These days, competition among businesses is fierce. If your company doesn’t deliver high-quality products and services promptly, customers can simply go somewhere else. In order to help your enterprise business stay competitive in the market, it’s important to reduce the time between the development and product release phases. The best way to do this is by strengthening your software development teams and employing reliable technologies to ensure the best quality products and services.
Technology is constantly changing and iterating. Staying current with your tech means you’re staying current with the market. With these five software development trends, you will be able to:
- Speed up your development, testing, and deployment processes.
- Keep a closer line of communication from customers to developers to operations.
- Be confident in your business’s ability to meet customer requirements.
- Better analyze your business requirements.
- Make better decisions for your business.
1. DevOps with continuous delivery
DevOps—a combination of the words “development” and “operations”—is a methodology based on the constant collaboration between development and operations teams in a company. Historically, the development team dealt with writing software while the operations team focused on testing and deploying code to production. The process of testing and deploying code usually took a lot of time, resulting in fewer code releases. With DevOps, the development and operation teams collaborate closely and deploy code daily through the use of automation.
DevOps is an advancement of an Agile methodology. This is where software projects are divided into several modules then a continuous iteration of tasks—including requirement gathering, planning, designing, development, and testing—are done concurrently across all of the software modules. Once finished, all the features integrate for final testing. While Agile methodologies bridge the gap between customers and developers by helping to clearly define key requirements and work that must be completed, virtually all testing and development are left until the end of the process.
With DevOps methodologies added on to Agile, that final gap (the one between developers and operations) is bridged. Code that is written by the development team is constantly tested and deployed by the operations team daily instead of all at once when the code is complete. This process, known as continuous delivery, helps ensure that bugs are caught early and software is always “production-ready” at any time. It allows for customer feedback to be cycled in more frequently.
2. Acceptance test-driven development (ATDD)
Acceptance test-driven development (ATDD) involves collaborative discussion between customers, developers, and testers. The main focus is on customer needs which leads to better code and testing.
A regular workflow in ATDD might look like:
- A team accepts a user story.
- That team collaborates with product owners to get additional criteria and information needed to flesh out the details on what is required of the product for the user story to be complete. This is generally called acceptance test criteria.
- The team writes a series of test cases to evaluate whether the acceptance test criteria has passed.
- Before beginning development, the tests are executed on the current product. These tests will (intentionally) fail.
- Developers begin work writing the code that will make the test cases pass.
- The development is complete once all tests for the acceptance test criteria pass.
Acceptance test-driven development is closely related to, yet different from, test-driven development (TDD). While both ATDD and TDD focus on early execution of test cases before coding, ATDD differs in that it is less focused on automated code testing and more on fulfilling business requirements. ATDD is beneficial to businesses because it encourages teams to view success as fulfilling customer’s requirements more than just getting an automated test to pass.
Since the code is constantly tested for requirement acceptance, bugs and issues are resolved faster which helps prevent bigger problems down the road. Plus, with ATDD there is better collaboration among business analysts, developers, product owners, and testers throughout the entire development process.
3. Container-based applications
Software applications typically have a hefty combination of configurations, dependencies, and code that are all unique to that particular project. This can get messy for large teams of developers working on many projects. Trying to host multiple repositories of code and configurations often leads to the “well, it works on my computer!” problem.
Container-based applications fix all of that by providing a unified way to logically pack each app’s configurations, code, and dependencies all into a single object. When a developer wants to work on that project, they run the container with everything the app needs to run. Then, when they are done, they switch containers.
This process significantly speeds up development. When engineers don’t have to worry about configuring and debugging their environments, they can focus on the actual coding at hand.
Containerization also makes deployment easy since it hides implementation details and only shows relevant data to whatever platform it is running on (public cloud, private data center, personal laptop, etc). This means that operation teams can focus specifically on deployment details and not on irrelevant application details.
4. Predictive analytics with big data
Back in 2017, the number of devices connected to the internet was already at over 7 billion. That number has only grown, particularly with the rise of the internet of things (IoT). Every new device that is connected to the internet provides large amounts of data, ripe for analysis.
Successful enterprises have learned to leverage this data using predictive analytics to draw insight into growth possibilities and potential risks for their business. Predictive analytics uses a combination of statistical algorithms and artificial intelligence (AI) to forecast the future. It can help your business by:
- Improving decision-making: Decisions based on data are always better than decisions not based on data, at least in business. Predictive analytics uses big data—complex and voluminous data—to identify the best practices for your business.
- Promoting better customer relationships: Big data and predictive analytics can be used to predict customer requirements before they even know what they want. These processes can also be used to analyze and predict market behavior.
- Increasing profitability: Predictive analytics can be leveraged to improve marketing promotions, winning you more customers and sales.
Here are the top five predictive analytics software that you can use for your business:
- Oracle Crystal Ball
- Microsoft R Open
- Microsoft Azure Machine Learning Studio
- IBM SPSS Predictive Analytics Enterprise
5. Service virtualization
Testing before development was finished used to be impossible since the test environment lacked vital dependencies—such as key parts of the unfinished code. Service virtualization, the practice of mocking up specific components to imitate expected behavior, makes testing unfinished code possible.
Using service virtualization, DevOps teams can implement virtual services in place of production services. This enables frequent and comprehensive testing, even when key components are missing from the system architecture, Since sections of the product can be tested as they come out, the speed of development and delivery can be significantly improved.
This is especially useful for large and complex applications. If all testing is left until the end, there’s a big chance that there will be deeply rooted bugs that will take ages to fix. When sections of large projects are tested as they come out, issues are caught and iterated on early in the process, creating a smoother development process.