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A verified and optimized Stream X-Machine testing method, with application to cloud service certification
The Stream X-Machine (SXM) testing method provides strong and repeatable guarantees of functional correctness, up to a specification. These qualities make the method attractive for software certification, especially in the domain of brokered cloud services, where arbitrage seeks to substitute functionally equivalent services from alternative providers. However, practical obstacles include: the difficulty in providing a correct specification, the translation of abstract paths into feasible concrete tests, and the large size of generated test suites. We describe a novel SXM verification and testing method, which automatically checks specifications for completeness and determinism, prior to generating complete test suites with full grounding information. Three optimisation steps achieve up to a ten-fold reduction in the size of the test suite, removing infeasible and redundant tests. The method is backed by a set of tools to validate and verify the SXM spec-ification, generate technology-agnostic test suites and ground these in SOAP, REST or rich-client service implementations. The method was initially validated using seven specifications, three cloud platforms and five grounding strategies
Technical alignment
This essay discusses the importance of the areas of
infrastructure and testing to help digital preservation services
demonstrate reliability, transparency, and accountability. It
encourages practitioners to build a strong culture in which
transparency and collaborations between technical frameworks
are valued highly. It also argues for devising and applying
agreed-upon metrics that will enable the systematic analysis of
preservation infrastructure. The essay begins by defining
technical infrastructure and testing in the digital preservation
context, provides case studies that exemplify both progress and
challenges for technical alignment in both areas, and concludes
with suggestions for achieving greater degrees of technical
alignment going forward
Teacher Evaluator Training & Certification: Lessons Learned From the Measures of Effective Teaching Project
Makes recommendations for the design and implementation of programs to train and certify principals in conducting teacher evaluations, including content, format, and length of training, scoring practice, and criteria for certification tests
A Framework to Evaluate Software Developer’s Productivity The VALORTIA Project
Currently, there is a lack in companies developing software in relation to assessing their staff’s productivity
before executing software projects, with the aim of improving effectiveness and efficiency. QuEF (Quality
Evaluation Framework) is a framework that allows defining quality management tasks based on a model.
The main purpose of this framework is twofold: improve an entity’s continuous quality, and given a context,
decide between a set of entity’s instances on the most appropriate one. Thus, the aim of this paper is to
make this framework available to evaluate productivity of professionals along software development and
select the most appropriate experts to implement the suggested project. For this goal, Valortia platform,
capable of carrying out this task by following the QuEF framework guidelines, is designed. Valortia is a
platform to certify users' knowledge on a specific area and centralize all certification management in its
model by means of providing protocols and methods for a suitable management, improving efficiency and
effectiveness, reducing cost and ensuring continuous quality.Ministerio de Ciencia e InnovaciĂłn TIN2013-46928-C3-3-
Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges
The technology in the area of automated vehicles is gaining speed and
promises many advantages. However, with the recent introduction of
conditionally automated driving, we have also seen accidents. Test protocols
for both, conditionally automated (e.g., on highways) and automated vehicles do
not exist yet and leave researchers and practitioners with different
challenges. For instance, current test procedures do not suffice for fully
automated vehicles, which are supposed to be completely in charge for the
driving task and have no driver as a back up. This paper presents current
challenges of testing the functionality and safety of automated vehicles
derived from conducting focus groups and interviews with 26 participants from
five countries having a background related to testing automotive safety-related
topics.We provide an overview of the state-of-practice of testing active safety
features as well as challenges that needs to be addressed in the future to
ensure safety for automated vehicles. The major challenges identified through
the interviews and focus groups, enriched by literature on this topic are
related to 1) virtual testing and simulation, 2) safety, reliability, and
quality, 3) sensors and sensor models, 4) required scenario complexity and
amount of test cases, and 5) handover of responsibility between the driver and
the vehicle.Comment: 8 page
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