Throwing the Bones: Predictions About New Trends in Software Development

If this year has taught us anything, it’s that we can’t predict the future—no matter what’s in our tech stack.

At the same time, we’re entering a new era. The pandemic has forced organizations to ramp up investments in digital transformation, embrace new ways of working, and replace legacy applications that no longer make the cut.

A few months into 2021, organizations are working with a new set of priorities—investing in software development technologies that help them build more agile, resilient business models to take them into the future.

In this article, we’ll take a look at some software development trends just on the horizon that could be the next big thing.

Infrastructure Will Evolve to Support New Needs

Organizations are beginning to acknowledge the critical role of infrastructure when it comes to supporting how we work today.

3Pillar’s Jaime Salame predicts we’ll see more remote work moving forward. At the same time, Salame cautions that organizations will need to make sure that they get strategic when it comes to building infrastructure to support a distributed org structure.

He says, “being a leader in IT without a physical office and team means that you’re going to need to rely more heavily on your communication and organizational skills to overview your teams and work. Plus, you’ll need to be able to understand business problems at the same time. It requires seeing problems from the technical point of view and how that problem is affecting your client in dollars.”

According to Deloitte’s 2021 Tech Trends report, organizations will need to seriously think about how they want to move forward with remote work. The firm recommends answering the following questions:

• Is it sustainable to embrace remote work as a permanent solution?
• What are the impacts on employee productivity and well-being?
• What role will the physical office play moving forward?
• Will remote work hurt the organization’s ability to innovate?

From there, you can decide whether you’ll move to a hybrid model, go fully remote, or return to the office full-time and plan your tech investments accordingly.

Finally, it’s important to focus on investing in software development technologies that support long-term resilience. According to a recent Enterprisers Project article, companies are ramping up investments in tools that strengthen business resiliency, such as data management, predictive analytics, and ensuring they’re working from a stable infrastructure.

Artificial Intelligence & Machine Learning Will Expand

Lead Software Engineer Paul Estrada predicts we’ll start to see AI/ML become more ubiquitous relatively soon.

He says, “AI and machine learning tools are evolving right now—and still exist primarily within this more professional niche. As these solutions become even more essential to the software development process, I can imagine they’ll become a requirement for building successful products—across all industries.”

According to McKinsey research, AI is currently used primarily for handling service operations, as well as developing products and services. Common use cases include customer service chatbots, AI-enabled sales analytics, and targeted, dynamic social media ads.

While the firm acknowledges that there’s already a general “playbook for success,” organizations need to focus on specific use cases to unlock the true potential of AI/ML.

As advanced AI applications become more affordable and easy to use, organizations may soon rely on niche-specific applications and custom software to capture insights that allow them to become more competitive.

Beyond making sure that your AI tools are capable of capturing and analyzing insights that aren’t readily available to your competitors, you’ll need to really get serious about AI engineering.

According to Gartner, AI is critical for digital transformation, with businesses relying more on intelligent solutions that automate and augment decision-making and basic tasks.

The problem is, organizations face several challenges: maintaining and scaling reliable solutions, interpreting insights and putting them to work, and developing strong governance for how AI applications are used.

AI engineering provides a solution. The idea is, AI becomes embedded into the DevOps process rather than being applied to individual, isolated projects. Per the Gartner report, organizations that focus on operationalizing AI across the entire network will get the most value from their investments.

Human Augmentation

Human augmentation describes the use of technology to enhance, replace, or supplement our natural capabilities. The global human augmentation market is expected to grow rapidly within the next few years—estimated to hit $207B by 2024.

Pre-Sales Engineer Denisse Vega predicts that we’ll see a lot more “bots and AI” applications enter the tech stack this year—and beyond.

3Pillar’s Henry Martinez says he expects that “data science will be used to automate low-value software skills like language translation, managed services, QA testing, web browser-agnostic testing, UX data validity testing, and so on. This means that we’re likely to see continued demand for data science skills—both machine learning and data engineering—for the foreseeable future.”

Human augmentation comes in many different forms. For example, we’re seeing AI guide salespeople through deals and help marketing teams create more relevant ads.

Robotic process automation (RPA) can automate e-commerce tasks and streamline customer service.

And then there’s low-code/no-code platforms—a software development trend that offers a ton of potential for helping teams do more in less time. Deloitte’s 2021 Tech Trends report predicts we’ll start to see more organizations look toward low-code platforms as a way to recreate legacy IT assets.

While there’s a lot of uncertainty about working hand-in-hand with robots, organizations will need to embrace this new class of workers to remain competitive in the future.

Programming Languages

Like low-code/no-code development, simpler programming languages are another software industry trend that has emerged in response to the need to move faster and democratize innovation.

Paul Estrada notes that programming languages, by nature, are always changing. He says, “programming languages have been evolving into high-level languages since the inception of the very first programming tools. This is most obvious with domain-specific languages (DSLs) layered on top of existing toolchains like React with JSX, TypeScript with plain JavaScript, or Multi-platform Kotlin with plain Java. These DSLs, along with custom toolchains, provide easier development, but it comes at the cost of increasing tooling complexity.”

But, he adds, “because these DSLs, run on top of existing tools, they’re able to evolve faster than other programming languages, thus creating larger knowledge gaps and a shortage of qualified engineering talent.”

Javier Trevino predicts that “developer profiles will need to become more “hybrid,” as demand for developers with cross-functional abilities like development, testing, DevOps, and cloud, etc., increase in demand.”

At the same time, complex languages can prevent development teams from moving fast. In other words, they may make it difficult to keep pace with competitors and meet customer expectations.

Octavio Islas predicts we’re on the brink of an industry-wide embrace of simpler solutions. He says, “as the industry continues to prosper, with new developers joining in large numbers, I think we’ll see easier, developer-friendly programming languages become more popular.”

He adds, “as it stands, Python is the number one data science language—and the second or third most popular language across all other fields, with the exception of low-level programming.
JavaScript is the undisputed leader in web development, but it’s also becoming increasingly popular across other domains, like backend development. It’s also a perfect language for beginners.”

While “older” languages like Python and JavaScript remain high in demand, we’re also seeing growth among modern programming languages like Rust, Go, Kotlin, Swift, and TypeScript, which focus on developer ergonomics and support modern features like type inference, null safety, expressiveness, and conciseness. What’s more, these newer languages are better equipped to leverage the benefits of new hardware and infrastructure—think cloud, multi-core, GPU, TPU, and so on.

Hyperautomation

According to Gartner, another key software development trend to watch is hyperautomation—which refers to the idea that anything that can be automated should be automated. This shift is being driven largely by organizations struggling with legacy processes and patchwork solutions that are creating unnecessary expenses and inefficiencies.

As organizations ramp up their investments in automation, they’ll need to develop a mature Agile practice to realize its full potential.

3Pillar’s Francisco Carvajal says, “Agile is considered a paradigm, as opposed to an actual methodology. Take all of its guidelines to heart, don’t skimp on anything, as unnecessary or labor-intensive as it may seem, and you’ll see your tech investments yield much greater returns relatively soon.”

In other words, don’t cut any corners. Abel Gonzalez Garcia echoes a similar idea. He says, “automation depends on the proper implementation of Agile best practices, as well as the use of CI/CD. These practices make a big difference and will help development teams get better results from automation.”

Final Thoughts

In looking at these software development trends, there’s a clear theme: embracing simplicity and speed when possible is the way forward. The tech landscape is complicated enough as-is. We’re dealing with rising customer expectations, the fallout of a global pandemic, and big data ecosystems expanding at an exponential rate. While leveraging these new software development technologies to their full potential requires more than implementing a few tools, those that manage to get it right stand to emerge as industry leaders in a new era.

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