Home Code Quality 4 trends to boost software quality in 2020

4 trends to boost software quality in 2020




Today’s abundance of technology has created an increased demand for higher software quality. With companies aware of this, spending on IT worldwide is estimated to grow by 3.7% during this year (Gartner Research). In this guest blog post, you’ll learn four trends to achieve higher software quality in 2020.

1. AI-Based software quality

Artificial Intelligence (AI) technologies enable the automation of processes. The goal is to design AI software that can perform tasks with as little human intervention as possible. Machine learning (ML) models train AI software to perform autonomously. 

The AI of today is not the sentient being of science fiction movies, but it has improved significantly. Advances in learning algorithms, supercomputers, and big data provide AI software with the three key necessities—understanding, speed, and information.

As AI models improve, more use cases become available to the public, especially in the field of software development. Automation enables developers and security experts to speed up deployment and maintenance. In this workflow model, AI carries out repetitive tasks, enabling developers to concentrate mainly on creative and complex work.

Applications of AI in the field of QA include:

  • Visual testing of User Interface (UI): visual testing is a quality assurance activity that checks whether your UI appears correctly to users. ML-based visual validation tools can evaluate the interface for predefined patterns and report discrepancies or issues. This can be especially useful for testing uniformity across devices.
  • Spidering AI: automatically write tests for your application. You point the spider bot at a web app and it starts to crawl through the application, building up a dataset. You then use this dataset to train an ML model based on the normal patterns of your application. When you run the finished tool, the AI detects and flags discrepancies. 
  • Optimizing the test suite: you can use ML algorithms to detect the minimum number of tests required to ensure adequate test coverage of your software. Eliminating duplicate or unnecessary tests can save time while ensuring that defects are not overlooked. It can also increase productivity since developers and testers are free to focus more time on any complex defects that are found.

2. Specialized testing for Big Data

When testing applications that work with big data, it is vital that information be efficiently processed. Applications need to be able to process and analyze data quickly and consistently. Unfortunately, large amounts of data can make application tests run significantly longer if not handled carefully. To overcome this challenge, companies are developing specialized testing platforms, taking into account big data. 

Platforms that can reproduce the behavior of layers of application data can be the solution to this challenge. These tools should help testers process data efficiently and uniformly. These tools should also enable testers to import new data while initial batches are still being processed, ensuring faster testing. 

3. Continuous Integration/Continuous Delivery (CI/CD) adoption

Many organizations are moving to, or have already adopted CI/CD processes, typically in combination with agile methodologies. CI/CD’s focus on faster development with small, frequent releases makes it a more flexible development process. It also helps organizations better meet customer demands for ever-improving products.

In contrast with traditional development processes, CI/CD requires testing early and throughout the lifecycle of a product. With CI/CD, code is tested immediately after development and before being added to the codebase. Once added, the combined code is tested again before deployment as well as in production. This ensures quality code from start to finish and reduces the risk of vulnerabilities causing further complexities or issues. Automated code review tools, like Codacy are being increasingly integrated into CI/CD pipelines of organizations to help produce higher quality code.

4. Quality Assurance for IoT

It is estimated that the number of Internet of Things (IoT) devices around the world will reach 75 billion by 2025. Although companies are increasingly adopting these technologies, many do not yet have adequate testing strategies. This makes IoT devices especially vulnerable and leaves much room for improvement. 

Quality assurance for IoT requires specialized testing to accommodate distributed workloads. To test these devices and applications, teams must be able to effectively monitor a wide range of communication protocols and operating systems. This includes testing performance, security, compatibility, device authenticity, and usability. Centralizing data and remotely operating tools from the same environments the devices are hosted will be key to addressing this difficulty. Again, tools like Codacy can help with the testing processes.

Wrap Up

Consumers have become more aware of the risks associated with poor software quality. Added to this, the market is continuously expanding. To produce successful software, then, you need to consistently produce an exceptionally high-quality product. Expectations of quality in 2020 will be no exception. If anything, the push for quality will increase, demanding more rigorous testing and more efficient processes. 

Hopefully, this article helped you understand some of the trends that will drive software testing and quality, in the coming year. Consider adopting some of these trends. Doing so can help ensure that your software continues to exceed customer expectations.

About the author

This blog is a guest post by Gilad David Maayan. Gilad is technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.


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