Artificial Intelligence is one of the trending technologies that has played a crucial role in transforming software development in recent years. Right from coding to the deployment of software, AI has enhanced the process and set a new benchmark for developers to follow.
Along with Machine Learning technology, AI supports developers optimize their workflow during every stage of a development process. Leading business analysts and IT experts have predicted a massive shift in the future with Artificial Intelligence poised to bring in new innovations in the process.
This includes a change in how developers work in a project and how they go about building an application. All these enhancements essentially mean the overall quality of the software will improve with better speed, quality, and productivity.
How Does AI Improve Software Development?
Here are 5 ways Artificial Intelligence improves the software development process:
Speed and Scalability
The speed and scalability of a software application are key aspects that can be judged by software delivery and performance. The deployment frequency, time taken for changes, and time taken to restore service are all key performance indicators you can gauge time by.
Machine learning is capable of shortening many processes in the SDLC with the QA process, in particular, getting by quickly. This is plausible because AI technology can run automation tests which save both human effort and time significantly.
AI has been a massive revelation for QA in particular because manual testing presents a higher possibility of error. It allows a system to run tests accurately and in quick time to reduce the rate of failure and shorten the overall development process.
Due to the acceleration in proceedings, more test scenarios are implemented and the chances of errors escaping are also reduced. AI also helps developers with coding by increasing speed and accuracy.
Changing The Role of Developers
While the process of software development is changing, so is the role of software developers. Although we have not the point where AI tech can write the entire code by itself, it does help developers a great deal while writing the code.
When developers automate certain repetitive tasks and assign them to an AI machine, it allows them to focus on critical areas of development. This is how AI collaborates with developers to maintain flexibility in the process. This is specifically the role of developers who will transition with AI integration.
Therefore, the software development process is improved by many notches and not replaced. While there were deliberations that developers will become redundant if AI takes care of everything, these developments have brought us to a stage where more developers are needed to manage AI and work around it.
Market analysts have predicted that even if AI will write code one day, it is unlikely to completely replace developers completely. But the role of developers in the process will see massive transformation in the coming decades.
AI can massively impact strategic decision-making as it automates and reduces the need for manual effort. All the time spent debating where the resources can be invested is radicalized courtesy AI. Especially if your AI is programmed according to how the previous software fared.
It naturally allows developers to assess the performance of the current software and mitigate the risks involved. Market analysts expect decision-making in the software development process to be revolutionized because most of these decisions are driven by analytics.
Better decision-making processes that are rooted in previous system behavior and based on those specific analytics will substantially minimize the risks involved in it.
Strategic decision-making by AI helps eliminate human errors significantly. Machine learning can gather, analyze, and leverage system data to base the decisions of this.
AI-enabled programs comprise smart features that enable it to identify errors quickly. For instance, if you provide data previously analyzed to this program, it will conveniently identify all the common errors. There is no need to roll back here if these errors were already flagged in the development phase.
Even in the post-deployment phase, Machine Learning is used by operations teams to actively flag errors and discover inconsistencies in the system logs. Poor error management is responsible for downtime in software development most of the time especially when running a SaaS application.
Even a single minute of downtime can affect the organization drastically because customers could be using an application at any time of the day. The automation features of AI help detect and diagnose errors in the software without any manual effort.
Even today, with so many algorithms and applications at their disposal, developers cannot be accurate with estimates on timeline and costs. This is where AI-enabled programs come in. Data from previous projects helps AI provide a precise estimate of the time and budget required for a project.
Accurate prediction requires a thorough understanding of the process and all elements involved. And developers can train an AI-based program to figure this out. Once it is trained, it will deliver highly accurate results that help you plan the production better.
This will also allow you to coordinate better with your clients and give them tentative delivery timelines. Sticking to these timelines will bode well for you with customer retention moving forward.
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