DEVOPSdigest

An increased demand for highly skilled developers is creating a talent gap within the software industry. Automation can help solve the developer gap across the board by:

1. Easing learning curves with new hires/entry-level developers

2. Assisting with the production of relevant content

3. Speeding up workflow with administrative tasks

More importantly, though, human oversight will always be needed, thus furthering the need for skilled developers to help manage automation. The onset and product releases incorporating generative AI has many leaders scrambling to figure out how to implement it within their own organization. For developers, this is more of a natural transition because many already utilize automation within their daily operational workflow.

Why Is There a Developer Gap?

Because of the current economic climate — including financial uncertainty, layoffs, staffing shortages, etc. — developer teams are stretched especially thin. Recruiters are having a difficult time finding skilled talent that matches the caliber of companies' needs, especially in specialized coding languages.

According to the Bureau of Labor Statistics(link is external), the US is expected to have a shortage of 1.2 million developers by 2026. Finding qualified candidates who fit the criteria will be difficult in a talent shortage. Long-term solutions are needed to help alleviate the pressure current teams are facing.

For the developer teams who are struggling with their workload already, hitting their product release dates can seem impossible with a lower headcount. This can lead to burnout and low employee retention if leaders don't manage and provide tools to support their teams.

How Can Automation Help Solve the Gap?

Implementing automation within the software development life cycle (SDLC) is a workflow issue that developers are already familiar with. However, because many development teams are still working in silos, some tasks are still coded manually. Utilizing automated tools for unit or security testing, for example, can help with managing workflows since these tasks can be done simultaneously. Feedback loops from end-users can be integrated into a platform and those inquiries can get directly sent to developers who can work on fixing it.

Additionally, automating security within the SDLC can help optimize time efficiencies for security teams as well. Developers will be able to better identify vulnerabilities with threat detection and reduce time spent on security monitoring. With cybersecurity issues rising and costing billions of dollars, AI could reduce administrative manual labor, as well as reduce the potential for human error, all while strengthening an organization's security posture.

Automation also further enables developers to increase the remediation process for SDKs. Third-party SDK/APIs can also be thought of as automation tools. If a team developing an app needs to support PDF, or TWAIN, or barcodes for example, and they rely on high-quality, high-performance SDKs, then, in a way, they are automating that specific portion of the application.

Engineers only code about 10% of the time, according to Forbes(link is external), and by utilizing SDKs, they can focus on what they do best. The usage of automation will help developers focus on more critical components, creative projects, or breakthrough research and, in turn, create a more productive organization.

What's Next for Automation and Developers

So, what does this mean moving forward for developer workflow evolution?

Developer operations will continue to be more intuitive, more configurable, more reliable, and faster with automation tools. With generative AI being incorporated into software development applications, organizations will be able to utilize citizen developers with low-code apps. This could be an alternative partial method to solving the talent gap as code becomes more automated and AI systems are able to create it faster than any human could.

Today, generative AI systems can pull together the source code in almost any programming language for simple applications and it will learn to generate more complex solutions in the near future. Developers can take advantage of the efficiency gains provided by AI to get certain portions of their projects done, which will increase productivity. Developers may also need to upskill and specialize in areas where AI can't add as much value as a skilled programmer can provide. This includes honing in on softer skills like interpersonal communication and planning for product launches.

We are going to see more automation in the developer space in the future, of course in obvious places like unit testing, but also in ways that we haven't even thought of yet. Creating SDKs and tools will continue to require programmers and developers, especially in monitoring the results of AI-influenced processes. The machines might be revolutionizing workflows, but we will still need those human eyes on everything we do. Development is a process, not an endpoint, and we need to safeguard the checks and balances of any typical process, whether human or machine-driven.