Fully Automated Development: Where We Are Today and Where We’re Headed

For years now, teams have been striving to automate individual development tasks in order to achieve faster development while simultaneously incorporating quality into every step. If your company is not currently automating at least some of your processes, right now is the perfect time to start!

What is Automation?

To put it simply, automation refers to the technique of making a process, apparatus, or system operate automatically-without the necessity of manual, human interaction. In modern development, automations democratize the development process so that users can build new applications quickly, and with ease, so that they can focus their time on more valuable work.

Here are some of the most commonly used automation tools to assist developers:

• Storybook — In our opinion,Storybook is the best tool for maintaining a visual component library. Teams can use it to collaborate, view components, and even select specific components for applications. They also have a visual validation testingfeature that helps users perform visual regression on all parts of a design, making it easier to catch any bugs prior to launch.
• Jest — Jest is a JavaScript testing framework designed to ensure the correctness of any JavaScript codebase. It allows you to write tests with an approachable, familiar, and feature-rich API that gives you results quickly.
• If you’re not a huge fan of end-to-end testing, Cypress makes setting up, writing, running, and debugging tests easy. Writing end-to-end tests requires a lot of different tools to work together, but with Cypress you get multiple tools all wrapped up in one.

If you’re looking to automate your end-to-end testing process, LambdaTest, TestProject, Katalon Studio, and Qualibrate are some additional options that we highly recommend!

SDLC Automation

There is an abundance of tools available on the market today to automate SDLC (Software Development Life Cycle) depending on the nature of your product and where you are in the cycle. Although there are many to choose from, here are some of our favorites for each step in the SDLC process:

Planning & Defining Requirements

• For the planning phase of the development process, we recommend Jama because it’s an easy to adopt requirements management solution.
• To help you and your team configure the nature of the problem you’re looking to solve and the solution, we highly recommend Confluence and IBM Rational DOORS Next Generation.

System Analysis

Design

• When converting logical design into system design that meets the functionality requirements of your project, give Lucidchart a try! It helps you create powerful visuals to understand the teams, information, and processes that drive better decisions in every area of your business.

Development

• To help your team build and code your solution, we recommend GitHub, but QMetry and Jira are fantastic alternatives.

Testing

• When you start testing your product or solution for defects or drawbacks, Katalon is an all-in-one solution that can help in this process. With that being said, TestProject, Selenium, and Junit are some great alternatives.

Implementation

• Once your system has been put into production, it’s time to ensure that what you’re creating works across multiple platforms. To test your solution in different environments, we recommend either Jira, Bamboo, or Jenkins.

Deciding Which Phase to Automate

As you can see from the previous section, automation can be implemented at any stage of your SDLC. When deciding which phase to automate, pay close attention to any tasks that you’re doing repetitively-these are ideal for automation!

That being said, here are some of the tasks that are automated today in multiple environments:

Automated Testing/QA

Smoke Testing, Unit Testing, Integration Testing, Regression Testing, Data Driven Testing, and Functional Testing are the main types of testing.

Automating testing/QA is the most recommended option by tech experts. In SDLC, automating the testing process at all levels (unit testing) gives teams more confidence and frees them up to focus on adding on new features.

Traditionally, testing was done manually with developers fixing bugs reported by customers on a case by case basis, and on occasion, they’d create test scripts to automate the repetitive testing task. Fully automated testing, on the other hand, saves a developer’s time by creating test bots which let them do the work for them.

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Automated Deployment

Automating this process saves developers from searching for a single file within a large number of configuration files when a server is updated. Once automated, every server possesses the same configuration-this saves developers time by eliminating conflicts.

• Repeatability: Once implemented, it’s a relatively straightforward process to reuse automations for other use cases, or even other projects.
• Reliability: Automated scripts works faster and reduce the risk of human error.
• Efficiency: If tasks are automated properly, then they can work and generate results faster which boosts productivity.
• Testing: Project execution is more successful when automated scripts undergo testing for each development phase.
• Leverage: Teams can focus on tasks that bring more value to the end product rather than worrying about infrastructure issues.

Strengths of Automation

When implemented properly, automation has the following advantages:

• Continuous Integration — This strategy uses the automated process to check, test, and verify code located in repository. Code quality can be tested, and syntax errors are located more easily. , , and are popular tools developers use to perform this step.
• Infrastructure as Code (IaC) — Automating with IaC means that developers are not required to manually provision and manage servers, operating systems, storage, and other infrastructure components each time they develop or deploy an application. , , and are some tools that help developers perform this step.
• Code Testing — Developers and QAs generally share the task of code testing, and it is considered a best practice to do integration and unit testing through CI prior to sending the build to your QA for black box testing. This helps you deploy both on time and within your budget.

Steps for Automated Development

Three basic steps can help while automating the development process:

Manual vs. Automated Testing

To recap what we’ve learned so far-automated testing is generally preferred due to the time consuming nature of manual testing, and those testing tasks that are repetitive are automated with the help of scripts. Writing test scripts can be challenging though, so tools like and Ranorex Studio help teams immensely.

While manual testing has its own implications, it’s the type of testing we want to achieve that really determines whether or not a task should be done manually or automated.

A manual approach is best suited for usability, exploratory, and ad-hoc based testing. On the other hand, automation is considered the best option for regression, load, and performance testing. When using automated testing, select tools capable of performing extensive tests, easily debugging automation scripts, recognizing objects in a development environment, and minimizing your costs.

Automating Software Development with Deep Learning

To achieve full automation, trends are heading in the direction of using gradient based approaches with deep learning. Here are some of the emerging tools for automating SDLC stages:

• GPT-3 can help in testing code generation by automatically developing test scripts from data.
• GPT-3 has implications in forming test scripts and test cases working on a Prompt, Example, and Output model. An engineer can provide a code example in a language he wants his data to be converted to by GPT-3, and GPT-3 will provide code as output for the given prompt.
• It can also assist in test framework generation. A developer can input data in GPT-3 for any application they’d like to test and specify the language and application for the test framework, and then GPT-3 will generate that framework within minutes.

More use cases of GPT-3 in generating code, web layouts, etc. can be found here.

GPT-3/NLP (Converting Plain Language Prompts into Code)

Generative Pre-trained Transformer 3 (commonly referred to as GPT-3) is an OpenAI model used for natural language processing. Using 175 billion parameters, GPT-3 is capable of generating text and code and is even capable of performing translation as well! We’re still feeling out the full capabilities of this kind of technology, but it has some noticeable features in the following test automation techniques:

CrowdBotics Figma Integration (Design to Code)

Many designers don’t develop apps, and most developers don’t design them. These two teams use different tools, follow different workflows, and often report to different managers. To turn design specs into a functioning UI, a designer needs to hand visual design files to a developer and provide constant guidance as the developer converts those designs into code.

This back-and-forth, known as the design-to-development handoff, creates friction before development even begins and can cause repeated delays downstream whenever design changes are implemented.

But thanks to the new Crowdbotics Figma integration, those struggles are a thing of the past.

By simply pasting your Figma share link into Crowdbotics, you’ll instantly convert your design files into real, cross-platform React Native code running in a live emulator. Developers can directly modify these screens inside an accompanying code editor and save their changes to a linked GitHub repository with a single click.

In other words, no more tedious documentation, handoff meetings, or miscommunications-just paste a link to turn your product specs into development-ready code.

Automated Assembly of Full App Templates

App templates are increasing in popularity because they allow someone to build an app in less time and with fewer costs than traditional app development. Platforms that offer this as an alternative (like Crowdbotics) are considered low code or no code solutions, and using one of them to build your app can save your organization weeks if not months worth of tedious coding. It can also help you get to market faster!

Which Tasks are Considered the Hardest to Automate?

While it’s generally best to automate as many steps in your development process as possible, there are a handful of cases where it might not be prudent to do so, at least initially. For example, test cases where requirements fluctuate with time are often incredibly challenging to automate properly and should be one of the last automations you consider implementing. Tasks in early stage development that you expect will undergo additional changes may also not be a viable option for automation. Instead, invest your time automating features that you know will have longevity and that are less complex to start.

There is one caveat to the complexity rule though, and that’s when it comes to security. Automating security for web applications and APIs is critical because they can face a significant number of surface attacks, and having an automation in place can save you from expensive manual security maintenance.

The Future of Automated Development

The future of automation looks incredibly promising, and we expect that it will bring about an increase in serverless adoption, AI and SDLC automation, and low code development.

Research suggests that 10–15% of companies have already implemented serverless architecture for their application development. Our projections suggest that this number will increase significantly over the next few years, and Lambda by Amazon and Event Grids by Microsoft have both been introduced as serverless concepts that allow teams to focus more on coding and less on the infrastructure their applications run on.

With the kind of rapid development we’ve seen in AI systems, automation is truly at a tipping point, and today, AI can perform a number of functions without much human intervention. Automated technologies are not only executing repetitive tasks, they’re also augmenting workforce capabilities significantly. Multiple industries, from manufacturing to banking, are adopting automation in order to drive productivity, safety, profitability, and quality. In the future, powerful AI systems will be programmed to understand past user behavior and automatically foresee any future requirements.

SDLC automation is becoming increasingly more popular. Today, Agile teams are using SDLC automation to generate user stories, and IT operations are incorporating SDLC automation in the management and configuration of their infrastructure. Over time, we expect to see more industries implementing SDLC automation into their processes.

Finally, if you ask any IT analyst or engineer what the future holds for application development, they’ll likely tell you that low code is taking the market by storm. Gartner predicts that low code development solutions will account for 65% of all app development by 2024, and this Forrester report reveals that the industry is expected to grow to $21.2 billion by 2022.

In today’s rapidly evolving app development world, low code and no code platforms are offering the fastest and most agile solution available for companies looking to build and innovate new and existing applications.

If you’re new to development automation and would like some additional support, Crowdbotics offers managed app development services to help you go from idea to launch as quickly and efficiently as possible. Get in touch with us today for a comprehensive assessment of your organization’s development automation needs.

Originally published on the Crowdbotics Blog on May 11, 2021.