For many years, software developers have used code generation tools to lighten the load. Some refer to these as “automatic programming” solutions, though you’d be hard-pressed to name any developer who has ever automated himself or herself out of a job. Another popular phrase for these tools is “low code” or even “no code.” However, those terms obscure the fact that they all generate lots of source code and usually give programmers tools for maintaining and revising it all.

“Augmented programming” refers to both established and emerging approaches for boosting developer productivity. One of the hottest new approaches under this umbrella is robotic process automation or RPA. The common thread in all augmented programming tools is use of an abstraction layer that allows developers to write declarative business logic that is then translated by tools into procedural programming code.

Increasingly, RPA and other augmented programming tools use machine learning as an abstraction layer for automatically inferring program code from screenshots and other existing application elements. By reverse-engineering program code from existing applications, ML can boost developer productivity to an extent that traditional code-generation tools – with their reliance on manual techniques for declarative specification — have been unable to deliver.

So what should organizations do? Here’s what Wikibon suggests:

• Determine whether to augment programming across all development projects or simply in particular application domains. Developers should identify the degree to which your augmented-programming initiative should address a narrow or broad range of development requirements. Some tools are limited to such development approaches as e-form and database applications, while others address a wider range of Web, workflow, mobile, analytics and case management requirements.
• Adopt robotic process automation to augment programming of office applications by nontraditional developers. Explore the use of RPA software “robots” to infer an application’s underlying logic from its presentation layer, user-interface controls, interaction and messaging flow, and application programming interfaces. In this regard, the robots rely on ML, deep learning, natural language processing and computer vision to infer source code from externally accessible program elements.
• Explore emerging tools that rely exclusively on machine learning for automated program-code generation. Though immature as a development tool, ML-augmented coding is likely to gain adoption as a rapid application development tool that supplements and extends enterprise investments in low-and RPA tools. Rather than reinvent the wheel with handcrafted code or repurposed code modules, future developers may simply check off program requirements in a high-level graphical user interface, and then, with a single click, autogenerate the predictively best-fit code-build into the target runtime environment.

The bottom line is that organizations should adopt visual augmented-programming tooling to boost developer productivity. Here’s a quick checklist on how to do that:

• Explore mature “low-code” tools for general-purpose code-generation and for narrowly scoped application domains, including Web, mobile and business process management.
• Make sure that the tooling integrates with your development shops’ DevOps tools for continuous integration, issue tracking, schema migration, monitoring, security and scaling associated with auto-generated application code.
• Venture into RPA for UI-driven augmented programming of office productivity applications. Give preference to low-code and RPA tools that use ML and other AI approaches to speed automation of key development tasks.

All that will help organizations not only keep their developers productive, but reassure them that they have a continued valuable role for years to come.

Read the full Wikibon report on augmented programming, including various approaches, evaluation criteria and extensive vendor comparisons.

Image: geralt/Pixabay