EuroSTAR Software Testing Conference 2012 presentation on How To Regression Test A Billion Rows Of Financial Data Every Sprint by Matt Archer.
See more at: http://conference.eurostarsoftwaretesting.com/past-presentations/
Regression testing is testing performed after changes to a system to detect whether new errors were introduced or old bugs have reappeared. It should be done after changes to requirements, new features added, defect fixes, or performance improvements. There are various strategies for regression testing including re-running all tests, test selection, test prioritization, and focusing on areas like frequently failing tests or recently changed code. While regression testing helps ensure system quality, managing large test suites over time poses challenges in minimizing tests while achieving coverage. Automating regression testing can help address these challenges.
This document provides an overview of performance tuning best practices for Scala applications. It discusses motivations for performance tuning such as resolving issues or reducing infrastructure costs. Some common bottlenecks are identified as databases, asynchronous/thread operations, and I/O. Best practices covered include measuring metrics, identifying bottlenecks, and avoiding premature optimization. Microbenchmarks and optimization examples using Scala collections are also presented.
This document discusses software testing techniques and best practices. It covers test design techniques like equivalence partitioning and boundary value analysis. It emphasizes the importance of tests being fast, isolated, repeatable, self-validating, and thorough. The testing pyramid hierarchy of tests is explained. Test-driven development and various test utilities are also outlined. The conclusions emphasize that tests help increase confidence in code, prevent accidental breaks, and ensure documentation remains relevant.
Performed predictive Data analytics for “Black Friday Sales Dataset” wherein the company wants to predict the purchase amount against the products using Rapid Miner Tool.
The document benchmarks 20 machine learning models on two datasets to compare their accuracy and speed. On the smaller Car Evaluation dataset, bagged decision trees, random forests and boosted decision trees achieved over 99% accuracy, while neural networks, decision stumps and support vector machines exceeded 95% accuracy. On the larger Nursery dataset, similar models exceeded 99% accuracy, while other models like decision rules and k-nearest neighbors exceeded 95% accuracy. However, models varied significantly in speed depending on the hardware, with decision trees, mixture discriminant analysis and gradient boosting as the fastest on Car Evaluation, and mixture discriminant analysis, one rule and boosted decision trees as the fastest on Nursery. The findings imply the importance of regular benchmarking
29 Advanced Google Tag Manager Tips Every Marketer Should KnowMike Arnesen
Google Tag Manager is an incredibly powerful tool and one you're likely not using to its full potential. In my talk from MozCon 2016, I delivered 29 rapid-fire tips intended to empower marketers to overcome the insurmountable odds and circumnavigate road blocks using this incredibly powerful marketing tool.
Regression testing is testing performed after changes to a system to detect whether new errors were introduced or old bugs have reappeared. It should be done after changes to requirements, new features added, defect fixes, or performance improvements. There are various strategies for regression testing including re-running all tests, test selection, test prioritization, and focusing on areas like frequently failing tests or recently changed code. While regression testing helps ensure system quality, managing large test suites over time poses challenges in minimizing tests while achieving coverage. Automating regression testing can help address these challenges.
This document provides an overview of performance tuning best practices for Scala applications. It discusses motivations for performance tuning such as resolving issues or reducing infrastructure costs. Some common bottlenecks are identified as databases, asynchronous/thread operations, and I/O. Best practices covered include measuring metrics, identifying bottlenecks, and avoiding premature optimization. Microbenchmarks and optimization examples using Scala collections are also presented.
This document discusses software testing techniques and best practices. It covers test design techniques like equivalence partitioning and boundary value analysis. It emphasizes the importance of tests being fast, isolated, repeatable, self-validating, and thorough. The testing pyramid hierarchy of tests is explained. Test-driven development and various test utilities are also outlined. The conclusions emphasize that tests help increase confidence in code, prevent accidental breaks, and ensure documentation remains relevant.
Performed predictive Data analytics for “Black Friday Sales Dataset” wherein the company wants to predict the purchase amount against the products using Rapid Miner Tool.
The document benchmarks 20 machine learning models on two datasets to compare their accuracy and speed. On the smaller Car Evaluation dataset, bagged decision trees, random forests and boosted decision trees achieved over 99% accuracy, while neural networks, decision stumps and support vector machines exceeded 95% accuracy. On the larger Nursery dataset, similar models exceeded 99% accuracy, while other models like decision rules and k-nearest neighbors exceeded 95% accuracy. However, models varied significantly in speed depending on the hardware, with decision trees, mixture discriminant analysis and gradient boosting as the fastest on Car Evaluation, and mixture discriminant analysis, one rule and boosted decision trees as the fastest on Nursery. The findings imply the importance of regular benchmarking
29 Advanced Google Tag Manager Tips Every Marketer Should KnowMike Arnesen
Google Tag Manager is an incredibly powerful tool and one you're likely not using to its full potential. In my talk from MozCon 2016, I delivered 29 rapid-fire tips intended to empower marketers to overcome the insurmountable odds and circumnavigate road blocks using this incredibly powerful marketing tool.
The promise of DevOps is that we can push new ideas out to market faster while avoiding delivering serious defects into production. Andreas Grabner explains that testers are no longer measured by the number of defect reports they enter, nor are developers measured by the lines of code they write. As a team, you are measured by how fast you can deploy high quality functionality to the end user. Achieving this goal requires testers to increase their skills. It’s all about finding solutions—not just problems. Testers must transition from reporting “app crashes” to providing details such as “memory leak caused by bad cache implementation.” Instead of reporting “it’s slow,” testers must discover “wrong hibernate configuration causes too much traffic from the database.” Using three real-life examples, Andreas illustrates what it takes for testing teams to become part of the DevOps transformation—bringing more value to the entire organization.
MeasureWorks - Why people hate to wait for your website to load (and how to f...MeasureWorks
My slides from DrupalJam 2014... About why users abandon your website and best practices to align content and speed to create a fast user experience, and continue to keep it aligned for every release
CQRS and Event Sourcing: A DevOps perspectiveMaria Gomez
This document discusses challenges of deploying, monitoring, and debugging systems using CQRS and event sourcing from a DevOps perspective. It describes using a blue/green deployment approach, implementing consistent and usable logging, monitoring key metrics and data streams, and employing distributed tracing to identify the origin of requests in order to quickly debug problems. The overall goal is to build scalable, resilient, and automated systems while facilitating operational tasks through iterative improvements to tools and processes.
This document provides instructions for creating a mapping in Informatica Power Center to perform data quality checks on financial account data from a source table to load into a target table. It describes importing the source and target tables, creating a filter transformation to select records where the account number length is 8 characters and the difference between open and close dates is not less than 30 days, and generating the mapping. The objective is to map data that meets specific rules for the target system.
Drupal content management system (cms) based e commerce portalSandeep Kumbhar
A content management system is computer software used to manage the creation and modification of digital content. CMSs are typically used for enterprise content management and web content management. Here we will see the Drupal content management system (cms) based e commerce portal.
This document provides an overview of GraphQL basics and how to use it with LeanIX. It describes GraphQL as a query language for APIs that allows clients to request specific data fields and relationships from an application's data model in a single request. The document demonstrates how to access the integrated GraphQL IDE from a LeanIX workspace and compile queries step-by-step using autocomplete and documentation. It shows how GraphQL enables more efficient and flexible data retrieval compared to REST APIs.
The document discusses the challenges of processing and storing billions of data inserts per day from vehicle telematics projects. Some key points:
- The project involves receiving continuous data streams from over 500 vehicles with 2500 data points captured per vehicle per second, resulting in over 1.5 billion MySQL inserts daily.
- A message queue is used to receive the streaming data and buffer inserts to help scale processing. Additional optimizations include bulk loading data via LOAD DATA INFILE for speed.
- Sharding and splitting the data across multiple databases by vehicle and time period (weekly tables) helps improve query performance for both live and historical data access.
- Techniques like asynchronous requests, caching, and a single entry point
When Data Visualizations and Data Imports Just Don’t WorkJim Kaplan CIA CFE
When Data Visualizations and Data Imports Just Don’t Work – Importing data is a dirty job as can painting user final pictures with that data. This webinar will explore the dirty little secrets that ensure data is imported completely and accurately, as well as, painting scenarios when a visualization may not be the best approach to meeting an audit objective. Specific learning objectives include:
o Walk through case studies of “dirty” data and how to improve then using improved data requests and cleansing tools.
o Watch case study examples of top tests to validate data tables to ensure data quality.
o Discover a host of baseline tests and other baseline statistics to validate, understand and possibly extract key trends for review.
o Understand visualization and dashboard types along with their associated analytical strengths from an audit perspective.
o Identify situations where statistics may be more effective audit extractors than relying on the human eye to spot notable events.
The document outlines a data science workflow including establishing a data science project lifecycle, assembling an effective multidisciplinary team to take on different roles, and setting up a standardized project structure and folder system to organize code, data, documentation, and deliverables. It advocates using an agile, iterative process to improve collaboration among team members throughout the data science pipeline from data acquisition and exploration to modeling, deployment, and customer acceptance.
Talk on how to easily integrate elasticsearch with react. Similar process with remapping of the data schema can yield a knowledge discovery and search application for any industry consuming huge amount of structured or unstructured data
Cloudera Data Science Challenge 3 Solution by Doug NeedhamDoug Needham
The document outlines the requirements and problems for Cloudera's Data Science certification challenge. It requires completing a test, and solving 3 problems involving flight delay prediction using machine learning, web analytics using statistical analysis, and recommending social media connections using graph analysis. Solutions are scored based on accuracy and a written abstract explaining the methodology.
MeasureWorks - Why your customers don't like to wait!MeasureWorks
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This document is a machine learning class assignment submitted by Trushita Redij to their supervisor Abhishek Kaushik at Dublin Business School. The assignment discusses data preprocessing techniques, decision trees, the Chinese Restaurant algorithm, and building supervised learning models. Specifically, linear regression and KNN classification models are implemented on population data from Ireland to predict total population and classify countries.
OSA Con 2022 - Scaling your Pandas Analytics with Modin - Doris Lee - Ponder.pdfAltinity Ltd
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Pandas is one of the most commonly used data science libraries in Python, with a convenient set of APIs for data cleaning, visualization, analysis, and exploration. However, despite its widespread adoption, Pandas suffers from severe scalability issues on large datasets. We developed the open-source project Modin, which is a fast, scalable drop-in replacement for pandas. Modin has been downloaded more than 4 million times and is used by leading data science teams, including Fortune 100 companies.
The document provides details about a business intelligence project for a fast fashion company. It includes:
1) An overview of the company's existing operational systems like their online website, mobile app, and desktop app and the limits of these systems.
2) An analysis of the company's requirements and key metrics like KPIs and KRIs that the BI project will measure.
3) The design of the BI project using a star schema model with dimensions like products, customers, and dates.
4) Details of the implementation including the hardware/software environment, ETL process using Talend, staging the data, transforming it, and loading it into views and tables with foreign keys in SQL Server.
[Tutorial] building machine learning models for predictive maintenance applic...PAPIs.io
The document discusses using machine learning for predictive maintenance in IoT applications compared to traditional approaches. It describes using publicly available aircraft engine data to build models in Azure ML to predict remaining useful life. Models tested include regression, binary classification, and multi-class classification. An end-to-end pipeline is demonstrated, from data preparation through deploying web services with different machine learning models.
Seagate relies heavily on big data analytics to ensure high quality in data storage. As data storage needs grow exponentially, predictive analytics are crucial to avoid costly failures. Seagate collects terabytes of manufacturing, testing, component, and field data daily. This data is analyzed using machine learning algorithms to predict and prevent drive failures, helping ensure the reliability of over 1 billion drives expected in cloud datacenters by 2020. Seagate's big data analytics infrastructure combines comprehensive data collection, large-scale analytics capabilities, and data-driven decision making to advance quality control in high-volume data storage manufacturing.
Automated Historical Performance Analysis with kmemtracerKyungmin Lee
This document discusses using kmemtracer to automate historical performance analysis on Android. It describes how kmemtracer uses instrumentation to track activity lifecycles and collect memory usage snapshots without modifying the application code. Snapshots containing metrics like native memory usage are saved in bundles and written to files by a ResultsWriter for later analysis. This allows measuring and improving an app's performance over time.
Why We Need Diversity in Testing- AccentureTEST Huddle
In this webinar Rasa (Testing capability lead for Denmark) and Matthias (EALA Testing capability lead) will share some of their own experiences why diversity matters, give insights into how Accenture as a global firm is promoting diversity and how we are in the process of changing our attitudes and processes to make all of this sustainable
Keys to continuous testing for faster delivery euro star webinar TEST Huddle
Your business needs to deliver faster. To accommodate, Development needs to introduce fewer changes but in a much more frequent cadence. This creates a challenge for test teams to keep up with the rapid pace of change without compromising on quality. Automation is paramount to the success or failure of Continuous Delivery, and Continuous Testing enables early and frequent quality feedback throughout the CI/CD pipeline.
In this webinar, Eran & Ayal will explore how to implement Continuous Testing to ensure high quality releases in a Continuous Delivery environment; including what to test and when to automate new functionality in order to optimize your efforts.
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This document provides instructions for creating a mapping in Informatica Power Center to perform data quality checks on financial account data from a source table to load into a target table. It describes importing the source and target tables, creating a filter transformation to select records where the account number length is 8 characters and the difference between open and close dates is not less than 30 days, and generating the mapping. The objective is to map data that meets specific rules for the target system.
Drupal content management system (cms) based e commerce portalSandeep Kumbhar
A content management system is computer software used to manage the creation and modification of digital content. CMSs are typically used for enterprise content management and web content management. Here we will see the Drupal content management system (cms) based e commerce portal.
This document provides an overview of GraphQL basics and how to use it with LeanIX. It describes GraphQL as a query language for APIs that allows clients to request specific data fields and relationships from an application's data model in a single request. The document demonstrates how to access the integrated GraphQL IDE from a LeanIX workspace and compile queries step-by-step using autocomplete and documentation. It shows how GraphQL enables more efficient and flexible data retrieval compared to REST APIs.
The document discusses the challenges of processing and storing billions of data inserts per day from vehicle telematics projects. Some key points:
- The project involves receiving continuous data streams from over 500 vehicles with 2500 data points captured per vehicle per second, resulting in over 1.5 billion MySQL inserts daily.
- A message queue is used to receive the streaming data and buffer inserts to help scale processing. Additional optimizations include bulk loading data via LOAD DATA INFILE for speed.
- Sharding and splitting the data across multiple databases by vehicle and time period (weekly tables) helps improve query performance for both live and historical data access.
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When Data Visualizations and Data Imports Just Don’t WorkJim Kaplan CIA CFE
When Data Visualizations and Data Imports Just Don’t Work – Importing data is a dirty job as can painting user final pictures with that data. This webinar will explore the dirty little secrets that ensure data is imported completely and accurately, as well as, painting scenarios when a visualization may not be the best approach to meeting an audit objective. Specific learning objectives include:
o Walk through case studies of “dirty” data and how to improve then using improved data requests and cleansing tools.
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o Identify situations where statistics may be more effective audit extractors than relying on the human eye to spot notable events.
The document outlines a data science workflow including establishing a data science project lifecycle, assembling an effective multidisciplinary team to take on different roles, and setting up a standardized project structure and folder system to organize code, data, documentation, and deliverables. It advocates using an agile, iterative process to improve collaboration among team members throughout the data science pipeline from data acquisition and exploration to modeling, deployment, and customer acceptance.
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The document outlines the requirements and problems for Cloudera's Data Science certification challenge. It requires completing a test, and solving 3 problems involving flight delay prediction using machine learning, web analytics using statistical analysis, and recommending social media connections using graph analysis. Solutions are scored based on accuracy and a written abstract explaining the methodology.
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This document is a machine learning class assignment submitted by Trushita Redij to their supervisor Abhishek Kaushik at Dublin Business School. The assignment discusses data preprocessing techniques, decision trees, the Chinese Restaurant algorithm, and building supervised learning models. Specifically, linear regression and KNN classification models are implemented on population data from Ireland to predict total population and classify countries.
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Pandas is one of the most commonly used data science libraries in Python, with a convenient set of APIs for data cleaning, visualization, analysis, and exploration. However, despite its widespread adoption, Pandas suffers from severe scalability issues on large datasets. We developed the open-source project Modin, which is a fast, scalable drop-in replacement for pandas. Modin has been downloaded more than 4 million times and is used by leading data science teams, including Fortune 100 companies.
The document provides details about a business intelligence project for a fast fashion company. It includes:
1) An overview of the company's existing operational systems like their online website, mobile app, and desktop app and the limits of these systems.
2) An analysis of the company's requirements and key metrics like KPIs and KRIs that the BI project will measure.
3) The design of the BI project using a star schema model with dimensions like products, customers, and dates.
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View On-Demand Webinar: https://youtu.be/EzyUdJFuzlE
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View webinar recording - https://huddle.eurostarsoftwaretesting.com/resource/agile-testing/scaling-agile-less-large-scale-scrum/
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View webinar recording here - http://huddle.eurostarsoftwaretesting.com/resource/agile-testing/creating-agile-test-strategies-larger-enterprises/
3 key takeaways
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View Webinar recording: https://huddle.eurostarsoftwaretesting.com/resource/test-management/is-there-a-risk/
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http://huddle.eurostarsoftwaretesting.com/resource/webinar/need-testers-agile-teams/
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https://huddle.eurostarsoftwaretesting.com/resource/webinar/use-selenium-successfully/
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https://huddle.eurostarsoftwaretesting.com/resource/people-skills/thinking-through-your-role/
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Matt Archer - How To Regression Test A Billion Rows Of Financial Data Every Sprint - EuroSTAR 2012
1. How to regression test a billion rows of
financial data every sprint
Matt Archer, Independent Software Tester, UK
www.eurostarconferences.com
@esconfs
#esconfs
2. Big data (a pension scheme example)
2
x x =
100,000
people
12
months
100
years
1.2 million
estimates
1.2 million
estimates
10s of financial
models
x
A Very Large
Database! =
Billions of
database rows
3. Creating a website
3
Page 1
Page 2
Page 3
Pages to develop + test
Sprint 1
4. Creating a website
4
Page 1
Page 2
Page 3
Page 6 Page 4 Page 5
Pages to regression test
Pages to develop + test
Sprint 1 Sprint 2
5. Creating a website
5
Page 1
Page 2
Page 3
Page 6 Page 4 Page 5
Pages to develop + test
Pages to regression test
Page 7 Page 8 Page 9
Sprint 1 Sprint 2 Sprint 3
6. Creating a website
Page 10 Page 11 Page 12
6
Page 1
Page 2
Page 3
Page 6 Page 4 Page 5
Pages to regression test
Pages to develop + test
Page 7 Page 8 Page 9
Sprint 1 Sprint 2 Sprint 3 Sprint 4
7. Creating a website
Page 10 Page 11 Page 12
7
Page 1
Page 2
Page 3
Page 6 Page 4 Page 5
Pages to enhance + test
+ regression test
Page 7 Page 8 Page 9
Pages to regression test
Pages to regression test
Sprint 1 Sprint 2 Sprint 3 Sprint 4 Sprint 5
8. A common approach
8
Regression Test Strategy 1
(“The Regression Pack”)
1. Re-run tests from previous sprints.
2. Select those tests using a risk-based
heuristic (rule of thumb).
3. Automate as much as possible,
increasing the coverage over time.
12. How many permutations for an entire site!?
12
Page 1
Page 2
Page 3
Page 6 Page 4 Page 5
Pages to enhance + test
+ regression test
Page 7 Page 8 Page 9
Page 10 Page 11 Page 12
Pages to regression test
Pages to regression test
13. The thoroughness vs. maintenance conundrum
13
Follow
Your
Instincts…
…create those
tests if you
Think they’ll
find bugs!
BUT more tests =
more maintenance
15. An alternative approach
15
Regression Test Strategy 2
(“Capture and Compare”)
1. Take snapshots of the data as it is
displayed in a known high-quality build
2. Store the snapshots somewhere safe
3. Use the snapshot to help identify
unforeseen changes (bugs) in future
release candidates and live releases
16. Step 1: Identify a gold build
16
Page 7 Page 8 Page 9
Page 6 Page 4 Page 5
Page
11
Page
10
Page
12
Page 1
Page 2
Page 3
The “Gold” Build (1.3.2.14)
I’m so great,
I’ve been the subject of…
BDD
Unit Tests
Integration Tests
Exploratory Testing
UX Inspections
SME Reviews
Customer Demos
And maybe even…
production use
(with NO complaints!)
17. Step 2: Specify the data permutations to capture
17
<WebPage path = “/analytics”>
<QueryStringParam name = “InformationType”>
<QueryStringValue value = “PresentValue”/>
<QueryStringValue value = “Cashflow”/>
</QueryStringParam>
…
…
</WebPage>
18. Step 3: Capture and clean the HTML tables
18
Capture specifications
Capture
String Host
String Spec_Location
String Save_Location
Void Capture()
Void Clean()
Data snapshots
<table>
<tr>
<td>4.24</td>
<td>15.93</td>
<td>12.67</td>
…
</tr>
…
</table>
Selenium
“Gold” build
19. Step 4: Synchronise the data
19
Database
Database Synchronise
Release Candidate “Gold” Build
20. Step 5: Compare the release against the snapshot
20
Compare
String Host
String Spec_Location
String Snap_Location
Void Compare()
Void Highlight()
Data snapshots
Capture specifications
Highlighted release candidate
Release candidate
21. 21
Step 6: Manual validation
What is a bug? What is a deliberate change?
22. What to capture, what to test?
What to capture? What to test?
The final build from
the previous sprint
The final build from
the current sprint
The build currently
deployed in live
The build currently
deployed in staging
The build deployed in
live prior to release
The build deployed in
live after the release
22
• The technique can support a variety of team milestones
23. Summary
• Beware generic regression
testing (or any testing) strategies
• Look for more efficient ways of
detecting regression bugs
• Balance your regression techniques
23
24. 24
Questions?
Matt Archer
August 2012
Twitter: @MattArcherUK
Blog: mattarcherblog.wordpress.com
Email: matthewjarcher@googlemail.com