## Automation QA's tests before having programming knowledge

## More focus on test design and application understanding

In the world of automation QA, when we first start, and we don't know much about programming, we mainly concentrate on really understanding the application as if we were the customer. This helps us create tests that act just like a regular person using the software, making sure we check it thoroughly in all kinds of situations. Of course, even after we learn programming, our dedication to understanding the application remains. But here's the important part: knowing how to code helps us do an even better job. We can create more advanced tests and make our testing methods more precise and efficient. This way, we keep testing the application like a real user while using our coding skills to do our job correctly.

### Preference for user-friendly automation tools

Additionally, testers with limited programming skills often prefer to stick to user-friendly automation tools with more intuitive interfaces that would require a minimal amount of coding, allowing them to create automated tests more quickly. One advantage here is that they can begin without facing the challenges related to programming languages, which is quite a time saver and lets them dive more into their field in detail.

### Preference for simple testing apps that offer test scripts

QAs who test software without knowing how to program often rely on online testing tools that record and save actions. After a test is registered, it can usually be modified so it can change what you do during different steps and also change the expected result. Such tools copy your interactions and record them in a way that's repeatable and faster than manual work. Later, they can modify them as you please, or you can create your own from existing samples. While they don't write code, we still make the most of what's already available to test software effectively. It's good to note, though, that generally, such online testing tools don't go beyond testing websites only.

### Reliance on regular collaboration with programmers

Given their limited programming expertise, testers often must collaborate more closely with programmers for help. This collaboration becomes essential for addressing script issues and inconsistencies and also handling more advanced automation tasks. 

Working closely with developers can be a valuable learning experience. It ensures that even if you lack programming skills, with enough effort - you can meet development standards and nurture devskills over time. Not to mention that when you start performing your tasks individually - programmers on their own don't have to worry about finding time to fix what you write. They focus on their part, you focus on yours, and productivity increases immeasurably. 

## Benefits of having programming knowledge

### Enhanced test-building skills

Having programming skills equips an automation tester with the ability to build sophisticated and comprehensive test scripts. They can create test scenarios that cover a more comprehensive range of inputs and conditions, resulting in more thorough testing. Additionally, the possession of programming skills allows testers to design tests that are reusable and maintainable, reducing the effort and time in the long run.

### Better debugging process

One aspect of testing, where the benefits of possessing programming skills are sure to make quite an impact, is debugging. Why? Knowing how to read code, analyze your test script errors, and identify issues with your strength saves your time, saves the programmer's time, and last but not least - boosts self-confidence. When a test fails, a QA with the above-mentioned "powers" can trace through the code and successfully pinpoint the problem's root cause and fix it efficiently. Self-education in debugging proves to be quite a utility to master.

Better data handling and validation of the software's behavior
Proficiency in programming enables automation testers to handle and manipulate testing data more effectively. How exactly? For instance, they can create scripts that simulate various users and check if a system differentiates between a new user, a premium user, and an admin. Or let's say they have to test an e-commerce website during which every time a user adds three different products to the cart - the price should be calculated correctly. Possessing skills to perform complex data validations and ensuring that the software behaves as expected under other conditions is an absolute must if we're talking about the reliability of test results.

### More effective collaboration with developers

As we can imagine, programming skills can be the way to "bridge the gap" between an automation QA and the developer for natural reasons, from engaging in relevant product discussions to understanding the code and communicating testing requirements clearly. All these mutual activities signify a more explicit workflow that will result in improved collaboration and communication. And on its own, this will reflect positively on every aspect of the working process.

## How the perspective changes with programming knowledge

### Writing less code that would do more

Acquiring programming knowledge transforms an automation QA's approach to test script development. The QA no longer needs to write long and repetitive code for every single test scenario. Instead, the focus shifts to designing functions and test suites that are somewhat optimized and efficient. Also, this will highlight a level of programming "hygiene" (writing less code while achieving broader test coverage), making the automation process better in the big picture.

### Designing more reusable tests/code

Programming knowledge enables automation QAs to design tests and code that are highly reusable. They can create scripts, functions, constants, and custom commands that can be applied to various test scenarios. It is making the test maintenance easier by far. Such focus on the reusability of the tests accelerates the test development process and proves helpful with fast adaptation of eventual testing of new add-ons to the project.
How programming knowledge boosts the prowess of automation QA

### Enhanced test-building techniques

With some programming skills, we QAs can apply applicable practices to our tests. For example, learning a little bit of HTML, CSS, and DOM tree basics is a good start that can help us to choose better elements to interact with. This means that the more you master them, the more logical assertions, combined with the custom mentioned above commands and costs, you will create. Another example from Cypress is including a well-built beforeEach hook that has saved me from writing the same things for more than 100 tests when I can write it as a precondition only once.

### Test efficiency improves

I've discovered that the power of thinking beyond the basics puts a green flag in your garden. From personal experience, I can tell you that testing a payment system does not include only filling in a card number and clicking the submit button. It's also crucial to not forget to test that you won't be able to pay with a non-active card, an expired one, or an empty one. So far, my programming skills allow me to design and execute tests beyond the standard user flow, ensuring that the payment system is thoroughly and creatively tested. 

### Error handling and reporting improve

Testers with an understanding of programming language can handle errors in their code better. As I already started walking that road, I can confirm that knowing to read your own code is a time saver for both you and the developer. And effective error handling, together with clear error reporting, is essential. A responsible QA also understands the importance of reporting errors that appear in the console during testing, even if they're not immediately visible. As we gain knowledge of programming, we start "speaking the same languages" with developers, so we learn to include the proper terminology like "validation," "verification," etc. 

### Test framework flexibility improves

Knowing more about programming helps us save some time in another way as well. Let's say you have to test a sports betting app on Android and iPhone - the user interface might vary with login buttons, input fields, etc., but that doesn't mean it's a bug. So, instead of writing a long script for two separate tests - you can use coding to adapt the script automatically to the respective system being tested. I already applied it to my approach to work, and it aged pretty well, saving me lots of writing.

## Can we, automation QAs, survive without programming?

Programming skills in automation testing are not a must. But when you want to raise the bar and perform different complex scenarios with your testing tool, you simply need them. Of course, you can always stick to manual work for the more complex tasks. Still, if you put effort and brainwork into learning a little programming and reach a level where no scenario is beyond your expertise to write - that's when you win at testing. Think of programming for automation, like having your own beach house. You can still live without a beach house, but having it kind of changes the horizon, doesn't it? So I suggest you consider building your own!

The Lexis' overview of how an automation QA experiences the testing process without any programming capabilities, why they are essential to acquire, how they change the QA's perspective, and whether testers can make ends meet without them.

pexels-photo-4974922

Programming in Аutomation: Why is it as Еssential as having a Compass in the Wilderness?

Step into the realm of AI innovations once again! Previously, Lexis Solutions introduced you to some of the top [AI-powered tools](https://www.lexis.solutions/blog/ai's-toolbox-the-instruments-driving-innovation-part-one) for videos, content, and images. In this second chapter, get ready to hit all the right notes with a collection of tools for producing the ultimate music, audio, and design masterpieces.

## Music

### AIVA

[AIVA](https://www.aiva.ai/) is the AI-powered composer that crafts unique soundtracks for your every need. Whether for a gaming adventure, a marketing masterpiece, or a special event, this musical prodigy will generate the perfect score. Select a predefined style or upload your sound to guide AIVA's creative flow. 

### Supertone

Get ready to be amazed by the audio alchemy of [Supertone](https://supertone.ai/), the innovative Korean studio that blends art and technology to create music, voices, and more! They're the masters of speech enhancement, song revival, and dubbing - and if you want to hear ABBA singing in Korean, they've got you covered.

### Magenta

[Magenta Studio](https://magenta.tensorflow.org/studio/) comprises a set of music plugins that are constructed upon Magenta's open-source tools and models. Leveraging advanced machine learning methods for musical composition, Magenta is accessible both as an independent application and as an integrated plugin for Ableton Live.

### Orb Producer Suite

Assisting producers in crafting melodies, basslines, and wavetable synthesizer sounds, [Orb Producer](https://www.orbplugins.com/orb-producer-suite/) employs cutting-edge technology to generate limitless musical patterns and loops. These serve as inspirational foundations for musicians to build their creative ideas upon.

### MuseNet

Under the ownership and operation of OpenAI, [MuseNet](https://openai.com/blog/musenet/) can produce songs featuring a diverse range of up to 10 instruments spanning across 15 distinct styles. While MuseNet offers a rich array of AI-generated music for enjoyment, it must provide the functionality to create original music independently.

## Audio

### Cleanvoice 

Introducing [Cleanvoic](https://cleanvoice.ai/)e, the AI-powered solution tailored to enhance your podcasts or audio recordings by eliminating filler sounds, stutters, and unwanted mouth noises. You can easily upload your audio, allow the AI to perform its cleaning magic, and obtain the refined outcome effortlessly. Experience the benefits firsthand with a complimentary 30-minute trial, allowing you to explore its functionality and witness its effectiveness.

### FakeYou

[FakeYou](https://fakeyou.com/) is a text-to-speech generator offering diverse options featuring hundreds of voices. You can effortlessly select any voice and have it articulate the text you input. Whether it's the voice of your favorite actor, singer, or even your cherished animated character, the choices are abundant. It's a creative and entertaining means to prank a friend playfully, wouldn't you agree? And the best part? It's free.

### LALAL.AI

[LALAL.AI](https://www.lalal.ai/) emerges as an advanced AI audio tool, harnessing the capabilities of machine learning algorithms to swiftly and accurately accomplish this task. This exceptional AI system empowers you to effectively extract voices or instruments from audio files while preserving the original quality. By offering a complimentary 10-minute trial period, you can experience its prowess firsthand. Furthermore, another 5 minutes can be unlocked by simply following them on Reddit.

### Uberduck

[Uberduck](https://www.uberduck.ai/)'s text-to-speech technology boasts an impressive selection of over 5,000 expressive voices at your disposal, ready to enhance your voiceovers. This platform goes further by enabling you to replicate your voice. Whether for entertainment – like creating singing or rapping voices – or for professional endeavors, Uberduck caters to both. It's a dynamic tool offering versatile applications, including the creative and the commercial. With subscription plans commencing at just $10 per month, it's an accessible gateway to a world of vocal possibilities.

## Design

### Design Beast

[Design Beast](https://www.designbeastapp.com/Dashboard/Account/Login) is a comprehensive AI-powered design platform that unites many design functionalities into a single hub. Encompassing the Mockup Engine, Logo Factory, Image Editor, Object Remover, Background Remover, and Pixel Perfect tools, this platform emerges as a valuable solution, particularly for those who may not have a design background. Its vast library of pre-designed templates caters to various applications, offering a user-friendly experience. With pricing starting at an affordable one-time payment of $67, Design Beast provides a cost-effective avenue into the world of versatile and professional designs.

### Beautiful.ai

[Beautiful.ai](https://www.beautiful.ai/) boasts an array of customizable templates, a vast selection of stock photos and videos, audio track uploads, and collaborative functionalities to enhance your workflow. Subscription plans commence at $12/month, while the option to purchase individual projects is also available at $45 per project.

### Uizard

[Uizard](https://uizard.io/) can convert screenshots into editable designs and can even automatically transform your sketches into polished designs. Whether your focus is crafting landing pages, designing apps, or creating wireframes, Uizard offers an intuitive and efficient solution with its time-saving features. Uizard's innovative functionality comprehensively addresses your design needs.

### Tome

[Tome](https://tome.app/) crafts incredible presentations as a seamless experience. Enter your prompts into the command bar, and watch as Tome creates mesmerizing slideshows. Whether you're turning strategy papers, creative briefs, websites, or extensive content into engaging presentations, Tome has you covered.

Welcome back to the AI-powered world with Lexis Solutions! 

AI-instruments-2

Innovating with AI instruments - Part Two

As an engineer at Lexis, I would like to share some helpful bits of my experience solving complex business intelligence problems. Recently, I was assigned a rather daunting task of extracting financial information from over half a million documents of financial reports from various companies. This task, though challenging, allowed me to delve deeper into the applications of the more recent AI advances, mainly through OpenAI embeddings and vector databases, for business intelligence problems. How, you ask? Let's take a journey through the process in my comprehensive case study.

## The problem

We were tasked to extract key-value pairs from inconsistent structure and text from business documents. Therefore, we couldn't quickly develop an algorithm to solve the problem and had to implement a solution to leverage the new advances in LLM. We used a cloud solution to train an AI model to extract the key-value pairs. However, it was still pretty challenging to deal with the sheer volume of documents, as not all documents held relevant information, and if we had to process each page, not only would it take more time, but it would be much more expensive. Manually sifting through these documents would be even more time-consuming and economically unviable. The solution? A classification program that combines OpenAI embeddings and ChromaDB vector databases, using Pytesseract for Optical Character Recognition (OCR). 

Before we delve into how these technologies helped me overcome the challenge, let's understand what vector embeddings and vector databases are. 

### Vector embeddings

In a high-dimensional space, vector embeddings are mathematical representations of objects, such as words or documents, capturing semantic meanings based on their context. These vectors provide a way for algorithms to understand the content and context of documents. 

### Vector databases

Vector databases, on the other hand, are databases designed to store and query these vector embeddings efficiently. They enable us to perform similarity search at scale, which is critical in tasks such as semantic search, relatedness search, or, in our example - classification. 

### Implementation

This is where OpenAI and ChromaDB came into the picture. OpenAI provides a powerful tool to generate embeddings for our documents, while ChromaDB allows us to store and query these embeddings efficiently. By leveraging the power of vector embeddings and vector databases, we can classify documents based on how close their vector representations are, which will help us identify relevant pages in a document.

First, I split the PDF documents into individual pages. For each page, I created a MySQL record, applied OCR using Pytesseract, and generated an embedding using OpenAI. Using ChromaDB, I queried the page embedding with the embeddings of already classified pages (that I organized personally) stored in the vector database. The result of the query would give me the most related page to the one I am querying, and I would classify it with the same type, storing the type back in the database.

This process enabled me to filter out irrelevant pages, leaving only the ones that needed further processing. This streamlined the process and reduced the cost by about 80% of the original estimate.

### A basic example

I will give you a simple example to showcase an implementation of a Python program that does a similar job. To implement this solution, you'll need to create a MySQL database that contains the information for each image and a ChromaDB vector database, which you will use to query the vector embeddings.

Here's a simple schema for the MySQL database:

```sql
CREATE TABLE documents (
    id BIGINT UNSIGNED PRIMARY KEY,
    name VARCHAR(255),
    file_path VARCHAR(255), # path of the document in the filesystem
);

CREATE TABLE document_pages (
    id BIGINT UNSIGNED PRIMARY KEY,
    document_id BIGINT UNSIGNED,
    type ENUM(‘invoice’, 'balance_sheet’, ‘income_sheet’, ‘none), # here we store the type of the image once we classify it
    file_path VARCHAR(255),
    FOREIGN KEY (document_id) REFERENCES documents(id)
);
```

We need to manually classify at least a couple hundred images and save them in a folder. The assigned type of each image should be contained in the name of the image, separated by a dash, for example: ```'[name]-invoice.jpg'``` or ```'[name]-none.jpg'```. Then, we will embed these images into a ChromaDB vector database using the following example:

```python
import chromadb
import glob
import os
import pytesseract as pt
from openai import OpenAI
import uuid

openai = OpenAI('your-api-key')

client = chromadb.PersistentClient(path="chromadb")

# create the classification chromadb collection
classification_collection = client.get_or_create_collection(name="classification_collection",  metadata={"hnsw:space": "cosine"})

classified_images = glob.glob("classified_images/*.jpg") # the location of the folder containing the manually classified images

for classified_image in classified_images:
    # get base name of image
    image_type = os.path.basename(classified_image).split(".")[0].split('-')[1]

    # get the ocr text
    ocr_text = pt.image_to_string(classified_image)

    embedding = openai.embeddings.create(
        input=ocr_text.lower(),
        model="text-embedding-ada-002"
    ).data[0].embedding

    # embed the image in the chromadb database
    classification_collection.add(
        embeddings=embedding,
        metadatas={'type': classified_image.type},
        ids = str(uuid.uuid4())
    )

```
After creating the classification database, you can use the following Python code to perform OCR, generate embeddings, and classify the pages of a given document page:

```python
from models import DocumentPage
import chromadb
import pytesseract as pt
from openai import OpenAI

openai = OpenAI('your-api-key')

client = chromadb.PersistentClient(path="chromadb")

# create the classification chromadb collection
classification_collection = client.get__collection(name="classification_collection")

for DocumentPage in DocumentPage.all():
    # get the ocr text
    ocr_text = pt.image_to_string(DocumentPage.file_path)

    embedding = openai.embeddings.create(
        input=ocr_text.lower(),
        model="text-embedding-ada-002"
    ).data[0].embedding

    # embed the image in the chromadb database
    query = classification_collection.query(
        query_embeddings=embedding,
        n_results=1
    )

    DocumentPage.type = query['metadatas'][0][0]['type']

    DocumentPage.save()
```

## Conclusion

This simple example should illustrate the process of classifying images using the latest trends in LLMs and vector databases. The synergy of OpenAI embeddings and ChromaDB vector databases has revolutionized our approach to document classification, making it more efficient and cost-effective. 

The article has given you a glimpse into the power of AI tools in business intelligence. As we continue to explore and experiment, we are excited about the endless possibilities that AI holds for us, and we will keep sharing our insights about how we can use them in real-world scenarios.


Uncover how OpenAI and ChromaDB revolutionized financial data extraction, streamlining document classification for efficiency.

Unleashing the Power of AI in Business Intelligence: Document Classification using OpenAI and ChromaDB

## Our work

[Meet.direct](https://lexis-solutions.com/index.php/portfolio/meet-direct/) started with a Web app and a native iOS app written on Swift. The iOS app validated both the business case and the business model and the platform was getting traction as the number of users rose every day. At this point, our team took responsibility for the [Web app](https://lexis-solutions.com/index.php/2020/07/26/meet-direct-web/).

Not long afterward there came a demand from the Android users -- they also wanted a native app. The team decided on re-writing everything on React Native. This was the only reasonable approach as otherwise, the client would have to hire yet another dev team.

React Native makes it easy to simultaneously develop for both Android and iOS, and we effortlessly replaced the Swift app with minimal cost. The benefits for the client were tangible:

-   two mobile applications that looked and felt truly "native"

-   the programming language among the web & mobile apps became just one

-   the development team was one

-   the codebase was being shared

React Native allowed for a straightforward deployment on Google Play & Apple Store.

## Results

As a result, every new feature and hotfix would be implemented in a single place, reducing time to market and expenses more than twice.

Meet.Direct is a social platform that connects businesses and people. It provides businesses more exposure and statistics about customers and discounts to private users.

Replacing Swift with React Native

Our work

Results

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