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pigen's Introduction

Krychko Mykyta (official)

Krichko Nikita (native)

email: [email protected]

I am an expert in performance testing and data engineering.

I create performance tests and build processes for finding performance issues. I help companies to understand why their programs work slowly. I teach people to look for and prevent such kind of issues.

I am looking for problems not only in programs but also in processes. I define the requirements, and I create test scripts based on those requirements for various test types and load profiles. I setup a pipeline of scenarios in a continuous integration system and setup a system for automatic analysis and anomaly search using machine learning. I keep human interaction with tests to a minimum and provide developers with the fastest possible feedback after their commits.

Companies contact me when they need to understand:

  • why the program is slow;
  • when it is necessary to build a process for detecting performance issues from scratch;
  • when it needs to teach the system automatically find issues during the development process.

I can work with many performances, monitoring, and profiling tools. I can find errors in desktop applications, web applications, and databases. I can independently analyze large volumes of logs and find hidden correlations between several data sources. I can use machine learning to find problems of balancing, memory leaks, and predict future resource consumption by servers.

I’m not a wizard, and I can’t find errors and bottlenecks just by looking at the application. Load testing is scrupulous research that required long preparation and gives accurate results. However, we can always find a compromise by sacrificing accuracy for the reason of speed (I prefer not to do so).

My mission is to create one button - which can say why the application is slow

MY PROJECTS

PERFORMANCE TESTING PROJECTS

  • Performance testing of the neural network's services
  • Devolop reporting API for generated yearly/monthly reports about system usage
  • Develop performance testing framework for elastic search engine
  • Anomaly detection in performance testing (static)
  • Anomaly detection for monitoring system (dynamic via Plumber)
  • Add machine learning in performance testing
  • Add automatic results analysis of performance testing
  • Generate recommendation system for performance testing engineer for a faster gathering of performance testing results
  • Create interactive shiny-dashboard for performance testing results
  • Create a robust regression model for faster gathering of performance testing results
  • Create CI performance framework in a company
  • Invent performance testing pipeline in a company
  • Structured performance methodology in a company
  • Build perfomance testing framework for legacy technologies (fat client performance testing)
  • Develop performance and profiling tools for legacy technologies

DATA SCIENCE PROJECTS

  • NLP: QA-fest review analysis
  • Regression: Predict performance behavior on high load based on low load.
  • Forecasting: Create expected requirements for performance testing based on historical vales
  • Other: detect anomalies in time series for bug and route cause detection on performance testing

METHODOLOGY PROJECTS

  • Develop methodology for economical efficiency performance benchmark of different CPUs for DBs
  • Develop methodology for AI performance testing
  • Develop performance testing process for release certification jobs
  • Develop methodology for performance testing and benchmarking services with neural network on backend
  • Develop methodology of performance testing management: "From chaos to application performance management"
  • Develop a methodology for faster gathering performance testing results using machine learning
  • Develop strategy (algorithm) about getting information about changes in application performance in one performance run
  • Develop an approach for predicting expected result without defined requirements (based on historical results and ARIMA forecasting)

EDUCATIONAL PROJECTS

  • Educate students in National aviation university for performance testing
  • Mentoring and teaching junior performance test engineers

Skills

programming languages level
R ⚫⚫⚫⚫⚫
Python ⚫⚫⚫⚫⚪
JAVA ⚫⚫⚫⚪⚪
groovy ⚫⚫⚫⚪⚪
bash, powerShell ⚫⚫⚫⚪⚪
  • data engineering: PySpark, Dask, Twisted, Tornado, asyncio, flask, rest, pytest, unittest,Tensorflow, pandas, plotly, dash, keras, numpy, scipy, mathplotlib, tesseract,
  • frameworks: Selenium webDriver, selenoid, zeep, requests, tidyverse, shiny, Scikit-learn, pandas, pytest, testNg, Junit,
  • testing tools: Load runner, jmeter, Gatling, tsung, ReadyAPI/SoapUI, postman
  • DB: mysql, postreSql, MSsql, Oracle SQL, mongoDB, sybase SQL
  • virtulization: vmware esxi
  • conteinerization: kubernetes, docker
  • CI/CD tools: teamcity, jenkins, hudson, azure devops, docker
  • monitoring: Dynatrace, appDynamics, newRelic, DataDog
  • cloud: AWS, AZURE

WORKING EXPERIENCE

year company / projects roles
2023 Crown Castle Senior Cloud platform engineer
2022 Casenet llc Senior performance engineer
2022 N-ix Senior R developer
2021 PropellMinds Senior performance engineer - consulting
2021 Percona Performance engineer - consulting
2020 Datarobot Senior AI performance engineer (prev. Senior performance engineer)
2019 HintMD Senior performance engineer
2019 DNVGL Performance architect - consulting
2018 RB Performance architect - consulting
2018 NDA-banki Senior Performance Engineer
2016 Islandsbanki Senior Performance Engineer
2016 Itera Data engineer / Data scientiest / Performance engineer (prev. Senior Performance Engineer)
2016 Luxoft Senior performance test engineer
2014 Terrasoft System analyst / Performance test Engineer / QA Engineer
2013 SRS QA engineer
2011 Apeal court System administratod / Deputy IT department
2009 SDICSED System administrator
2008 Geochemincal insitute System administrator

EDUCATION

years university specialization
2018 Moscow Institute of Physics and Technology Machine learning and data analysis
2017 John Hopkins University Data Science / Executive Data Science
2015 «STRATOPLAN» Management School Project management
2012 Institute of postgraduate education of Taras Shevchenko National University of Kyiv Psychology
2010 National Academy of security service of Ukraine System and technology of protection restricted access information

My Articles

VIDEO (from conferences)

pigen's People

Contributors

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Watchers

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pigen's Issues

add story illustrator

User's Story

1. Persona and Role

Our persona here is Alex, a passionate English teacher who loves to encourage his students to be industrious when reading stories. He uses modern technology to help him deliver his lessons effectively. He often uses visualisation to make stories memorable and vivid for his students.

  • Alex is comfortable with technology and picks up new tools quickly.
  • He has a creative approach to teaching and enjoys finding innovative ways to bring his lessons to life.
  • He is passionate about literature and enjoys sharing this love with his students.
  • He is extremely organized, patient, and cares about each student's progress.
  • He always looks for new ways to engage his students and make learning fun.

He would like a tool that could help automate the process of visualising his students' stories and stimulate their creativity. Being able to illustrate their own stories with Dalle3 through a console application would be an exciting, fun and learning experience for them.

2. Industry Best Practices

Summary: Alex wishes to have an application which allows him and his students to provide a story text file and receive outputs as visual representations of main scenes from the story, with given style preferences and storage capabilities.

10 best practices for this story:

  1. Use a tool which supports multiple formats for story document input.
  2. Guard against loss of data by ensuring regular backups.
  3. Build in functionalities for automatic categorization and storage of outputs.
  4. Consider security and privacy issues when storing and accessing input and output files.
  5. Establish an intuitive user interface to enhance user experience.
  6. Ensure the system can handle large files & varying file types.
  7. Application should provide clear and detailed error messages when an error occurs during generation of images.
  8. Implement strategies for efficient use of resources (e.g., time, processing power) when creating images.
  9. Ensure compatibility and ease of integration with existing systems.
  10. Ensure the application's sustainability by considering future needs and providing for scalability.

4. Functionality or Improvement

Functional Requirements:

  1. Extract main scenes from a supplied story.
    1.1. Identify common elements within the scenes.
    1.2. Extract common elements into a separate entity.
  2. Create detailed prompts for each scene.
    2.1. Adapt prompts to the provided style.
    2.2. Incorporate common elements.
  3. Generate images for each scene based on the prompts.
  4. Store generated images to a user-defined folder.
  5. Allow user to input story and style.
  6. Automate the image generation process.
  7. Provide options for output folders.
  8. Ensure compatibility with various file formats.
  9. Facilitate easy retrieval and sharing of created images.
  10. Allow customization of image generation.

5. Non-functional Requirements

  1. Provide pleasant and easy to use CLI.
  2. Include comprehensive user guides and documentation.

6. Acceptance Criteria

  1. User is able to provide story and style as input.
  2. Application correctly identifies main scenes and common elements.
  3. Application generates accurately styled images for each scene.
  4. Images are stored in user-defined folder.
  5. All generated files are stored securely and can be easily retrieved.

7. Business Value

This application will stimulate creativity among students and make teaching more effective and enjoyable. It can also be used to create visual aids for presentations, thereby aiding understanding and retention. Furthermore, the application can be a powerful tool for storytellers, writers, and content creators.

8. Positive Test Cases

  1. Provide story in text format and style, the application should create visuals correctly.
  2. Test the application with different styles, it should adapt successfully.
  3. The application is able to identify and extract common elements in different scenarios.
  4. Test the application's ability to handle large files and different file types.

9. Negative Test Cases

  1. Test how the application handles unavailable or inaccessible output folders.
  2. Test the application's handling of incorrect or invalid input files or formats.

10. Overview of Documentations

Documentation should include user manual, troubleshooting guide, privacy policy, and data handling protocol.

11. Typical Bugs and Pitfalls

  1. Inaccurate scene spotting
  2. Failure to adapt to provided style
  3. Unsuccessful image storage
  4. Poor UI design
  5. Slow performance

Developers can avoid these by following best practices, thorough testing and continuous application improvement.

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