Test trend analysis: Towards robust reliable and timely tests

If you are looking for solutions that allow you not only to manage all of your data (structured, semi-structured and unstructured) but to also make the most out of them, using a common language is critical. Adding Semantic Technology to data integration is the glue that holds together all your enterprise data and their relationships in a meaningful way. Learn how you can quickly design data processing jobs and integrate massive amounts of data and see what semantic integration can do for your data and your business. www.ontotext.com

The Bounties of Semantic Data Integration for the Enterprise

Applying large scale text analytics with graph databases

Marissa Kobylenski

Enterprise knowledge graphs

These are slides from a live webinar taken place January 2018. GraphDB™ Fundamentals builds the basis for working with graph databases that utilize the W3C standards, and particularly GraphDB™. In this webinar, we demonstrated how to install and set-up GraphDB™ 8.4 and how you can generate your first RDF dataset. We also showed how to quickly integrate complex and highly interconnected data using RDF and SPARQL and much more. With the help of GraphDB™, you can start smartly managing your data assets, visually represent your data model and get insights from them.

Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018

Despite an improved digital access to scientific publications in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. The document-oriented workflows in science have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis. We need to represent, analyse, augment and exploit scholarly communication in a knowledge-based way by expressing and linking scientific contributions and related artefacts through semantically rich, interlinked knowledge graphs. This should be based on deep semantic representation of scientific contributions, their manual, crowd-sourced and automatic augmentation and finally the intuitive exploration and interaction employing question answering on the resulting scientific knowledge base. We need to synergistically combine automated extraction and augmentation techniques, with large-scale collaboration to reach an unprecedented level of knowledge graph breadth and depth. As a result, knowledge-based information flows can facilitate completely new ways of search and exploration. The efficiency and effectiveness of scholarly communication will significant increase, since ambiguities are reduced, reproducibility is facilitated, redundancy is avoided, provenance and contributions can be better traced and the interconnections of research contributions are made more explicit and transparent. In this talk we will present first steps in this direction in the context of our Open Research Knowledge Graph initiative and the ScienceGRAPH project.

Tools and techniques for data science

Ajay Ohri

Pandas, Data Wrangling & Data Science

Krishna Sankar

Integration of data ninja services with oracle spatial and graph

Data Ninja API

Do it on your own - From 3 to 5 Star Linked Open Data with RMLio

Open Knowledge Belgium

Towards Knowledge Graph based Representation, Augmentation and Exploration of...

Linking Open, Big Data Using Semantic Web Technologies - An Introduction

Ronald Ashri

[Conference] Cognitive Graph Analytics on Company Data and News

The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Now it is possible to rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the creation and evolution of information models for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. Also research results become directly comparable and easier to reuse.

Knowledge graphs on the Web

Armin Haller

Powerful Information Discovery with Big Knowledge Graphs –The Offshore Leaks ...

Connected Data World

A New Year in Data Science: ML Unpaused

Paco Nathan

iRODS UGM 2018 Fair data management and DISQOVERability

Maarten Coonen

Towards an Open Research Knowledge Graph

In this presentation its given an introduction about Data Science, Data Scientist role and features, and how Python ecosystem provides great tools for Data Science process (Obtain, Scrub, Explore, Model, Interpret). For that, an attached IPython Notebook ( http://bit.ly/python4datascience_nb ) exemplifies the full process of a corporate network analysis, using Pandas, Matplotlib, Scikit-learn, Numpy and Scipy.

Python for Data Science

Gabriel Moreira

First Steps in Semantic Data Modelling and Search & Analytics in the Cloud

Property graph vs. RDF Triplestore comparison in 2020

In agile software development world, we are dealing with many test tasks such as user story testing, exploratory testing, check-list based testing, regression testing, performance testing, security testing in each sprint. Besides these testing activities, one of the test types which is considerably getting crucial is visual regression testing. Visual regression testing focuses on to check visual contents and animations, page layout, and responsive design of a website/app. Because of the limits of human vision, human-based visual regression testing is generally error-prone and cumbersome. Hence, automation is inevitable. It enables us to run the tests much more precisely in a short time period. Also, it saves us a significant amount of time to deal with other manual test activities in each sprint. In this talk, we will walk through well-known open-source and commercial solutions for visual test automation. We will learn which technologies they use, what type of visual tests they are suitable for, and their major differences between each other. Besides this overview, we will also make a real-life visual test automation demo by using Selenium, ImageMagick, and AShot.

La actualidad más candente (20)

Knowledge Graph Introduction

The Bounties of Semantic Data Integration for the Enterprise

Applying large scale text analytics with graph databases

Enterprise knowledge graphs

Transforming Your Data with GraphDB: GraphDB Fundamentals, Jan 2018

Tools and techniques for data science

Pandas, Data Wrangling & Data Science

Integration of data ninja services with oracle spatial and graph

Do it on your own - From 3 to 5 Star Linked Open Data with RMLio

Towards Knowledge Graph based Representation, Augmentation and Exploration of...

Linking Open, Big Data Using Semantic Web Technologies - An Introduction

[Conference] Cognitive Graph Analytics on Company Data and News

Knowledge graphs on the Web

Powerful Information Discovery with Big Knowledge Graphs –The Offshore Leaks ...

A New Year in Data Science: ML Unpaused

iRODS UGM 2018 Fair data management and DISQOVERability

Towards an Open Research Knowledge Graph

Python for Data Science

First Steps in Semantic Data Modelling and Search & Analytics in the Cloud

Property graph vs. RDF Triplestore comparison in 2020

Destacado

Colorful world-of-visual-automation-testing-latest

Onur Baskirt

Slides from my talk at Selenium Camp Test Automation Conference - 2017 https://seleniumcamp.com/talk/end-to-end-test-automation-for-both-horizontal-and-vertical-scale/ Test automation (TA) activity has become a key critical work to guarantee the quality of system under test (SUT) by driving test and also development effort effectively. To bring this efficiency to projects, companies are investing on TA projects in a more motivated way. The question here is how we should design the automation strategy to handle complex TA projects together effectively. It can be done by automating test scenarios as E2E (end to end). Vertical E2E TA consists of; automating Test Data Preparation Phase and Unit, Integration and UI tests. For horizontal E2E TA; UI and Integration test cases, which are automated, designed as integrated real user scenarios. I will tell about the prerequisites, principles and key factors to have E2E automated tests. And also I will share hands on experienced E2E test automation projects that Selenium was the key tool.

End-to-End Test Automation for Both Horizontal and Vertical Scale

Erdem YILDIRIM

Protractor framework – how to make stable e2e tests for Angular applications

Ludmila Nesvitiy

Slides for the SeleniumCamp 2017 talk: http://seleniumcamp.com/talk/better-bullshit-driven-development/ Presented by Julia Iluhina, Software Developer in Test at Automician. Talk description: BDD stands as Behaviour Driven Development and was invented to increase efficiency of ATDD by involving Business Analysts/Product Owners to write Acceptance Criteria in “ready to be automated by developers” format – Gherkin. Then customers and managers invented Bullshit Driven Development when had made automation engineers write both Gherkin and implementation of its steps just for one reason: make tests and their reports readable. This is a classic “hammering nails with a microscope” example, when engineers have to add additional limiting layers to test framework for no any valuable reason. Because… There is much simpler and more powerful way to make simple xUnit tests and reports readable. And in this talk we will learn how. How to implement common xUnit tests in BDD style and make everyone happy – test automation engineer, customer and manager. 

Better Bullshit Driven Development [SeleniumCamp 2017]

automician

Testing Metrics - Making your tests visible

Alper Mermer

Grading the Quality of Selenium Tests

Marcus Merrell

Design patters exist for years in software development. Some developers love them, some think they are useless. But design patters has very clear goals: describe common solutions for common problems, create shared language for community, improve understanding and reuse of existing approaches. Test automation has its own set of problems, so there is a set of helpful design patterns for this area. In this talk I will run through all known patterns and describe them in details with several practical samples.

Design patterns in test automation

Mikalai Alimenkou

Excuse me, sir, do you have a moment to talk about tests in Kotlin

leonsabr

SoapUI one key to all doors

Yegor Maksymchuk

How does Java 8 exert hidden power on Test Automation?

Sergey Korol

WixAutomation - Test State Pattern - Selenium Camp 2017

Roi Ashkenazi

Test Automation Architecture in Microservices

Alper Mermer

This talk is a brief overview of how we deal with UI test architecture on our project. Complex backend, SPA as a frontend, 20+ different brands and 100+ features for each, more and more code. We will discuss: – fakes and test doubles, approaches how to build and maintain them; – development patterns, which help you to make your architecture more simple, stable and readable; – tips and tricks – how to make your life with UI tests easier.

An easy way to automate complex UI

Ivan Pashko

Fabulous Tests on Spock and Groovy

Yaroslav Pernerovsky

Page Objects Done Right - selenium conference 2014

Oren Rubin

Unit Testing TypeScript

Daniel Jimenez Garcia

Combining Heritrix and PhantomJS for Better Crawling of Pages with Javascript

Michael Nelson

Today’s applications are becoming more complex. From multi-layers applications, to micro-services to containers, QA & automation engineers are required to test more with less and without compromising the quality of the app. Join me and Yossi Neeman as we explain the pros & cons of testing at each of the different layers of the application and also share some best practices around Agile Testing. Everything will be demonstrated on a demo application built with the latest technology stack including NodeJS, REST APIs and MongoDB and tested using UFT 12.52.

Testing NodeJS, REST APIs and MongoDB with UFT

Ori Bendet

Angular js automation using protractor

Ravi Gupta

API Testing with Frisby and Mocha

Lyudmila Anisimova

Destacado (20)

Colorful world-of-visual-automation-testing-latest

End-to-End Test Automation for Both Horizontal and Vertical Scale

Protractor framework – how to make stable e2e tests for Angular applications

Better Bullshit Driven Development [SeleniumCamp 2017]

Testing Metrics - Making your tests visible

Grading the Quality of Selenium Tests

Design patterns in test automation

Excuse me, sir, do you have a moment to talk about tests in Kotlin

SoapUI one key to all doors

How does Java 8 exert hidden power on Test Automation?

WixAutomation - Test State Pattern - Selenium Camp 2017

Test Automation Architecture in Microservices

An easy way to automate complex UI

Fabulous Tests on Spock and Groovy

Page Objects Done Right - selenium conference 2014

Unit Testing TypeScript

Combining Heritrix and PhantomJS for Better Crawling of Pages with Javascript

Testing NodeJS, REST APIs and MongoDB with UFT

Angular js automation using protractor

API Testing with Frisby and Mocha

Similar a Test trend analysis: Towards robust reliable and timely tests

Searching for Meaning

The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.

The Relevance of the Apache Solr Semantic Knowledge Graph

Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances. This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.

How to Build a Semantic Search System

Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016) Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.

The Semantic Knowledge Graph