Continuous Delivery: Updates by Automating Quality Assurance

Continuous Delivery: Updates by Automating Quality Assurance

Continuous delivery has become one of the foremost talked-about development practices within the last decade. The idea is to release software updates as soon as they’re ready. While frequent updates won’t seem impossible, advances in automation engineering have made it possible.

Let’s take a glance at continuous delivery and integration from a technical, operational, and business standpoint.

Software Updates Became More Frequent as a Result of the Evolution of Development Practices

Waterfall, agile, and continuous delivery are the three main approaches to software development. In software engineering, all three are used. However, we will consider these three to own evolved into each other as methods and tools have improved over time.

Waterfall: All stages of development, from getting to production deployment and maintenance, are interconnected. Waterfall has proven ineffective for products that need frequent updates. Due to the long development cycles, it hasn’t been possible to retort quickly to customer feedback. Today, the waterfall is most ordinarily used for brief, fixed-cost projects that may not have enough time to age before release.

Agile: Agile may be a development philosophy that supports iterative development. There is a spread of agile methods, but the bulk of them involve short engineering cycles that cover all of the key stages: planning, development, testing, and deployment. Each cycle takes one to 2 weeks to complete. Agile is predicated on the thought of shipping a product as soon as possible and incrementally updating it to support customer feedback. Agile methods are still widely utilized in modern software development because they allow products to adapt to changing market and customer demands.

Continuous Delivery: Continuous delivery is often interpreted in an exceeding form of ways. We see it as a natural progression of agile principles. The tactic doesn’t necessitate frequent release iterations and easily allows new code to be committed when it’s ready. Developers can update the merchandise multiple times per day in this way, providing users with continuous value. This is often accomplished through extensive testing and deployment automation.

Continuous Delivery Encompasses Business Value

Continuous delivery (CD) relies on the concept of getting any update ready for release at any time. This is often what distinguishes the practice from traditional Agile methods like Scrum, where iterations last 1-2 weeks, and every new feature may wait before being released in production.

According to Mark Warren, European Marketing Director at Perforce, 65 percent of software developers, managers, and executives practice continuous delivery in their companies, while 28% use it on some projects. Eighty percent of these polled said they were functioning on software-as-a-service (SaaS) solutions.

Continuous delivery adoption is probably going to be more widespread now, as more software providers adopt the SaaS model. Cloud-based services are the most field for practice adoption because these products can receive customer feedback quickly and respond with fixes and updates almost as quickly.

So, what are a number of the foremost compelling reasons to think about CD?

The proportion of your time to value: the speed of development is high, and therefore the time between proposing a replacement feature and its implementation is brief. During this case, the so-called “integration hell” is avoided. The team devotes less time to debugging and more to making new products. This also means a shorter feedback circuit or the time between user interaction and new information.

The highest level of automation: Continuous delivery is just possible with automated testing and deployment stages. As a result, the sole time-consuming aspect is programming. In a very moment, we’ll get into technical details.

High-quality and low-risk products: the quantity of possible errors and bugs is significantly reduced because every update goes through several stages of automated verification before being deployed.

Data-driven decision-making: The strategy also allows for continuous monitoring of development data. You gain visibility into your processes and, as a result, you gain insights into the way to improve your current workflow and eliminate engineering bottlenecks.

Cost savings: one in all the foremost significant drawbacks of long release cycles is that the increased cost of a blunder as a bug remains in production. If it persists after multiple updates, the price of repairing it escalates exponentially. Continuous integration lowers this cost by catching bugs as soon as they seem.

How to Approach Continuous Delivery: An Adoption Framework

In order to implement the strategy in your development workflow, your software engineering team must follow a group of guidelines that make it possible:

• Following the continual delivery method’s core principles: continuous integration and deployment
• A software engineering infrastructure that unites all aspects of product delivery into one ecosystem
• Code branches are kept to a bare minimum within the project repository.
• Manual tests are outnumbered by automated tests.
• Use of a cloned production environment that closely resembles real-world conditions

These five aspects are more complicated than continuous delivery, but they determine whether your organization is capable of implementing the practice. Let’s take a better observe these requirements.

1. Continuous deployment and integration

Continuous integration (CI) describes how the methodological business principle is implemented on the software engineering level, whereas CD defines the methodological business principle. In other words, it tells the event team the way to do things:

• Every day, developers commit code multiple times.
• Each piece of code is put through a series of automated tests to catch any errors or bugs as soon as possible.
• When a difficulty is discovered, the event team’s first priority is to mend it.

The ability to deploy updates quickly is most ordinarily related to SaaS models, during which developers have complete control over the merchandise that end-users interact with. If you’ve got client software that’s installed on users’ devices, it’s more common to bundle updates and notify users that some changes are going to be made.

2. Infrastructure for continuous integration

Many CI systems exist that provide software engineering infrastructure for the practice to be implemented. Although custom software for CI workflow may be built, there is a variety of off-the-shelf options available. Cruise Control, Atlassian Bamboo, and Team City are the foremost popular. These systems are essentially testing and development environments that bring the complete development process together. They keep track of recent code commits and enable the creation and integration of automated tests that run whenever new code is formed. What’s the mechanism behind this?

New code monitoring has been implemented: The CI system detects when a brand new piece of code is committed to the repository (code storage).

Packaging and construction: The new code is made automatically and put through unit tests.

Testing that doles out automatically: Packages must pass a series of automated tests to confirm that the new code is functional.

Deployment: The CI system then deploys the update on a production server once the packages are accepted. The system ensures that packages are ready for production deployment whether or not deployment isn’t automated.

Because of the high level of automation, this process can happen several times per day, revealing bugs much earlier than if developers were committing large chunks of code.

3. There’s just one main line of the project within the repository

In layman’s terms, a repository may be a location where development tools are stored and managed. This repository should be the “home” for all written code for a project in continuous integration. Additionally, the repository stores test scripts, third-party libraries, and other development-related items, i.e., everything required for a build.

To reap the complete benefits of such a deployment strategy, the repository’s number of code branches should be kept to a bare minimum. Reckoning on the scope of the update, code branches from multiple developers are accustomed merge into most branches every so often. In CI, the software environment keeps the number of branches to a bare minimum and continuously sends new code to the master branch, ensuring quality assurance on the fly.

The more code branches and versions of a product you have got, the more likely there are to be conflicts.

4. Automated testing

Automated tests are essential for continuous integration. They check to determine if the new code is functioning properly. If it doesn’t, the system shuts down the development. What’s the aim of this strategy? Developers won’t be able to work on the build again until bugs are fixed.

Although complete test automation isn’t required, CI is barely worthwhile if the quantity of automated test cases exceeds the number of manual test cases. Fortunately, CI systems enable the mixing of a good range of automated tests, including smoke testing to work out whether your product will simply launch after updates, as security and performance tests. Furthermore, your QA engineers have the choice of writing these test scripts in one of several programming languages.

5. A just like the assembly environment for testing

The polygon, which represents a testing environment, should be a precise replica of the assembly environment. This suggests you want to run tests with identical databases, infrastructure, patches, network structure, and so on. To place it otherwise, you want to do everything possible to totally comprehend how the assembly version will appear and test it automatically across a spread of browsers and devices.

The Most Significant Obstacles to Adoption

Automation’s Tag

While CI-based development lowers the price of errors and increases productivity, it necessitates a major financial and time commitment, the foremost significant of which is that they must hire and retain QA automation engineers who will incrementally cover your evolving product with automated tests. It can take anywhere from 6 to 18 months to get the groundwork for QA automation if you’re ranging from scratch.

Microservices are a sort of architecture

In a nutshell, the microservices or components architecture could be a software pattern that permits different functional elements to be decoupled and shipped separately. The monolithic architecture, on the opposite hand, is characterized by functional components that are tightly coupled to at least one another, and each change has a bearing on the complete code base.

Continuous delivery isn’t doomed if your wares could be a massive monolith. However, it’ll make life harder for developers working with continuous delivery logic. The monolithic code could also be difficult to understand, making it harder to reconcile the work of multiple development teams, each engaging at their own pace.

Embracing the DevOps Mindset

Without frontline operations workers, your developers are going to be unable to create relevant and user-oriented updates. Close collaboration between software engineers (the dev part) and operations workers (the ops part) is central to the DevOps culture. The latter refers to everyone who is involved in the post-launch phase of a product’s life cycle. Systems engineers, administrators, operations staff, network engineers, security specialists, and other professionals constitute this category. Building a DevOps culture requires these two sides to collaborate as one unit so as to enable a fast electric circuit from users to operations to developers.

Challenges of Integration

This is a minor issue compared to the others, but development teams sometimes have trouble adopting new software, like continuous integration and version control systems, and integrating them into their existing workflows. Fortunately, there are many simple-to-use solutions on the market, like DevOps products or Team City, which will be chosen, to support the preferences of the team, its size, and level of experience.

Extensive Deployment

Even though developers are aware of breaking down their work into stages, it is difficult to take care of such modularity. Builds vary in scope and complexity, and your engineers may lack the flexibility to think and code in a very modular manner. It’ll take a concerted effort to retrain engineers to figure within the new way if they need been build monoliths for years.

Concentrate on the Bugs

Development delays may occur as a result of frequent commits and therefore the need for immediate fixes. If a replacement feature is proposed but fails to pass testing, everyone seems to be focused on fixing the bug, whether or not the feature isn’t a high priority.

The capacity of the Assembly Environment

Many operating systems, browsers and devices are going to be handled by your production environment polygon. It also becomes cost-sensitive to achieve the desired capacity. as an example, running all tests on one CPU takes about 200 work hours for every Firefox browser update. This suggests that you just don’t need to check your product on a laptop, whether or not you are not a Firefox user. it is a good idea to use cloud providers like Amazon Web Services, Google, or Microsoft, which may provide enough computing power to hurry up testing.

The culture within the Workplace

The major barriers to continuous integration adoption, in step with 40% of Cloud Bees’ report respondents, are organizational traditions and also the fear of tangible investments. As previously stated, automation, skilled QA engineers, and changes to a standard workflow structure all necessitate time and financial investments, and a few businesses question whether or not they are worthwhile.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.