The Next Generation of Software: Embracing Automation and Machine Learning

Automation and machine learning technologies can help to improve efficiency, reduce safety risks and speed up the development of software. However, there are also some novel chances that desperately draw on specialised knowhow.

Automation is often seen as a threat to human jobs, but typically these technologies simply remove tedious tasks and allow staff to focus on more complex chores. Cloud-based platforms, moreover, make this affordable even to the smallest businesses.


Automation entails employing hardware, software and algorithms to execute tasks that a human would be capable of performing, such as answering customer queries over the phone faster and with greater personalisation via an automated helpline or chatbot that uses natural language processing, or scanning medical images or other data to find clues to disease via machine learning programmes.

An automation tool, for instance, can be used to accelerate (rather than replace) the steps needed to keep programmers and teams working more efficiently on repetitive tasks such as code (unit) testing or deploying software, allowing more creative, strategic work to be done. Similarly, security threats to IT environments can be detected and responded to much quicker by humans due to automation systems.

By sending automated processes into the loop, smart software developers save time and effort in the long run by making their apps and processes more reusable. Thanks to intelligent automation software, developers can set their own automated processes without increas­ing the time the automation task will take or the cost of developing manual code.

Machine Learning

Machine Learning (ML) is the study of methods that enable computer software programs to become better at performing a task – by practising at that task – just as humans become better at typing or sports by practising. The algorithms learn from experience (ie, data) to discover patterns or future trends, for example, to support human decision making.

Industry after industry now uses machine learning (ML) models to remove manual bottlenecks and instead redirect human labour to higher level decisions. In the pharmaceutical industry, ML models are used to identify the most effective places to recruit patients and add diversity in the clinical trial participant population; in retail settings, they conduct the research needed to find customer trends and then recommend products and services to those whose interests align with such discoveries; manufacturing companies use them to learn when plants are likely to fail in order to issue timely maintenance notices, and in quality control monitoring, trusted cameras can look at products as items continue to move along the production line.

Predictive modelling enables early identification of coming needs, and activates them through automation to speed up software development. Early identification of potential performance issues ensures that bottlenecks and problems get fixed early. Through good planning and optimal allocation, predictive modelling can reduce costs, speed up developments, and improve quality of delivered applications. For example, with the unique insight of predictive modelling, the automation of testing across different types of systems increases efficiency substantially, while reducing the time and effort required for individual test cases. This is crucial as machine learning relies heavily on generating extensive tests.

Artificial Intelligence

While it is unlikely that AI will ever supplant human developers, there are places where it can enable automation and process improvement in software development: by helping to identify patterns and faults in the creation of new software; by reducing the amount of time and effort spent on testing; and by improving the experiences for those who use the software being created.

Many businesses are integrating AI technology as part of their product offerings or in their software-development process, yet it’s not always clear when or how to introduce AI technology to maximise value.

AI can be manifested in many forms, from the algorithms used to suggest you a purchase to a live chatbot you may be chatting with, automation of everyday tasks, or even predictive technology.

AI will enable an enhanced design phase by feeding the developer with automated tools that can, for example, assess the security, scalability and performance of the application. Next, AI will be able to highlight where problems may arise during the build and debugging processes, and it could even suggest ways to resolve those issues faster at each step of development. These smart applications will soon be able to predict the success of an app, before the designers have crafted it, thereby potentially helping to prevent costly surprises further down the line, when programs—and hard-earned cash—are redesigned or scrapped.

Next-Generation Applications

For software engineering projects, engineers are now reliant more on automation and machine learning technologically speaking, while harnessing new technologies to develop next-gen apps that create a collaborative and customised user experience.

Sophisticated tools need to be developed to design apps, and they must be able to detect and quickly patch security vulnerabilities accurately. AI-powered tools to detect and monitor threats in real time can enable software engineers to build software systems that can evoke users’ faith and trust.

These tools would facilitate remote work as well. In fact, collaboration would be an essential piece of any next-generation developers’ toolbox. It enables developers to work together to innovate and be creative. Collaborative development is a tool for employees to meet user demands faster in the moment, too. In the end, shifting to collaborative development will promote efficient workflows and increase the speed at which software systems can be developed, keeping business competitive in the ever-changing digital landscape.

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