Estadísticas y predicciones de Federal A Zona C

P: ¿Qué es exactamente el torneo Federal A?

R: El torneo Federal A es uno de los torneos más importantes de la tercera categoría del fútbol argentino. Su principal objetivo es proveer una plataforma para que equipos menos reconocidos participen, dando la oportunidad a muchos clubes pequeños para demostrar sus capacidades y aspirar al ascenso a categorías superiores.

P: ¿Cómo funciona el sistema de ascenso?

R: El sistema de ascenso en el Federal A sigue un modelo complejo que se basa tanto en la fase regular como en los playoffs finales. Los mejores equipos de cada zona se clasifican para una serie de rondas decisivas donde se determina quién asciende a la Primera B Nacional, la segunda división del fútbol local.

P: ¿Dónde puedo encontrar predicciones diarias?

R: Nuestro contenido está actualizado diariamente con las últimas predicciones expertas sobre cada partido. Puedes acceder a nuestras recomendaciones directamente desde nuestra página web o mediante nuestras notificaciones mediante correo electrónico y redes sociales.

P: ¿Qué tipo de estadísticas son más útiles para las apuestas?

R: Las estadísticas más útiles incluyen el número de goles anotados y recibidos, diferencias de goles, puntos obtenidos durante la fase regular, desempeños recientes, historial entre equipos y la condición física actualizada de los jugadores claves. Estas variables juntas te ofrecen una perspectiva equilibrada para realizar apuestas informadas.

P: ¿Puedo confiar en las cuotas para decidir mis apuestas?

R: Aunque las cuotas son una herramienta útil para medir la percepción del mercado, siempre deben usarse en combinación con un análisis propio detallado. Las cuotas reflejan la opinión pública generalizada y pueden ser influenciadas por factores que no están necesariamente relacionados con el rendimiento real. Por lo tanto, usarlas como referencia en vez de un dictamen absoluto es lo más recomendable.

P: ¿Qué son las estrategias ganadoras?atssumit/QA<|file_sep|>/README.md # QA Key concepts about QA <|file_sep|># Linting in Python tech stack Linter are very useful software tools that help programmers read and write cleaner code. Let’s first get to know what linters do and how they do it. A linter is what you run in your favorite Integrated Development Environment (IDE) to check for common programming errors in your code, undefined variables, unused variables/code segments and formatting issues. The best analogy is to see a linter like some type of “spell check” for your code except it’s doing more than just that. ## How does it work? Linters are used to statically analyze program codes as an independent step before compilation. At high level, It means that they help to find mistakes in program codes that could lead to logic bugs/errors before the code is even compiled or executed. They also help to correct and prevent insecure coding practices by analyzing source code. To understand how it works with python code, we have to understand that linters typically do not execute your program code but instead parse the code string into their own intermediate representation (IR). Note that this IR is completely independent of how python parses and executes the same code. After parsing the code string into IR, these linter packages then verify if there are any syntax errors or logical errors in the code. ## Types of Linters There are two types of linters: * Static code validators which checks source code for style inconsistencies and programming errors. * Dynamic code analyzers monitors application runtime for undefined errors or an unexpected program behavior. ## PyLint [PyLint](https://www.pylint.org/) is the most popular static checker for Python source code. ## flake8 Another very popular source code checker is [flake8](https://flake8.pycqa.org/en/latest/), it is proposed as a combination of `pycodestyle`, `pyflakes` and `mccabe` (a complexity checker). ### Other useful tools to use are * [bandit](https://bandit.readthedocs.io/en/latest/) – Check for common security issues in your code. * [prospector](https://prospector.landscape.io/) – Advanced Quality control tool that offers linting on top of other features like coding standard enforcement, unit tests development and others. * [pyrama](https://pyrama.github.io/) - Automatically beautify your python code. * [Pydocstyle](https://www.pydocstyle.org/en/stable/) - Book convention deviations from PEP257. * [vulture](https://github.com/jendrikseipp/vulture)– Detect unused python code. * [wemake-python-styleguide](https://wemake-python-stylegui.de/) – Strict python linter that aims to be almost 100% accurate. ### Enforcement To enforce the coding best practice and prevent bad code from going into production it is necessary to automate each step of the linting process as much as possible. It should be no choice to avoid running linters when adding/modifying code. Every code push should run all linters. <|repo_name|>atssumit/QA<|file_sep|>/Functional Testing.md # Functional Testing Software specification is just a contract between customer/developer. Write a bunch of statements or test cases which can be validated and verified using automation tools. ## Important guidelines: * Every functional test case should be exhausted by automation tools. * Maintain a separate folder/libraries at project level for automation specific test cases and framework specific configuration files (ex.pom.xml). * Use data driven approach where ever possible to test application on certain volume of data. * Defects caused by automation tools should be raised in the respective tool’s defect tracker (ex. Selenium). ## What should be included in your test case design? 1. **Test case name** - It should represent the feature that it is going to test. 2. **Test steps** - The name of the test step should be descriptive in nature so that developer/user directly understand what action this step is going to cover. The steps should be ordered from application level to detailed levels. 3. **Test data** - It can be some specific values which would be hard coded in test steps or prepared at run time by writing specific test scripts. 4. **Expected result** - To enable validation mechanism on what action just performed and to ensure that this action leads to expected outcome. The expected result should also have descriptive statements. 5. **Actual result** - The result that has been obtained after executing the testing steps against the system under test. 6. **Status** - The status of the test case should be calculated from actual result and expected/outcome. If both are same we say pass else fail. 7. **Comments** - The purpose of including comments is to describe the test case reading with some contextual descriptions. These comments can be useful if this test case will require the attention of someone else like developer/analyst 8. **Additional information** - Description of issue when a defect has been found. ## Example **1. Functional test case design:** | Test case Name | Test steps | Test data | Expected result | |-|-|-|-| | Add new address | User login with test credential | Address information | Validation / Confirmation message | **2. Test report (example when a defect has been found)** | Test case Name | Status | Actual result | Additional info | |:-|:-|:-|:-| | Add new address | Fail | System prompts user to enter address name | User failed to enter address in ‘New Address’ dropdown list ## Designing a good test scenario To write good test scenarios do as follows: 1. Compare the requirements of functionality you are testing with expected outcomes. 2. Make sure that test cas name is defined properly. 3. Test steps are defined with valid input & expected output. 4. Include some edge cases which might not be covered under normal flow. 5. Add few scenarios which will ensure the negative outcome (ex: user inputted invalid data). ## Naming convention When preparing test cases or test scenarios developers should follow some known conventions so that any third party can understand what was being tested. For example: 1. Test Case Name should be (Verb + Object + Summary) 2. Test Name should always give user clear understanding of what was being tested like: * Login functionality * Google accounts validation * Contact us page functionality * Etc. The above naming convention ensures that any new developer who joins the team or works on this project can understand what has been tested against this feature. ## Identify & Test Functionality Boundaries Always remember that functional requirement describes what an application does and not how its built. As testers we have to understand how each feature can be tested against these functional requirements by defining scope of application literally (boundaries). If we don’t define boundaries beforehand tester starts identifying application capabilities and not limitations. ## Test Case Categories There are two types of test cases to be created separately 1. **Integration Testing:** It tests whether two components of the application work correctly together or not. When two or more components need to communicate with each other they may fail to do so because of reasons like mismatched data sent by one component instead of receiving expected data by