DevOps in Practice -- A preliminary Analysis of two Multinational Companies

DevOps is a cultural movement that aims the collaboration of all the stakeholders involved in the development, deployment and operation of soft-ware to deliver a quality product or service in the shortest possible time. DevOps is relatively recent, and companies have developed their DevOps prac-tices largely from scratch. Our research aims to conduct an analysis on practic-ing DevOps in +20 software-intensive companies to provide patterns of DevOps practices and identify their benefits and barriers. This paper presents the preliminary analysis of an exploratory case study based on the interviews to relevant stakeholders of two (multinational) companies. The results show the benefits (software delivery performance) and barriers that these companies are dealing with, as well as DevOps team topology they approached during their DevOps transformation. This study aims to help practitioners and researchers to better understand DevOps transformations and the contexts where the practices worked. This, hopefully, will contribute to strengthening the evidence regarding DevOps and supporting practitioners in making better informed decisions about the return of investment when adopting DevOps.Comment: 8 pages, 1 figure, 2 tables, conferenc

Pain detection with bioimpedance methodology from 3-dimensional exploration of nociception in a postoperative observational trial

Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0-10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r(2) = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions

Selection of Software Product Line Implementation Components Using Recommender Systems: An Application to Wordpress

In software products line (SPL), there may be features which can be implemented by different components, which means there are several implementations for the same feature. In this context, the selection of the best components set to implement a given configuration is a challenging task due to the high number of combinations and options which could be selected. In certain scenarios, it is possible to find information associated with the components which could help in this selection task, such as user ratings. In this paper, we introduce a component-based recommender system, called (REcommender System that suggests implementation Components from selecteD fEatures), which uses information associated with the implementation components to make recommendations in the domain of the SPL configuration. We also provide a RESDEC reference implementation that supports collaborative-based and content-based filtering algorithms to recommend (i.e., implementation components) regarding WordPress-based websites configuration. The empirical results, on a knowledge base with 680 plugins and 187 000 ratings by 116 000 users, show promising results. Concretely, this indicates that it is possible to guide the user throughout the implementation components selection with a margin of error smaller than 13% according to our evaluation.Ministerio de Economía y Competitividad RTI2018-101204-B-C22Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-RMinisterio de Economía, Industria y Competitividad MCIU-AEI TIN2017-90644-RED

Robust, automated sleep scoring by a compact neural network with distributional shift correction.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring

Transferable knowledge for Low-cost Decision Making in Cloud Environments

Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenario

Sediment structure and physicochemical changes following tidal inundation at a large open coast managed realignment site

Managed realignment (MR) schemes are being implemented to compensate for the loss of intertidal saltmarsh habitats by breaching flood defences and inundating the formerly defended coastal hinterland. However, studies have shown that MR sites have lower biodiversity than anticipated, which has been linked with anoxia and poor drainage resulting from compaction and the collapse of sediment pore space caused by the site's former terrestrial land use. Despite this proposed link between biodiversity and soil structure, the evolution of the sediment sub-surface following site inundation has rarely been examined, particularly over the early stages of the terrestrial to marine or estuarine transition. This paper presents a novel combination of broad- and intensive-scale analysis of the sub-surface evolution of the Medmerry Managed Realignment Site (West Sussex, UK) in the three years following site inundation. Repeated broad-scale sediment physiochemical datasets are analysed to assess the early changes in the sediment subsurface and the preservation of the former terrestrial surface, comparing four locations of different former land uses. Additionally, for two of these locations, high-intensity 3D-computed X-ray microtomography and Itrax micro-X-ray fluorescence spectrometry analyses are presented. Results provide new data on differences in sediment properties and structure related to the former land use, indicating that increased agricultural activity leads to increased compaction and reduced porosity. The presence of anoxic conditions, indicative of poor hydrological connectivity between the terrestrial and post-inundation intertidal sediment facies, was only detected at one site. This site has experienced the highest rate of accretion over the terrestrial surface (ca. 7 cm over 36 months), suggesting that poor drainage is caused by the interaction (or lack of) between sediment facies rather than the former land use. This has significant implications for the design of future MR sites in terms of preparing sites, their anticipated evolution, and the delivery of ecosystem services

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively