Fashion-store app

Professional Mobile App "Fashion/Clothes Store" using Flutter

Flutter Fashion Store App

Provides an executable that converts a rails app's config structure. The basic idea is that environments themselves are now first-class citizens, allowing you to create several environments (e.g. staging, prodtest, demo, etc.) in a clean, organized fashion. Each environment is given its own folder where it can store its own set of configuration files (think mongrel configs, apache configs, etc.) without polluting the top-level config/ directory.

👗 Full-stack React app that's a one stop shop for jet-setters: store your trips in a calendar, create itineraries that are saved in Mongo, & receive fab fashion advice through the Tumblr + Open Weather Map API

pay with pi leading payment app Use pay with pi for the fastest, most secure online and in-store payments! Pay for movie tickets, food, coffee, fashion, gas, and many more in Pay with Pi. Quick, easy, convenient and secure. **Send / Receive Payments** Pay in store or send money to friends

Fashion. is a sleek mobile app for an e-commerce store selling stylish women's outfits. Built using React Native and Expo, the app features a polished UI designed in Figma. With 6+ screens, it provides a seamless shopping experience for customers to browse, filter, and purchase the latest trends

# All the News That's Fit to Scrape ### Overview In this assignment, you'll create a web app that lets users view and leave comments on the latest news. But you're not going to actually write any articles; instead, you'll flex your Mongoose and Cheerio muscles to scrape news from another site. ### Before You Begin 1. Create a GitHub repo for this assignment and clone it to your computer. Any name will do -- just make sure it's related to this project in some fashion. 2. Run `npm init`. When that's finished, install and save these npm packages: 1. express 2. express-handlebars 3. mongoose 4. cheerio 5. axios 3. **NOTE**: If you want to earn complete credit for your work, you must use all five of these packages in your assignment. 4. In order to deploy your project to Heroku, you must set up an mLab provision. mLab is remote MongoDB database that Heroku supports natively. Follow these steps to get it running: 5. Create a Heroku app in your project directory. 6. Run this command in your Terminal/Bash window: * `heroku addons:create mongolab` * This command will add the free mLab provision to your project. 7. When you go to connect your mongo database to mongoose, do so the following way: ```js // If deployed, use the deployed database. Otherwise use the local mongoHeadlines database var MONGODB_URI = process.env.MONGODB_URI || "mongodb://localhost/mongoHeadlines"; mongoose.connect(MONGODB_URI); ``` * This code should connect mongoose to your remote mongolab database if deployed, but otherwise will connect to the local mongoHeadlines database on your computer. 8. [Watch this demo of a possible submission](https://youtu.be/4ltZr3VPmno). See the deployed demo application [here](http://nyt-mongo-scraper.herokuapp.com/). 9. Your site doesn't need to match the demo's style, but feel free to attempt something similar if you'd like. Otherwise, just be creative! ### Commits Having an active and healthy commit history on GitHub is important for your future job search. It is also extremely important for making sure your work is saved in your repository. If something breaks, committing often ensures you are able to go back to a working version of your code. * Committing often is a signal to employers that you are actively working on your code and learning. * We use the mantra “commit early and often.” This means that when you write code that works, add it and commit it! * Numerous commits allow you to see how your app is progressing and give you a point to revert to if anything goes wrong. * Be clear and descriptive in your commit messaging. * When writing a commit message, avoid vague messages like "fixed." Be descriptive so that you and anyone else looking at your repository knows what happened with each commit. * We would like you to have well over 200 commits by graduation, so commit early and often! ### Submission on BCS * **This assignment must be deployed.** * Please submit both the deployed Heroku link to your homework AND the link to the Github Repository! ## Instructions * Create an app that accomplishes the following: 1. Whenever a user visits your site, the app should scrape stories from a news outlet of your choice and display them for the user. Each scraped article should be saved to your application database. At a minimum, the app should scrape and display the following information for each article: * Headline - the title of the article * Summary - a short summary of the article * URL - the url to the original article * Feel free to add more content to your database (photos, bylines, and so on). 2. Users should also be able to leave comments on the articles displayed and revisit them later. The comments should be saved to the database as well and associated with their articles. Users should also be able to delete comments left on articles. All stored comments should be visible to every user. * Beyond these requirements, be creative and have fun with this! ### Tips * Go back to Saturday's activities if you need a refresher on how to partner one model with another. * Whenever you scrape a site for stories, make sure an article isn't already represented in your database before saving it; Do not save any duplicate entries. * Don't just clear out your database and populate it with scraped articles whenever a user accesses your site. * If your app deletes stories every time someone visits, your users won't be able to see any comments except the ones that they post. ### Helpful Links * [MongoDB Documentation](https://docs.mongodb.com/manual/) * [Mongoose Documentation](http://mongoosejs.com/docs/api.html) * [Cheerio Documentation](https://github.com/cheeriojs/cheerio) ### Reminder: Submission on BCS * Please submit both the deployed Heroku link to your homework AND the link to the Github Repository! --- ### Minimum Requirements * **This assignment must be deployed.** Attempt to complete homework assignment as described in instructions. If unable to complete certain portions, please pseudocode these portions to describe what remains to be completed. Hosting on Heroku and adding a README.md are required for this homework. In addition, add this homework to your portfolio, more information can be found below. --- ### Hosting on Heroku Now that we have a backend to our applications, we use Heroku for hosting. Please note that while **Heroku is free**, it will request credit card information if you have more than 5 applications at a time or are adding a database. Please see [Heroku’s Account Verification Information](https://devcenter.heroku.com/articles/account-verification) for more details. --- ### Create a README.md Add a `README.md` to your repository describing the project. Here are some resources for creating your `README.md`. Here are some resources to help you along the way: * [About READMEs](https://help.github.com/articles/about-readmes/) * [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) --- ### Add To Your Portfolio After completing the homework please add the piece to your portfolio. Make sure to add a link to your updated portfolio in the comments section of your homework so the TAs can easily ensure you completed this step when they are grading the assignment. To receive an 'A' on any assignment, you must link to it from your portfolio. --- ### One Last Thing If you have any questions about this project or the material we have covered, please post them in the community channels in slack so that your fellow developers can help you! If you're still having trouble, you can come to office hours for assistance from your instructor and TAs. That goes threefold for this unit: MongoDB and Mongoose compose a challenging data management system. If there's anything you find confusing about these technologies, don't hesitate to speak with someone from the Boot Camp team. **Good Luck!**

Free Responsive Prestashop Themes (Black Hawk) Features 1. Multiple Language This themes is compatible in 6 different language – Br, Fr, It, De, Es, En respectively . All the language was translated through google online language translation tool. 2. Responsive Template It is compatible in multiple device in PC, Notebook and tablet such as iPad and Android Tablet and mobile devices. 3. Front Banner We have integrated Jquery Zoom Slider to promote your highlighted product and it is fully customizable. Here, we have utilize default prestashop’s image slider module . It’s easy to configuration through back office of prestashop with “Jquery Zoom Slider” 4. Prestashop Compatible This theme is compatible from Prestashop 1.5.1.0 – 1.5.2.0. Most probably it will compatible on all prestashop 1.5 version series 5. Quick Cart We have integrated quick cart view once you hover link of cart in header portion. 6. Extra Modules zoomslider, click here for Demonstration Buy one Coffee to us :) Download Black Hawk Prestashop Themes Share this: More Related Posts Touch me not 1.0 (Prestashop 1.5.2.0 Themes) release (4) Jewelery Store – Responsive Prestashop Themes (4) Fashion Clothes Store Template – Vip Dress (4) Jewelry Store 2.6 themes for Prestashop 1.5.2.0 (3) Hard Rocker prestashop themes for restaurant (3) Snow Bear – Prestashop Themes (3) Mobile Themes for Prestashop – DevilMobile (3) Download Free Prestashop Themes – BlackInk (3) Cave – Prestashop Themes (2) Prestashop Themes for Movie, Game’s CD and Book eshop – Deep Sea (2) This entry was posted in Black, Electronics, Free, Responsive Prestashop Themes, Show all Themes and tagged Black Hawk, Free download Prestashop 1.5.2.0 themes, free mobile prestashop themes, Free Prestashop 1.5 themes, Free Responsive Presashop Themes, Responsive Prestashop Themes on December 19, 2012. Edit Post navigation ← Touch me not 1.0 (Prestashop 1.5.2.0 Themes) release Jewelry Store 2.6.5 is released → 2 thoughts on “100% Free Responsive Prestashop Themes 1.5 (Black Hawk )” Pingback: Thème responsive prestashop : Black Hawk (Edit) Pingback: 11 Great Free PrestaShop 1.5 Themes - App Rental Online Blog (Edit) Translation EnglishDanskNederlandsFrançaisDeutschBahasa IndonesiaItaliano日本語PolskiPortuguêsRomânăРусскийSlovenčinaSlovenščinaEspañolTürkçe Edit Translation Categories Article (8) Beauty & Cosmetics (4) Books and CD (2) Cloth Stores (4) Color (21) Black (12) Blue (6) Bringle (1) Brown (2) Green (3) Pink (4) Red (2) White (6) yellow (1) Electronics (3) Free (4) Furniture (1) Jewelery & Watch (5) PSD Files (3) Responsive Prestashop Themes (7) restaurant (1) Show all Themes (16) Sports (1) Uncategorized (6) Like Us :) Recent Posts Softsteam 2.0.2 is live in demonstration Top 10 Ecommerce Web Store in 2012 Prestashop 1.5.3 is released. YouTube Demonstrationeo for black hawk, jewelrystore and touch me not Jewelry Store 2.6.5 is released Meta Site Admin Log out Entries RSS Comments RSS WordPress.org Comments admin on Touch me not 1.0 (Prestashop 1.5.2.0 Themes) release gary on Touch me not 1.0 (Prestashop 1.5.2.0 Themes) release 11 Great Free PrestaShop 1.5 Themes - App Rental Online Blog on 100% Free Responsive Prestashop Themes 1.5 (Black Hawk ) Thème responsive prestashop : Black Hawk on 100% Free Responsive Prestashop Themes 1.5 (Black Hawk ) admin on Download Free Prestashop Themes – BlackInk Tags Article Beauty & Cosmetics Black Blue Books and CD Bringle Brown Cloth Stores Color Electronics Free Furniture Green Jewelery & Watch Pink PSD Files Red Responsive Prestashop Themes restaurant Show all Themes Sports Uncategorized White yellow Subscribe to Blog via Email Enter your email address to subscribe to this blog and receive notifications of new posts by email.

# All the News That's Fit to Scrape ### Overview In this assignment, you'll create a web app that lets users view and leave comments on the latest news. But you're not going to actually write any articles; instead, you'll flex your Mongoose and Cheerio muscles to scrape news from another site. ### Before You Begin 1. Create a GitHub repo for this assignment and clone it to your computer. Any name will do -- just make sure it's related to this project in some fashion. 2. Run `npm init`. When that's finished, install and save these npm packages: 3. express 4. express-handlebars 5. mongoose 6. body-parser 7. cheerio 8. request 9. **NOTE**: If you want to earn complete credit for your work, you must use all six of these packages in your assignment. 10. In order to deploy your project to Heroku, you must set up an mLab provision. mLab is remote MongoDB database that Heroku supports natively. Follow these steps to get it running: 11. Create a Heroku app in your project directory. 12. Run this command in your Terminal/Bash window: * `heroku addons:create mongolab` * This command will add the free mLab provision to your project. 13. When you go to connect your mongo database to mongoose, do so the following way: ```js // If deployed, use the deployed database. Otherwise use the local mongoHeadlines database var MONGODB_URI = process.env.MONGODB_URI || "mongodb://localhost/mongoHeadlines"; // Set mongoose to leverage built in JavaScript ES6 Promises // Connect to the Mongo DB mongoose.Promise = Promise; mongoose.connect(MONGODB_URI); ``` * This code should connect mongoose to your remote mongolab database if deployed, but otherwise will connect to the local mongoHeadlines database on your computer. 14. [Watch this demo of a possible submission](mongo-homework-demo.mov). See the deployed demo application [here](http://nyt-mongo-scraper.herokuapp.com/). 15. Your site doesn't need to match the demo's style, but feel free to attempt something similar if you'd like. Otherwise, just be creative! ### Submission on BCS * Please submit both the deployed Heroku link to your homework AND the link to the Github Repository! ## Instructions * Create an app that accomplishes the following: 1. Whenever a user visits your site, the app should scrape stories from a news outlet of your choice and display them for the user. Each scraped article should be saved to your application database. At a minimum, the app should scrape and display the following information for each article: * Headline - the title of the article * Summary - a short summary of the article * URL - the url to the original article * Feel free to add more content to your database (photos, bylines, and so on). 2. Users should also be able to leave comments on the articles displayed and revisit them later. The comments should be saved to the database as well and associated with their articles. Users should also be able to delete comments left on articles. All stored comments should be visible to every user. * Beyond these requirements, be creative and have fun with this! ### Tips * Go back to Saturday's activities if you need a refresher on how to partner one model with another. * Whenever you scrape a site for stories, make sure an article isn't already represented in your database before saving it; we don't want duplicates. * Don't just clear out your database and populate it with scraped articles whenever a user accesses your site. * If your app deletes stories every time someone visits, your users won't be able to see any comments except the ones that they post. ### Helpful Links * [MongoDB Documentation](https://docs.mongodb.com/manual/) * [Mongoose Documentation](http://mongoosejs.com/docs/api.html) * [Cheerio Documentation](https://github.com/cheeriojs/cheerio) ### Reminder: Submission on BCS * Please submit both the deployed Heroku link to your homework AND the link to the Github Repository! --- ### Minimum Requirements Attempt to complete homework assignment as described in instructions. If unable to complete certain portions, please pseudocode these portions to describe what remains to be completed. Hosting on Heroku and adding a README.md are required for this homework. In addition, add this homework to your portfolio, more information can be found below. --- ### Hosting on Heroku Now that we have a backend to our applications, we use Heroku for hosting. Please note that while **Heroku is free**, it will request credit card information if you have more than 5 applications at a time or are adding a database. Please see [Heroku’s Account Verification Information](https://devcenter.heroku.com/articles/account-verification) for more details. --- ### Create a README.md Add a `README.md` to your repository describing the project. Here are some resources for creating your `README.md`. Here are some resources to help you along the way: * [About READMEs](https://help.github.com/articles/about-readmes/) * [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) --- ### Add To Your Portfolio After completing the homework please add the piece to your portfolio. Make sure to add a link to your updated portfolio in the comments section of your homework so the TAs can easily ensure you completed this step when they are grading the assignment. To receive an 'A' on any assignment, you must link to it from your portfolio. --- ### One Last Thing If you have any questions about this project or the material we have covered, please post them in the community channels in slack so that your fellow developers can help you! If you're still having trouble, you can come to office hours for assistance from your instructor and TAs. That goes threefold for this week: MongoDB and Mongoose compose a challenging data management system. If there's anything you find confusing about these technologies, don't hesitate to speak with someone from the Boot Camp team. **Good Luck!**

MVP, Builder, Router, DI, CoreData, async/await, Keychain, CollectionView with snapshots, Font Styles

Pay with Pi leading payment app Use Pay with pi for the fastest, most secure online and in-store payments! Pay for movie tickets, food, coffee, fashion, gas, and many more in Pay with Pi. Quick, easy, convenient and secure. **Send / Receive Payments** Pay in store or send money to friends. No need to carry cash or worry about loose change!

Fashion Store App we've redefined the art of shopping by blending technology, design, and user-centricity. With a strong emphasis on state-of-the-art state management, seamless API integration, and efficient HTTP client usage, we offer an unparalleled shopping experience.

Pay with Pi leading payment app Use Pay with pi for the fastest, most secure online and in-store payments! Pay for movie tickets, food, coffee, fashion, gas, and many more in Pay with Pi.

# All the News That's Fit to Scrape ### Overview In this assignment, you'll create a web app that lets users view and leave comments on the latest news. But you're not going to actually write any articles; instead, you'll flex your Mongoose and Cheerio muscles to scrape news from another site. ### Before You Begin 1. Create a GitHub repo for this assignment and clone it to your computer. Any name will do -- just make sure it's related to this project in some fashion. 2. Run `npm init`. When that's finished, install and save these npm packages: 1. express 2. express-handlebars 3. mongoose 4. cheerio 5. axios 3. **NOTE**: If you want to earn complete credit for your work, you must use all five of these packages in your assignment. 4. In order to deploy your project to Heroku, you must set up an mLab provision. mLab is remote MongoDB database that Heroku supports natively. Follow these steps to get it running: 5. Create a Heroku app in your project directory. 6. Run this command in your Terminal/Bash window: * `heroku addons:create mongolab` * This command will add the free mLab provision to your project. 7. When you go to connect your mongo database to mongoose, do so the following way: ```js // If deployed, use the deployed database. Otherwise use the local mongoHeadlines database var MONGODB_URI = process.env.MONGODB_URI || "mongodb://localhost/mongoHeadlines"; mongoose.connect(MONGODB_URI); ``` * This code should connect mongoose to your remote mongolab database if deployed, but otherwise will connect to the local mongoHeadlines database on your computer. 8. [Watch this demo of a possible submission](https://youtu.be/4ltZr3VPmno). See the deployed demo application [here](http://nyt-mongo-scraper.herokuapp.com/). 9. Your site doesn't need to match the demo's style, but feel free to attempt something similar if you'd like. Otherwise, just be creative! ### Commits Having an active and healthy commit history on GitHub is important for your future job search. It is also extremely important for making sure your work is saved in your repository. If something breaks, committing often ensures you are able to go back to a working version of your code. * Committing often is a signal to employers that you are actively working on your code and learning. * We use the mantra “commit early and often.” This means that when you write code that works, add it and commit it! * Numerous commits allow you to see how your app is progressing and give you a point to revert to if anything goes wrong. * Be clear and descriptive in your commit messaging. * When writing a commit message, avoid vague messages like "fixed." Be descriptive so that you and anyone else looking at your repository knows what happened with each commit. * We would like you to have well over 200 commits by graduation, so commit early and often! ### Submission on BCS * **This assignment must be deployed.** * Please submit both the deployed Heroku link to your homework AND the link to the Github Repository! ## Instructions * Create an app that accomplishes the following: 1. Whenever a user visits your site, the app should scrape stories from a news outlet of your choice and display them for the user. Each scraped article should be saved to your application database. At a minimum, the app should scrape and display the following information for each article: * Headline - the title of the article * Summary - a short summary of the article * URL - the url to the original article * Feel free to add more content to your database (photos, bylines, and so on). 2. Users should also be able to leave comments on the articles displayed and revisit them later. The comments should be saved to the database as well and associated with their articles. Users should also be able to delete comments left on articles. All stored comments should be visible to every user. * Beyond these requirements, be creative and have fun with this! ### Tips * Go back to Saturday's activities if you need a refresher on how to partner one model with another. * Whenever you scrape a site for stories, make sure an article isn't already represented in your database before saving it; Do not save any duplicate entries. * Don't just clear out your database and populate it with scraped articles whenever a user accesses your site. * If your app deletes stories every time someone visits, your users won't be able to see any comments except the ones that they post. ### Helpful Links * [MongoDB Documentation](https://docs.mongodb.com/manual/) * [Mongoose Documentation](http://mongoosejs.com/docs/api.html) * [Cheerio Documentation](https://github.com/cheeriojs/cheerio) ### Reminder: Submission on BCS * Please submit both the deployed Heroku link to your homework AND the link to the Github Repository! --- ### Minimum Requirements * **This assignment must be deployed.** Attempt to complete homework assignment as described in instructions. If unable to complete certain portions, please pseudocode these portions to describe what remains to be completed. Hosting on Heroku and adding a README.md are required for this homework. In addition, add this homework to your portfolio, more information can be found below. --- ### Hosting on Heroku Now that we have a backend to our applications, we use Heroku for hosting. Please note that while **Heroku is free**, it will request credit card information if you have more than 5 applications at a time or are adding a database. Please see [Heroku’s Account Verification Information](https://devcenter.heroku.com/articles/account-verification) for more details. --- ### Create a README.md Add a `README.md` to your repository describing the project. Here are some resources for creating your `README.md`. Here are some resources to help you along the way: * [About READMEs](https://help.github.com/articles/about-readmes/) * [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) --- ### Add To Your Portfolio After completing the homework please add the piece to your portfolio. Make sure to add a link to your updated portfolio in the comments section of your homework so the TAs can easily ensure you completed this step when they are grading the assignment. To receive an 'A' on any assignment, you must link to it from your portfolio. --- ### One Last Thing If you have any questions about this project or the material we have covered, please post them in the community channels in slack so that your fellow developers can help you! If you're still having trouble, you can come to office hours for assistance from your instructor and TAs. That goes threefold for this unit: MongoDB and Mongoose compose a challenging data management system. If there's anything you find confusing about these technologies, don't hesitate to speak with someone from the Boot Camp team. **Good Luck!**

Fashion-Store-Ecommerce-App

No description available

No description available

bia-baby store: A React Native app for a children’s fashion and party store, developed as a group project for Estácio’s Android Development course.

🧥 Have Fashi – Web-Based Inventory Management System A modern, responsive, and user-friendly inventory management web app built for the Have Fashi men’s fashion store. The system allows shop owners to easily manage products, sales, suppliers, and reports through a clean admin dashboard built with Next.js, React, and Tailwind CSS.

All the News That's Fit to Scrape Overview In this assignment, you'll create a web app that lets users view and leave comments on the latest news. But you're not going to actually write any articles; instead, you'll flex your Mongoose and Cheerio muscles to scrape news from another site. Before You Begin Create a GitHub repo for this assignment and clone it to your computer. Any name will do -- just make sure it's related to this project in some fashion. Run npm init. When that's finished, install and save these npm packages: express express-handlebars mongoose cheerio axios NOTE: If you want to earn complete credit for your work, you must use all five of these packages in your assignment. In order to deploy your project to Heroku, you must set up an mLab provision. mLab is remote MongoDB database that Heroku supports natively. Follow these steps to get it running: Create a Heroku app in your project directory. Run this command in your Terminal/Bash window: heroku addons:create mongolab This command will add the free mLab provision to your project. When you go to connect your mongo database to mongoose, do so the following way: // If deployed, use the deployed database. Otherwise use the local mongoHeadlines database var MONGODB_URI = process.env.MONGODB_URI || "mongodb://localhost/mongoHeadlines"; mongoose.connect(MONGODB_URI); This code should connect mongoose to your remote mongolab database if deployed, but otherwise will connect to the local mongoHeadlines database on your computer. Watch this demo of a possible submission. See the deployed demo application here. Your site doesn't need to match the demo's style, but feel free to attempt something similar if you'd like. Otherwise, just be creative! Commits Having an active and healthy commit history on GitHub is important for your future job search. It is also extremely important for making sure your work is saved in your repository. If something breaks, committing often ensures you are able to go back to a working version of your code. Committing often is a signal to employers that you are actively working on your code and learning. We use the mantra “commit early and often.” This means that when you write code that works, add it and commit it! Numerous commits allow you to see how your app is progressing and give you a point to revert to if anything goes wrong. Be clear and descriptive in your commit messaging. When writing a commit message, avoid vague messages like "fixed." Be descriptive so that you and anyone else looking at your repository knows what happened with each commit. We would like you to have well over 200 commits by graduation, so commit early and often! Submission on BCS This assignment must be deployed. * Please submit both the deployed Heroku link to your homework AND the link to the Github Repository! Instructions Create an app that accomplishes the following: Whenever a user visits your site, the app should scrape stories from a news outlet of your choice and display them for the user. Each scraped article should be saved to your application database. At a minimum, the app should scrape and display the following information for each article: Headline - the title of the article Summary - a short summary of the article URL - the url to the original article Feel free to add more content to your database (photos, bylines, and so on). Users should also be able to leave comments on the articles displayed and revisit them later. The comments should be saved to the database as well and associated with their articles. Users should also be able to delete comments left on articles. All stored comments should be visible to every user. Beyond these requirements, be creative and have fun with this! Tips Go back to Saturday's activities if you need a refresher on how to partner one model with another. Whenever you scrape a site for stories, make sure an article isn't already represented in your database before saving it; Do not save any duplicate entries. Don't just clear out your database and populate it with scraped articles whenever a user accesses your site. If your app deletes stories every time someone visits, your users won't be able to see any comments except the ones that they post. Helpful Links MongoDB Documentation Mongoose Documentation Cheerio Documentation

<!-- markdownlint-disable no-inline-html --> <!-- markdownlint-disable ol-prefix --> # GRAVITON (aka the TEAL Blackbox Toolkit): Program Reporting and Testing via Dry Runs <img width="345" alt="http://cds.cern.ch/record/2315186/files/scoap3-fulltext.pdf" src="https://user-images.githubusercontent.com/291133/160721859-21a3560a-0a82-4249-aa54-5ede4c60f8d2.png"> **NOTE: to get math formulas to render here using Chrome, add the [xhub extension](https://chrome.google.com/webstore/detail/xhub/anidddebgkllnnnnjfkmjcaallemhjee/related) and reload** **DISCLAIMER**: Graviton is subject to change and makes no backwards compatability guarantees. ## Blackbox Testing Howto ### What is TEAL Blackbox Testing? TEAL Blackbox Testing lets you gain confidence that your Algorand smart contracts are correct by writing assertions and and analyzing results via dry runs. ### Why Blackbox Testing? Here are some use cases: * by allowing you to assert that certain invariants hold over a large set of inputs you gain greater confidence that your TEAL programs and AVM smart contracts work as designed * when tweaking, refactoring or optimizing your TEAL source, ensure that no regressions have occured * allows AVM developers to practice the art of TTDD (TEAL Test Driven Development) ## Simple TEAL Blackbox Toolkit Example: Program for $`x^2`$ Consider this [TEAL program](./tests/teal/lsig_square.teal) for computing $`x^2`$: ```plain #pragma version 6 arg 0 btoi callsub square_0 return // square square_0: store 0 load 0 pushint 2 // 2 exp retsub ``` We'd like to write some unit tests to validate its correctness and make **assertions** about the: * program's opcode cost * program's stack * stack's height * scratch variables * final log message (this is especially useful for [ABI-compliant programs](https://developer.algorand.org/docs/get-details/dapps/smart-contracts/ABI/)) * status (**PASS** or **REJECT**) * error conditions that are and aren't encountered Even better, before making fine-grained assertions we'd like to get a sense of what the program is doing on a large set of inputs and discover _experimentally_ these **program invariants**. Let's go through how we can do this: * start by making basic assertions and validate them using dry runs (see "**Basic Assertions**" section below) * execute the program on a sequence of inputs and explore the results (see "**EDRA: Exploratory Dry Run Analysis**" section below) * create invariants for the entire sequence and assert that the invariants hold (see "**Advanced: Asserting Invariants on a Dry Run Sequence**" section below) > Becoming a TEAL Blackbox Toolkit Ninja involves 10 steps as described below ### Dry Run Environment Setup **STEP 1**. Start with a running local node and make note of Algod's port number (for our [standard sandbox](https://github.com/algorand/sandbox) this is `4001`) **STEP 2**. Set the `ALGOD_PORT` value in [tests/clients.py](./tests/clients.py#L7) to this port number. (The port is already pre-set to `4001` because [graviton](https://github.com/algorand/graviton)'s [CI process](https://en.wikipedia.org/wiki/Continuous_integration) uses the standad sandbox) ### TEAL Program for Testing: Logic Sig v. App **STEP 3**. Next, you'll need to figure out if your TEAL program should be a Logic Signature or an Application. Each of these program _modes_ has its merits, but we won't get into the pros/cons here. From a Blackbox Test's perspective, the main difference is how external arguments are handled. Logic sigs rely on the [arg opcode](https://developer.algorand.org/docs/get-details/dapps/avm/teal/opcodes/#arg-n) while apps rely on [txna ApplicationArgs i](https://developer.algorand.org/docs/get-details/dapps/avm/teal/opcodes/#txna-f-i). In our $`x^2`$ **logic sig** example, you can see on [line 2](./tests/teal/lsig_square.teal#L2) that the `arg` opcode is used. Because each argument opcode (`arg` versus `ApplicationArgs`) is mode-exclusive, any program that takes input will execute succesfully in _one mode only_. **STEP 4**. Write the TEAL program that you want to test. You can inline the test as described here or follow the approach of `./tests/integration/blackbox_test.py` and save under `./tests/teal`. So following the inline appraoch we begin our TEAL Blackbox script with an <a name="teal">inline teal source variable</a>: ```python teal = """#pragma version 6 arg 0 btoi callsub square_0 return // square square_0: store 0 load 0 pushint 2 // 2 exp retsub""" ``` ### The TEAL Blackbox Toolkit's Utitlity Classes The TEAL Blackbox Toolkit comes with the following main classes: * `DryRunExecutor` - executes dry run's for apps and logic sigs for one or more inputs * `DryRunInspector` - encapsulates a dry run's result for a single input and allows inspecting and making assertions about it * `Invariant` - class for asserting invariants about a _sequence_ of dry run executions in a declarative fashion ### Basic Assertions When executing a dry run using `DryRunExecutor` you'll get back `DryRunInspector` objects. Such objects have **assertable properties** which can be used to validate the dry run. **STEP 4**. Back to our $`x^2`$ example, and assuming the `teal` variable is defined [as above](#teal). You can run the following: ```python from graviton.blackbox import DryRunExecutor from tests.clients import get_algod algod = get_algod() x = 9 args = (x,) inspector = DryRunExecutor.dryrun_logicsig(algod, teal, args) assert inspector.status() == "PASS" assert inspector.stack_top() == x**2 ``` Here we have executed a dry run on input $`x=9`$, then asserted that: * the program status was `PASS` * the program exited with the top of its stack containing $`x^2 = 9^2 = 81`$ Some available _assertable properties_ are: * `stack_top()` * `last_log()` * `cost()` * `status()` * `final_scratch()` * `error()` * `max_stack_height()` See the [DryRunInspector class comment](./graviton/blackbox.py#L387) for more assertable properties and details. ### Printing out the TEAL Stack Trace for a Failing Assertion **STEP 5**. The `DryRunInspector`'s `report()` method lets you print out a handy report in the case of a failing assertion. Let's intentionally break the test case above by claiming that $`x^2 = x^3`$ for $`x=2`$ and print out this _report_ when our silly assertion fails. ```python from graviton.blackbox import DryRunExecutor from tests.clients import get_algod algod = get_algod() x = 2 args = (x,) inspector = DryRunExecutor.dryrun_logicsig(algod, teal, args) # This one's ok expected, actual = "PASS", inspector.status() assert expected == actual, inspector.report( args, f"expected {expected} but got {actual}" ) # This one's absurd! x^3 != x^2 expected, actual = x**3, inspector.stack_top() assert expected == actual, inspector.report( args, f"expected {expected} but got {actual}" ) ``` If we run the test (e.g. with `pytest`) we'll see a printout such as: ```sh AssertionError: =============== <<<<<<<<<<<expected 8 but got 4>>>>>>>>>>> =============== App Trace: step | PC# | L# | Teal | Scratch | Stack --------+-------+------+-------------------+-----------+---------------------- 1 | 1 | 1 | #pragma version 6 | | [] 2 | 2 | 2 | arg_0 | | [0x0000000000000002] 3 | 3 | 3 | btoi | | [2] 4 | 7 | 6 | label1: | | [2] 5 | 9 | 7 | store 0 | 0->2 | [] 6 | 11 | 8 | load 0 | | [2] 7 | 13 | 9 | pushint 2 | | [2, 2] 8 | 14 | 10 | exp | | [4] 9 | 6 | 4 | callsub label1 | | [4] 10 | 15 | 11 | retsub | | [4] =============== MODE: ExecutionMode.Signature TOTAL COST: None =============== FINAL MESSAGE: PASS =============== Messages: ['PASS'] Logs: [] =============== -----BlackBoxResult(steps_executed=10)----- TOTAL STEPS: 10 FINAL STACK: [4] FINAL STACK TOP: 4 MAX STACK HEIGHT: 2 FINAL SCRATCH: {0: 2} SLOTS USED: [0] FINAL AS ROW: {'steps': 10, ' top_of_stack': 4, 'max_stack_height': 2, 's@000': 2} =============== Global Delta: [] =============== Local Delta: [] =============== TXN AS ROW: {' Run': 0, ' cost': None, ' last_log': '`None', ' final_message': 'PASS', ' Status': 'PASS', 'steps': 10, ' top_of_stack': 4, 'max_stack_height': 2, 's@000': 2, 'Arg_00': 2} =============== <<<<<<<<<<<expected 8 but got 4>>>>>>>>>>> =============== assert 8 == 4 ``` In particular, we can: * Track the program execution by viewing its **App Trace** * 2 was assigned to **scratch slot #0** at step 5 * the stack ended up with **4** on top * the run **PASS**'ed * Read the message parameter that was provided and which explains in English what went wrong: `expected 8 but got 4` ### EDRA: Exploratory Dry Run Analysis Let's expand our investigation from a single dry-run to multiple runs or a **run sequence**. We'll observe how _assertable properties_ depend on inputs and conjecture some program invariants. To aid in the investigation we'll generate a report in CSV format (Comma Separated Values) where: * columns represent _assertable properties_ of dry-runs, and * rows represents dry-run executions for specific inputs **STEP 6**. Back to our $`x^2`$ example, here's how to generate a report with 1 row for each of the inputs `0, 1, ... , 15`: ```python from graviton.blackbox import DryRunExecutor, DryRunInspector from tests.clients import get_algod algod = get_algod() inputs = [(x,) for x in range(16)] run_results = DryRunExecutor.dryrun_logicsig_on_sequence(algod, teal, inputs) csv = DryRunInspector.csv_report(inputs, run_results) print(csv) ``` Note that each element in the `inputs` array `(x,)` is itself a tuple as `args` given to a dry run execution need to be of type `Sequence` (remember, that these will be passed to a TEAL program which may take one, several, or no inputs at all). At this point, you'll be able to look at your [dry run sequence results](./graviton/blackbox.py#L752) and conduct some analysis. For the $`x^2`$ example, after loading the CSV in Google sheets and reformating a bit it will look like: <img width="465" alt="image" src="https://user-images.githubusercontent.com/291133/158812699-318169e2-487c-4dac-b97b-a9db8148b638.png"> Pointing out some interesting results: * column `D` **Arg 00** has the input $`x`$ (it's the argument at index 0) * column `A` contains the **Run** number * column `E` **top of stack** is the value at program's termination, i.e. $`x^2`$ * column `B` **Status** of each runs **PASS**es _except for **Run 1** with **Arg 00** = 0_. (The first run **REJECT**s because $`0^2 = 0`$ and TEAL programs reject when the top of the stack is 0) * column `G` shows scratch slot **s@000** which stores the value of $`x`$ (except for the case $`x = 0`$ in which appears empty; in fact, slots always default to the zero value and an **<a name="0val-artifact">artifact</a>** of dry-runs is that they do not report when 0-values get stored into previously empty slots as no state change actually occurs) * column `F` **max stack height** is always 2. The final observation makes sense because there is no branching or looping in the program. **STEP 7**. We can re-cast the observed effects in `Columns E, B, G, F` as **invariants** written in Python as follows: * `inspector.stack_top() == x ** 2` * `inspector.max_stack_height() == 2` * `inspector.status() == ("REJECT" if x == 0 else "PASS")` * `inspector.final_scratch() == ({} if x == 0 else {0: x})` ### Advanced: Asserting Invariants on a Dry Run Sequence The final and most advanced topic we'll cover is how to assert that invariants hold on a sequence of inputs. Lets take the information we gleaned in our EDRA CSV report, and create an integration test out of it. There are two ways to achieve this goal: * Procedural invariant assertions * Declarative invariant assertions #### Procedural Blackbox Dry Run Sequence Assertions **STEP 8**. The procedural approach takes the _invariants_ and simply asserts them inside of a for loop that iterates over the inputs and dry runs. One can call each dry run execution independently, or use `DryRunExecutor`'s convenience methods `dryrun_app_on_sequence()` and `dryrun_logicsig_on_sequence()`. For example, let's assert that the above invariants hold for all $`x \leq 100`$: ```python from graviton.blackbox import DryRunExecutor from tests.clients import get_algod algod = get_algod() inputs = [(x,) for x in range(101)] dryrun_results = DryRunExecutor.dryrun_logicsig_on_sequence(algod, teal, inputs) for i, inspector in enumerate(dryrun_results): args = inputs[i] x = args[0] assert inspector.stack_top() == x**2 assert inspector.max_stack_height() == 2 assert inspector.status() == ("REJECT" if x == 0 else "PASS") assert inspector.final_scratch() == ({} if x == 0 else {0: x}) ``` #### Declarative Blackbox Dry Run Sequence Assertions **STEP 9**. The TEAL Blackbox Toolkit also allows for declarative style test writing. Let's define some invariants for a particular sequence of `lsig_square` TEAL program dry runs: ```python scenario = { "inputs": [(i,) for i in range(100)], "invariants": { DRProp.stackTop: lambda args: args[0] ** 2, DRProp.maxStackHeight: 2, DRProp.status: lambda i: "REJECT" if i[0] = 0 else "PASS", DRProp.finalScratch: lambda args: ({} if args[0] else {0: args[0]}), }, } ``` In the parlance of the TEAL Blackbox Toolkit, a set of such declarative assertions is called a **test scenario**. Scenarios are dict's containing two keys `inputs` and `invariants` and follow [certain conventions](./graviton/invariant.py#L101). In particular: * **inputs** gives a list of tuples, each tuple representing the `args` to be fed into a single dry run execution * **invariants** gives a dict that maps [DryRunProperty](./graviton/blackbox.py#L25)'s to an invariant _predicate_ In English, letting $`x`$ be the input variable for our square function, the above **test scenario**: * provides a list of 100 tuples of the form $`(x)`$ that will serve as args. * IE: $`(0), (1), (2), ... , (99)`$ * establishes 4 different _invariants_ as follows: * the **stack's top** will contain $`x^2`$ * the **max stack height** during execution is always 2 * the executions' **status** is **PASS** except for the case $`x=0`$ * the **final scratch** will have $`x`$ stored at slot `0` except for that strange $`x=0`$ case (recall the [0-val scratch slot artifact](#0val-artifact)) Declarative invariants make use of the following: * `DryRunProperty` (aka `DRProp`): an enum that acts as a key in a scenario's assertions dict * class `Invariant` * its constructor takes in a predicate (there are [4 kinds of predicates](#predicate)) and returns a callable that is used for runtime assertions * method `inputs_and_assertions()` validates a scenario and extracts out its assertions * method `dryrun_assert()` evaluates the dry-run sequence using the constructed `SequenceAssertion` To employ the declarative test scenario above write the following: ```python from graviton.blackbox import ( DryRunExecutor, DryRunProperty as DRProp, ExecutionMode, ) from graviton.invariant import Invariant from tests.clients import get_algod algod = get_algod() scenario = { "inputs": [(i,) for i in range(100)], "invariants": { DRProp.stackTop: lambda args: args[0] ** 2, DRProp.maxStackHeight: 2, DRProp.status: lambda args: "REJECT" if args[0] == 0 else "PASS", DRProp.finalScratch: lambda args: ({0: args[0]} if args[0] else {}), }, } # Validate the scenario and dig out inputs/invariants: inputs, invariants = Invariant.inputs_and_invariants( scenario, ExecutionMode.Signature ) # Execute the dry runs and obtain a sequence of DryRunInspectors: inspectors = DryRunExecutor.dryrun_logicsig_on_sequence(algod, teal, inputs) # Invariant assertions on sequence: for dr_property, invariant in invariants.items(): invariant.validates(dr_property, inputs, inspectors) ``` **STEP 10**. _**Deep Dive into Invariants via Exercises**_ Four kinds of <a name="predicate">predicates</a> are used to define _invariants_: 1. _simple python types_ - these are useful in the case of _constant_ invariants. In the above `maxStackHeight` is asserted to _**ALWAYS**_ equal 2 by using `2` in the declaration: `DRProp.maxStackHeight: 2` 2. _1-variable functions_ -these are useful when you have a python "simulator" for the invariant. In the above `stackTop` is asserted to be $`x^2`$ by using a lambda expression for $`x^2`$ in the declaration: `DRProp.stackTop: lambda args: args[0] ** 2` 3. _dictionaries_ of type `Dict[Tuple, Any]` - these are useful when you want to assert a discrete set of input-output pairs. For example, if you have 4 inputs that you want to assert are being squared, you could use ```python DRProp.stackTop: { (2,): 4, (7,): 49, (13,): 169, (11,): 121, } ``` >Note that this case illustrates why each `args` container should be a tuple intead of a list. In order to specify a map from args to expected, we need to make `args` a key in a dictionary. Python dictionary keys must be hashable and lists are _not hashable_ while tuples _are_ hashable, hence the tuple-requirement. 4. _2-variable functions_ -these are useful when your assertion is more subtle than out-and-out equality. For example, suppose you want to assert that the `cost` of each run is _between_ $`2n \pm 5`$ where $`n`$ is the first arg of the input. Then you could declare: `DRProp.cost: lambda args, actual: 2*args[0] - 5 <= actual <= 2*args[0] + 5` #### **EXERCISE A** Convert each of the lambda expressions used above to dictionaries that assert the same thing. #### **EXERCISE B** Use 2-variable functions in order to _ignore_ the weird $`x=0`$ cases above. #### _PARTIAL SOLUTIONS to EXERCISES_ **Exercise A Partial Solution**. For `DRProp.status`'s declaration you could define the `dict` using dictionary comprehension syntax as follows: ```python DRProp.status: {(x,): "PASS" if x else "REJECT" for x in range(100)}, ``` **Exercise B Partial Solution**. For `DRProp.status`'s declaration you could ignore the case $`x=0`$ as follows: ```python DRProp.status: lambda args, actual: "PASS" == actual if args[0] else True, ``` ## Slow and Bad Fibonacci - Another Example Report [This](https://docs.google.com/spreadsheets/d/1ax-jQdYCkKT61Z6SPeGm5BqAMybgkWJa-Dv0yVjgFSA/edit?usp=sharing) is an example of `app_slow_fibonacci.teal`'s Dryrun stats: <img width="1231" alt="image" src="https://user-images.githubusercontent.com/291133/158705149-302d755f-afcc-4380-976a-ca14800c138f.png"> A few items to take note of: * $`n`$ is given by **Arg_00** * the app was **REJECT**ed for $`n = 0`$ because `fibonacci(0) == 0` is left at the top of the stack * the app was **REJECT**ed for $`n > 7`$ because of exceeding budget * the app **errored** only for $`n > 16`$ because of exceeding _dynamic_ budget * the **cost** is growing exponentially (poor algorithm design) * the **top of stack** contains `fibonacci(n)` except in the error case * the **final_log** contains `hex(fibonacci(n))` except in the error and reject cases * **max stack height** is $`2n`$ except for $`n=0`$ and the error case * you can see the final values of scratch slots **s@000** and **s@001** which are respectively $`n`$ and `fibonacci(n)` Here's an example of how [invariants can be asserted](https://github.com/algorand/graviton/blob/a8c7eab729a36503948849674ea55995d5fc4ec1/tests/integration/blackbox_test.py#L315) on this function.

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