To elevate service offerings and remain competitive in the market, most enterprises face the challenge of reducing the duration of the software engineering process, speeding up the time from the development phase to product release. If you want to become more responsive to opportunities in the market, you should strengthen your software development team and use reliable technologies to ensure the quality of your products and services.

We have listed top technology trends that will help you:

  • Analyze your business requirements.
  • Speed up your development, testing, and deployment processes.
  • Make better decisions for your business.

1. DevOps integrating with continuous testing

Businesses are adopting DevOps to accelerate their software delivery process.DevOps is a methodology that emphasizes collaboration between the development and operations team. It shortens the software development life cycle ( development process) by deploying code faster to the production in an automated way. With DevOps, organizations can compete better in the market by serving their customer needs better.

DevOps is an advancement of an agile methodology ( In an agile methodology, the software project is divided into several modules. Then, a continuous iteration of tasks (including requirement gathering, planning, designing, development, and testing) is done concurrently across all the modules of the software. Then, in the end, all the features integrate for final testing.

To know the difference between Agile and DevOps, consider the following:

Agile Vs. DevOps

Focuses on customer feedback, and this, in turn, bridges the gap between developers and customers.
2. It mainly focuses on developing software.
3. In agile, the planned amount of work is completed in one or two week(s) and gets delivered to the customer for the feedback.

  1. Coordinates the developers and IT operations team.
  2. It focuses on developing, testing, implementing and deploying the software in a secure manner.
  3. In DevOps, the code is delivered to production daily.

Before the evolution of agile and DevOps, testing and deployment tasks were carried out individually after the development phase, resulting in delaying software delivery. Whereas, with the standardized production environment and automated deployment of DevOps, an organization can deliver the product and services faster.

Continuous testing in DevOps

Continuous testing is a testing methodology that includes a process of testing (early and regularly) the code snippets written by the development team in a Continuous Delivery Process ( is a process in which the code is regularly developed, tested and deployed to make the software ‘production ready’ at anytime).

Some key points about continuous testing-

It includes continuously analyzing and reviewing the code to improve its quality at every step of the development life cycle.It aims to evaluate business risk coverage. It improves the user experience by eliminating software failure risks.
It seamlessly integrates into the DevOps toolchain.

2. Acceptance Test Driven Development (ATDD)

Acceptance Test Driven Development (ATDD) involves collaborative discussion with different team members, such as business customers, developers, and testers. It mainly focuses on customer needs, which leads to better coding and testing.

Work process in ATDD

  1. Project team takes user story.
  2. Then, they collaborate with product owners to elaborate the user story by adding ( (acceptance test is used to determine if the software system has met the requirement specifications).
  3. The team writes test cases to test each acceptance criterion.
  4. Before the development begins, test cases are executed, which intentionally causes the test cases to fail.
  5. Then developers write the code to pass these test cases.
  6. After that, test cases are again executed, but this time, they are expected to pass the acceptance test.

Acceptance Test Driven Development is closely related, yet different from Test Driven Development (TDD) (

Here are the key similarity and difference between ATDD and TDD:


  • Both ATDD and TDD involve the early execution of test cases before the coding begins.


  • While ATDD is used to test the software according to requirement specifications of business users, the TDD mainly focuses on automating the unit tests.


  1. It enables the project team to think from the customer’s perspective, resulting in the development of software that meets the user’s requirement.
  2. With a better collaboration among business analysts, developers, product owners, and testers throughout the development process, it’s easy to analyze the software requirement from a user’s point of view.
  3. It resolves bugs and issues faster, as the test cases are executed multiple times to ensure the new code is fulfilling the user’s needs.

3. Container-based application

In recent years, containerization is revolutionizing cloud-based architecture, enabling enterprises to design and deploy software quickly and efficiently.

What is containerization?

Containerization provides a unified way to logically pack your application’s configurations, code, and dependencies into a single object. It makes the deployment easy, as the container-based application can be abstracted (meaning they hide the implementation details on a design level and show only relevant details) from the platform in which they run. With this, the IT operations team can focus on deployment tasks without worrying about the application details.

Some key points about containerization:

It makes application deployment easy, as it is independent of its target environment (such as public cloud, private data center, employee’s personal laptop,etc.).

  • With its logical packing mechanism, it enables developers to focus on application dependencies and logic.

Why container-based applications?

  1. Containers include software dependencies required by the application (such as software libraries, versions of programming languages, etc.). It makes the application robust and flexible for any environment. Developers and IT operations teams will spend less time analyzing and debugging differences in the software environment.
  2. Container-based applications can run virtually anywhere, making overall management of the applications easier.
  3. It eases every task in the software development life cycle (including designing, developing, testing, and deploying).

4. Predictive analytics based on big data

Enterprises leverage predictive analytics technology to draw insight into growth possibilities and potential risks for their businesses. Predictive analytics use statistical algorithms and artificial intelligence to forecast the future of your business. It will help you with the following:

  • It facilitates better decision-making, as it uses big data ( and voluminous data used to address business problem) to identify the best practices for your business.
  • It promotes better relationships with customers and business partners by predicting their requirements and market behavior.
  • It generates revenue opportunities and increases profitability.

Here are the top 5 predictive analytics software that you can use for your business-

  1. Sisense
  2. Oracle Crystal Ball
  3. Microsoft R Open
  4. Microsoft Azure Machine Learning Studio
  5. IBM SPSS Predictive Analytics Enterprise

5. Service virtualization

It is impossible to carry out testing and system integration processes if your testing environment lacks vital dependencies. This is where service virtualization comes into play. It reduces delay and dependencies during delivery pipeline of the software, speeding up the software development and its delivery process.

How is service virtualization helping DevOps teams?

  • Using service virtualization, your DevOps team can implement virtual services in place of production services. It enables them to do frequent and comprehensive testing, even when key components are missing from your system architecture.
  • It enables complex and large applications to undergo integration testing much earlier in the development process, speeding up the overall software development life cycle.