activities need to be done and not “how” these processes and activities are done. In this paper we introduce the SPIES methodology that integrates the CMMI-DEV v1.2 Level 2 process areas to specify a process for developing embedded systems. this methodology incorporates the TSP principles to support the lack of management and improve the process specification. To illustrate this approach, we describe an experimental system in which it has been applied to develop and manage a traffic light system.
Feature models are frequently used for specifying variability of user-configurable software systems, e.g., software product lines. Numerous approaches have been developed for automating feature model validation concer...
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ISBN:
(纸本)9783662496657;9783662496640
Feature models are frequently used for specifying variability of user-configurable software systems, e.g., software product lines. Numerous approaches have been developed for automating feature model validation concerning constraint consistency and absence of anomalies. As a crucial extension to feature models, cardinality annotations and respective constraints allow for multiple, and even potentially unbounded occurrences of feature instances within configurations. this is of particular relevance for user-adjustable application resources as prevalent, e.g., in cloud computing. However, a precise semantic characterization and tool support for automated and scalable validation of cardinality-based feature models is still an open issue. In this paper, we present a comprehensive formalization of cardinality-based feature models with potentially unbounded feature multiplicities. We apply a combination of ILP and SMT solvers to automate consistency checking and anomaly detection, including novel anomalies, e.g., interval gaps. We present evaluation results gained from our tool implementation showing applicability and scalability to larger-scale models.
An unsatisfiable set of constraints is minimal if all its (strict) subsets aresatisfiable.A number of forms of error diagnosis, including circuit error diagnosis and type error diagnosis, require finding all minimal u...
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ISBN:
(纸本)9781581137057
An unsatisfiable set of constraints is minimal if all its (strict) subsets aresatisfiable.A number of forms of error diagnosis, including circuit error diagnosis and type error diagnosis, require finding all minimal unsatisfiable subsets of a given set of constraints (representing an error), in order to generate the best explanation of the error. In this paper we give algorithms for efficiently determining all minimal unsatisfiable subsets for any kind of constraints. We show how taking into account notions of independence of constraints and using incremental constraint solvers can significantly improve the calculation of these subsets.
this volume of Science of Computer programming publishes extended versions of selected papers from the 14thinternationalconference on Formal Aspects of Component Software (FACS 2017), held in Braga, Portugal, Octobe...
this volume of Science of Computer programming publishes extended versions of selected papers from the 14thinternationalconference on Formal Aspects of Component Software (FACS 2017), held in Braga, Portugal, October 10-13, 2017. the objective of FACS is to bring together practitioners and researchers in the areas of component software and formal methods in order to promote a deeper understanding of how formal methods can or should be used to make componentbased software development succeed. the component-based software development approach has emerged as a promising paradigm to transport sound production and engineering principles into software engineering and to cope withthe ever increasing complexity of present-day software solutions. However, many conceptual and technological issues remain in component-based software development theory and practicethat pose challenging research questions. Moreover, the advent of cloud computing, cyber-physical systems, and of the Internet of things has brought to the fore new dimensions. these include quality of service, reconfiguration and robustness to withstand inevitable faults, which require established concepts to be revisited and new ones to be developed in order to meet the opportunities offered by those architectures. this conference exists since 2003, reaching its 14th edition in 2017 in Braga, Portugal. their proceedings have been published in Elsevier's Electronic Notes in theoretical Computer Science and Springer's Lecture Notes in Computer Science (LNCS). the annals of 2017 edition were published in volume 10487 of LNCS. the conference program of FACS 2017 included two invited talks, by Catuscia Palamidessi and Farhad Arbab, and 14 presentations of research papers. Authors of eight selected papers from FACS 2017 were invited to submit to this special issue. After an extensive and rigorous reviewing process, with each paper reviewed by at least three reviewers, seven of them were selected to appear in this speci
In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids...
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this document represents the proceedings of the 2024 XCSP3 Competition. the results of this competition of constraint solvers were presented at CP'24 (30thinternationalconference on principles and practice of Co...
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