A Updated Look at No-Code/Lo-Code

 A long time interest of mine:  How do we make complex things with much less code ... with no code if possible?   More productive.   Making it more secure as well.    Also includes much automated coding.  Here an update of the space:  Its coming fast:

No-code/low-code: Why you should be paying attention

Setrag Khoshafian, Khosh Consulting  in VentureBeat,  February 14, 2021 6:20 AM

We’ve all been hearing the hype lately about low-code and no-code platforms. The promise of no-code platforms is that they’ll make software development just as easy as using Word or PowerPoint so that the average business user can move projects forward without the extra cost (in money and time) of an engineering team. Unlike no-code platforms, low-code platforms still require coding skills but promise to accelerate software development by letting developers work with pre-written code components.

According to Gartner, 65% of application development will be low code by 2024.

I was involved in an early comparative productivity benchmark test between traditional development (using Java) and a model-driven low-code/no-code development project back in 2017. The results were impressive: 5X to 7X productivity improvement with low-code/no-code development. A survey by No-Code Census in 2020 showed a 4.6X productivity gain over traditional programming.... '

Here Comes the No-Code Generation?

Been a long time follower of automating coding.   What will it mean to the future?   Hardly superpowers.  In fact when it finally fully arrives, you will hardly know its there at all.    It will then be folded into an algorithm,   Until then the coders of the future will still have to make sure the complex logic works in context.   And its implications for the 'right' answer.   Sure it should remove lots of the bother of getting the code correct and as secure as possible.  But then, click, it should be gone.   No more generation needed. 

The No-Code Generation is Arriving  By TechCrunch

October 28, 2020

The success and notoriety of no-code platforms comes from the feeling that they grant "superpowers" to their users.

In the distant past, there was a proverbial "digital divide" that bifurcated workers into those who knew how to use computers and those who didn't.[1] Young Gen Xers and their later millennial companions grew up with Power Macs and Wintel boxes, and that experience made them native users on how to make these technologies do productive work. Older generations were going to be wiped out by younger workers who were more adaptable to the needs of the modern digital economy, upending our routine notion that professional experience equals value.

Of course, that was just a narrative. Facility with using computers was determined by the ability to turn it on and log in, a bar so low that it can be shocking to the modern reader to think that a "divide" existed at all. Software engineering, computer science and statistics remained quite unpopular compared to other academic programs, even in universities, let alone in primary through secondary schools. Most Gen Xers and millennials never learned to code, or frankly, even to make a pivot table or calculate basic statistical averages.

There's a sociological change underway though, and it's going to make the first divide look quaint in hindsight.   ... " 

Github Code Scanning to Find Vulnerabilities

Like the idea of scanning for basic vulnerabilities.  and ultimately linking to code automation.  Further would like these to be adapted to specific domain risk analysis.  Have not seen this in the enterprise.  

GitHub Launches Code Scanning to Unearth Vulnerabilities Early   in VentureBeat, by Paul Sawers

GitHub last week launched a code-scanning tool to help developers identify flaws in code prior to its public rollout. A result of GitHub’s takeover last year of code analysis platform Semmle, the new tool is a static application security testing solution that converts code into a queryable format, then searches for vulnerability patterns. It automatically identifies flaws and errors in code revisions in real time, alerting the developer before the code approaches production. GitHub said during the scanner's beta-testing phase it scanned more than 12,000 repositories more than 1 million times, discovering 20,000 vulnerabilities; developers and maintainers corrected 72% of these errors within 30 days.  ... '  

Automating Coding: MISIM

 Automatic coding should be here.   This provides a simple approach, but where will it work best?  Where the context and intent can be well determined.

Automated Coding Could Mean Big Changes for Software Developers
By ZDNet in CACM

Researchers from Intel and the Massachusetts Institute of Technology have unveiled a machine learning tool that brings developers one step closer to automated coding.

MISIM, or machine inferred code similarity technology, analyzes snippets of code to understand a piece of software's intended function, then constructs a database in which software code with similar outcomes are assigned similarity scores. MISIM can understand a new algorithm's underlying intent, and suggest to engineers alternative programming approaches, or efficiency-boosting corrections and options.

Intel's Justin Gottschlich believes MISIM could have a great effect on productivity, as software development grows in complexity. This invites speculation that the algorithm could eventually be used by any member of the general public with a good software concept.

From ZDNet
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