This kind of automated coding is called “machine programming.” One of its most interesting capabilities is “code semantic similarity,” which attempts to autonomously determine whether two code snippets show similar characteristics or achieve similar goals. This has only recently become achievable due to advances in computing, access to “big code data” such as IBM/MIT’s new Project CodeNet which includes approximately 14 million code samples, and new machine learning algorithms.

By harnessing the power of code semantic similarity, the industry can develop automated systems to help CIOs ensure developer teams are maintaining the same level of productivity despite increased software and hardware complexity, all the while addressing the software developer talent shortage and combating burnout.

Enabling language-to-language translations

Code semantics similarity could also be used in tools that translate between programming languages (i.e., transpilers). Historically, software systems that convert a program’s source code from one programming language to another were out of reach. However, recent advancements in transpilation could be critical for large, global organizations that have traditionally coded in more specialized legacy languages.

Imagine a world where, instead of spending many years manually translating an entire organization’s code bank from COBOL to Python, a machine programming system could do it all for you — in just a few days. The beginnings of such systems already exist and are even used in some tech companies today, such as Adobe. For example, Adobe Photoshop, as I understand it, is using verified lifting to convert C/C++ to Halide in its current version.

Code semantics similarity systems – such as machine inferred code similarity (MISIM) — will not only help an organization to update its entire code system; they will also open up the talent pool. Updating an organization’s codebase to a modern programming language from older legacy languages that are less understood by today’s software developers will make recruiting easier as more developers are familiar with these newer languages (e.g., moving from FORTRAN to Python). CIOs might even see a reduction in coding errors because new-age languages tend to be easier to work with and handle much of the system complexity internally.

Elevating novice developers, helping to fill the developer gap

Code semantics similarity systems can also recommend code. GitHub’s Co-Pilot, which I mentioned earlier, for example, is designed to learn what the intent of a piece of software is and then recommend improved (or more complete) versions to help the developer.

When fully realized, such code recommendation systems have the potential to raise the software quality and productivity of both novice and expert developers by providing them with improved alternatives. Ultimately, this will help CIOs and their IT departments keep up with software demands without hiring additional employees or spending money on new resources. The blue-sky vision of these recommendation systems is to improve the productivity of all developers. Semantics similarity systems can also work in tandem with developers to autonomously detect errors in code.

The bottom line

The landscape of software development is growing in complexity due to software and hardware heterogeneity. Development teams are also expected to produce software at an increasing pace. Machine programming may be the only fiscally viable way forward for CIOs and the software development they oversee. So this is the right time to begin testing out emerging machine programming tools and seeing how to best implement them in your organization.

The largest areas of spending are communications services ($1.4 trillion projected for 2021) and IT services ($1.2 trillion), with the latter expected to grow more rapidly, primarily due to investment in cloud infrastructure to support remote working and continued digital transformation. John-David Lovelock, distinguished research vice president at Gartner, said in a press release:

“Technology spending is entering a new build budget phase. CIOs are looking for partners who can think past the digital sprints of 2020 and be more intentional in their digital transformation efforts in 2021. This means building technologies and services that don’t yet exist, and further differentiating their organisation in an already crowded market.”

Gartner’s projections are split across five categories, all of which are expected to expand in 2021 and into 2022. The highest projected growth rate is in enterprise software, at 13.2% in 2021 and 11.7% to $670 billion in 2022. Meanwhile, devices, which have seen a bumper 2021 thanks to the pandemic (13.9% growth), are projected to see much lower – although still positive – demand in 2022 (0.2%).

The IT services segment, which includes cloud, is projected to grow 9.8% in 2021 and 8.5% in 2022 primarily due to continued demand for IaaS. In 2020 the worldwide IaaS public cloud services market expanded by 40.7% in 2020, according to Gartner. While the firm has not yet broken out projections for IaaS in 2021/2022, it is likely that this trajectory will continue.

Recent research among UK IT leaders found that cloud IaaS/PaaS spending topped the shopping list, followed by end-user compute and then security software.