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 Innovación 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