The editor can now generate React code, which has been a highly requested capability by the community, according to Infragistics. With this addition, App Builder now covers all of the major web frameworks. 

“Whether you are working with Angular, Blazor, or Web Components and now React, App Builder will generate code for pixel-perfect apps that are production-ready, performant, and maintainable,” said Jason Beres, SVP of developer tools at Infragistics. 

Another new feature in this release is support for two-way data binding in the Select, Text-area, and Radio-group components. With this new feature, any changes to the UI are reflected in the underlying data model immediately and vice versa, which cuts down on manual updates and ensures data consistency across the app. 

This release also includes improved Datasource notifications, which are notifications that allow developers to view changes that impact their application. 

And finally, the latest version of App Builder also improves the onboarding process for new customers. A new Guest-access mode allows first-time users to explore the platform without having to sign-up for an account. Guests will be able to save all of the progress they have made by signing in with an account once they have decided to continue using the platform to build their apps. 

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Platform engineering, and Instance Management Platforms, emerged as a way to codify and standardize the management of the platform including its CI/CD production pipelines. However, in the age of low-code/no-code (LCNC) platforms like the ones named above, applying platform engineering principles to these platforms is beneficial for non-developers and classic developers alike. LCNC platforms allow developers to immediately focus directly on developing sound business logic without coding the requisite application logic. Theoretically, this should shorten the time to market and lower maintenance costs since the platform handles all the application infrastructure (memory, storage, network, etc.). However, it’s critical not to overlook that organizations onboarding citizen developers will face the same challenges pro-coders see in enterprise development. 

Addressing the Root Causes of Chronic Delays

Most prominent players are still experiencing chronic delays in their operations, so they have turned to platforms. However, they often quickly find that even with these platforms, they are still experiencing chronic delays at pivotal times in the development lifecycle, which can be due to several factors. 

Inefficient deployment practices, slow approval processes, and lengthy manual testing all contribute to delays. Fixed release schedules are another big contributor. When companies can’t release on demand, they have to wait for the next change window, which limits how often they can release to production.

Beyond this, for companies using platforms like ServiceNow or Salesforce, processes like cloning databases or instances to serve as production environments can also be time-consuming. Cloning is typically used to copy production data/information to pre-production environments to test developed changes. 

While cloning is necessary to align production updates across all non-prod environments, this process (typically being database-heavy) can take up to 10, 20, or even 30 hours. That’s a lot of time for developers to sit idle; lost time is only the tip of the iceberg. 

These are just a few of the hurdles platform engineering teams are helping companies overcome, and they are doing it in a variety of ways. 

First, platform engineering teams and technology are helping to navigate the transition from fixed release schedules to on-demand releases by introducing better infrastructure, tools and processes that enable continuous integration and continuous delivery (CI/CD) pipelines. Beyond that, with automated deployment processes, companies can push changes to production without manual intervention, allowing for frequent and smaller releases.

Second, when it comes to processes like cloning, automation and accuracy are everything. If platform engineering teams can automate and accelerate their cloning process, they can minimize the discrepancies between source and target. The key is to establish and standardize better ways to minimize downtime and errors so that the platforms themselves can support a better service delivery standard. 

Who Owns that Delivery Pipeline?

Governance and standardization are crucial elements in the context of platform engineering. The platform engineering movement began when software engineers realized that building a CI/CD delivery pipeline involved significant coding. They recognized that the pipeline itself should be treated as an application platform, requiring a dedicated team of engineers. 

Many enterprises don’t anticipate hiring people specifically to maintain and build delivery pipelines. They might assume that using cloud services means everything is automatically taken care of. Consequently, part of the development team’s time is often allocated to managing the delivery pipeline as an application, which can be feasible since they are already responsible for app maintenance. This hidden burden is typically integrated into the overall maintenance costs of all the applications the development team is working on.

However, issues can arise in delivery pipeline governance when admin privileges become too widespread, and deployment practices too inconsistent. Beyond this, platform environments can spiral out of governance when there are too many changes in non-production environments. 

This is where we are seeing platform engineering teams begin to own the delivery pipeline, and introduce more automation surrounding governance and deployment flows and around the software development lifecycle in general. The reality is that platform teams should be looking to operationalize governance in the same way they standardize how code is developed, built, and deployed. The tools are out there to mindfully and intentionally embed governance in processes, and the results are helping teams to become better aligned. 

Keeping Environments as Production-Like as Possible

Often, when companies think about platform engineering, they think about the pipeline, not what environment the pipeline is passing through, or how to keep non-prod environments as production-like as possible. Without this alignment, the classic ‘works in development, not in production’ conundrum may be inevitable. 

Successful platform engineering teams keep environments as production-like as possible because they understand the value of testing and pushing tiny snippets of code to reduce the risk of something going wrong. When new functionality is tested in production-like environments all the way through, companies can demonstrably reduce the risk by size and volume, and improve quality. This is all part of the practice of scaling and building sustainable large enterprise systems

Ultimately, platform engineering has been tasked with solving the enterprise development problems encroaching on developer’s lives, and there is still a lot of work to be done. Without a strategic approach to managing platform engineering within modern LCNC platforms themselves, the enterprise development community won’t be anywhere near close to delivering at the speed today’s business demands without compromising quality or compliance.

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The first update is the ability to extract insights from large amounts of data. It provides an easy-to-understand summary of the insights, which can then be shared with other users or teams. For instance, it can analyze unstructured data such as customer feedback and summarize the most relevant points. 

Next, the company added the ability to automatically apply categories or tags to data, helping teams keep their data better organized. Categories can be based on things like theme, sentiment, product features, and more. 

“If you aggregate thousands of transcripts of customer calls, you can ask Airtable AI to categorize each call according to sentiment (positive, negative, or neutral) and group the data accordingly,” Airtable wrote in a blog post. 

Users can also now create content based off of their data, such as generating emails for sales teams or drafts of campaign assets for marketing teams. It does this by using information about messaging, product, and audience that has been stored in Airtable.

Airtable AI also now offers the ability to automatically translate content across multiple languages. In addition to just translating language, it can also translate tone based on a specific region. 

“Rather than just translating from English to Spanish, Airtable AI can translate copy from English to Mexican Spanish specifically, with a friendly, consumer-oriented voice and tone. This hyper-local approach makes your content immediately relevant to your target audience, and produces a much more accurate and compelling translation compared to other solutions,” the company wrote. 

And finally, there are new capabilities to improve data routing so that data gets in the hands of the right team. This frees up managers from having to spend their time identifying what teams data requests should go to. 

“Organizations have enormous potential to transform the way they work with AI,” said Howie Liu, co-founder and CEO of Airtable. “From the beginning, Airtable has been the most powerful way for teams to work with structured data. Now, Airtable AI offers the fastest way to bring AI to the data and the workflows that matter the most, uniquely unlocking AI capabilities for customers small and large.”

The post Airtable launches several new AI-based features to improve productivity appeared first on SD Times.

With this update, developers will be able to write code from within their preferred IDE, rather than having to go through Prismatic’s low-code designer.

Many options can now be configured directly within code, such as triggers, connections, integration logic, and customer-facing configuration experience. 

According to Prismatic, one benefit of this update is that developers can now define their own integration logic rather than only being able to choose from predefined logic steps. Another benefit is being able to quickly add integrations to CI/CD platforms and code repositories. 

“Today’s launch of code-native integrations is part of our deep commitment to providing the most versatile and dev-friendly embedded iPaaS on the market,” Marcus Edgington, VP of product at Prismatic, wrote in a blog post. “At Prismatic, we believe that any integration solution for B2B SaaS needs to be extremely versatile to keep up with the huge variety of integrations you build, all the teams involved in delivering them, and the ways your integration strategy might evolve over time.”

The post Prismatic adds code-native integrations to its low-code platform appeared first on SD Times.

The new platform, Einstein 1 Studio, builds on Einstein Copilot, which is the company’s generative AI solution. The Studio includes three main components: Copilot Builder, Prompt Builder, and Model Builder.

Copilot Builder allows users to create custom AI actions for different tasks. It provides access to use familiar tools like Apex, Flow, and Mulesoft APIs so that AI actions can be completed within the flow of work. Custom actions can be activated in any Salesforce application or external system. 

 Prompt Builder can be used to build AI prompts that can be reused across different experiences. For instance, a user could build a button that enables a contact center agent to get an overview of all of a customer’s escalated cases in just one click.

Model Builder provides the ability to build or import AI models. Customers can either use its low-code interface to build their own AI models that are trained on their cloud data, or integrate with existing models from Amazon, Anthropic, Azure OpenAI, Cohere, Databricks, Google Cloud, and OpenAI. 

Einstein 1 Studio also introduces the Einstein Trust Layer, which is a collection of features to enforce security on generative AI, such as data masking and audit trails. 

“Customers have always loved how easy it is to customize Salesforce. Our new Einstein 1 Studio makes it easy for admins and developers to build and customize Einstein Copilot and embed AI apps in the flow of work within Salesforce, tailored to the specific requirements of their company and industry,” said Clara Shih, CEO of Salesforce AI. “Built on our Einstein 1 Platform and unified by metadata that safely connects and unlocks data from across an enterprise, Einstein 1 Studio’s low-code tools democratize AI app development, unleashing a new wave of innovation that will transform workflows and augment human capabilities across every team and function.”

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This is according to Forrester’s Low-Code And Digital Process Automation Market, 2023 To 2028 trends report.

Forrester says the first option — that low-code continues on its current growth trend — is the most likely scenario at the moment. This scenario would see low-code and digital process automation (DPA) growth being driven by AI. 

The firm predicted back in 2020 that the market would grow to $12 billion by 2023, which was actually an underestimate as the market was actually evaluated as a $13.2 billion market last year, giving it a 21% average growth yearly since 2019. Forrester predicts that with this outcome, the low-code market grows to $30 billion by 2028. 

The company also predicted two other scenarios that could occur: low code gets more growth because of the popularity of AI, or the opposite occurs and AI hinders low-code growth. 

According to Forrester, nearly every low-code and DPA vendor is adding AI-enhanced capabilities, aka TuringBots. This scenario assumes that low-code market growth will roughly follow the growth trajectory for generative AI, which Forrester predicts as 33% per year. 

The other scenario — that AI kills the low-code market — is the one Forrester considers to be least likely. It would come about as a result of conditions like a bad economy, market saturation, or several high-profile security incidents tied to citizen developers. 

“The most dramatic possibility is that TuringBots make traditional high-coding so productive that professional developers reject low-code and switch back to high-coding everything,” Forrester wrote in the report. “Therefore, in this scenario, we assume a growth rate of 11% over the next five years, which is generally in line with Forrester’s projections for the commercial software market as a whole.”

In addition to predicting what’s to come, Forrester’s report also included several observations on what’s happening in the market currently. It seems that low code and DPA have become interchangeable and that the distinction between citizen and professional developers is blurring, with fusion teams are becoming a reality.  

Trends among low-code vendors have included that vendors from adjacent categories are entering the space, the larger vendors (Microsoft, Salesforce, and ServiceNow) are dominating the space, and the smaller vendors are starting to specialize on specific use cases as a result.

The post Forrester predicts three possible outcomes for the future of low-code appeared first on SD Times.

But as businesses grew and innovated, many companies still looked to highly skilled developers to meet their needs.

Today, the demand for tech talent is far outpacing supply. And while growth in software development as a profession is expected to swell by more than 25% over the next 10 years, it’s just not enough. An expected 750 million cloud-native apps are forecast to be created globally by 2025. To accomplish that, low-code is again back in the spotlight; only this time backed by a technology that is truly game-changing.

Enter generative AI.  When married with low-code platforms, generative AI can:

• Lower barriers of skill or technology for anyone to build automations and apps, with no coding knowledge
• Streamline dev processes and first mile setup for high skilled developers, increasing productivity and higher velocity leading to reduce IT backlogs

When employing these tools effectively, there is often a multiplier effect.

For the so-called “citizen developer”— or non-IT-trained employees who create software — there can be an almost transformative result, injecting novel approaches and a diversity of perspectives into software development. A business analyst, for example, who knows a company’s pain-points and bottlenecks could be empowered, using AI-powered low-code platforms, to automate operational processes in ways that were never possible in the past.

For the highly skilled IT worker, who knows how to prompt the AI in specific ways, that individual could see massive efficiency and velocity gains in productivity.

Assume, for example, that 60% of software development is boilerplate – it relies on sections of code that are repeated frequently with no or little variation. The other 40% is where the majority of customization and changes occur. By using a localized AI-powered low-code platform to identify and automate those boilerplate tasks, the skilled developer is then freed up to spend more time and brainpower on what the remaining 40% requires.

The result is often a better and more quickly developed product. And that is something companies are beginning to recognize in greater numbers. In fact, such tools are already reshaping much of the landscape.

By the Numbers

By 2026, it is projected that developers outside company IT divisions will account for at least 80% of the user base for low-code development tools. That’s up from 60% in 2021, according to the technological research and consulting firm, Gartner. Meanwhile, other low-code technologies, such as rapid mobile app development (RMAD) and rapid application development (RAD) programs – designed to help developers build apps more quickly – are also increasing.

Such tools, powered by generative AI, also integrate within existing systems and infrastructure. For instance, if a company wanted to build a tracker for marketing events, it could utilize pre-built ServiceNow technologies. And, by leveraging those platforms and using natural language inputs, like an English-based text, the tracker could be built within minutes.

The hurdle for companies, however, may be just in getting started. Even for those who embrace low-code, training is often needed for broader adoption among employees. But there are ways to mitigate those obstacles, including chat interfaces that are becoming ever more effective and user-friendly.

The Bottom Line

In the end, it’s worth it.

AI-powered low-code platforms are a way of shortcutting the traditional app-development process, getting to the design, dev and release phase within just minutes, as opposed to weeks or months. Governance and control are still paramount, however. And security measures need to be established to make certain applications safe before they are widely adopted.

But the possibilities are virtually endless. And with the right tools, companies can make themselves more responsive to market needs, and more agile in making good on new ideas.

The post How GenAI and Low-Code are Turbo-Charging Software Development appeared first on SD Times.

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Parasoft helps organizations continuously deliver high-quality software with its AI-powered software testing platform and automated test solutions. Supporting embedded and enterprise markets, Parasoft’s proven technologies reduce the time, effort, and cost of delivering secure, reliable, and compliant software by integrating everything from deep code analysis and unit testing to UI and API testing, plus service virtualization and complete code coverage, into the delivery pipeline.

RELATED CONTENT: The promise of generative AI in low-code, testing

OTHERS

Airtable is a popular low-code platform that allows you to turn your databases into interactive applications. It has already implemented a number of AI capabilities to help make it easier to use, such as the ability to add AI models to applications and refine workflows with the help of AI. 

Appian is a low-code tool for automating business processes, and it features a number of AI-powered capabilities, such as the AI Skill Designer for creating custom AI models, generative AI functionality in the low-code design studio, and a data fabric that connects data across an organization. 

 AWS supports low-code development with multiple platforms, including Amazon QuickSight, which is a low-code application development platform specifically designed for creating business intelligence applications; and AWS Amplify Studio, which allows developers to build web and mobile apps. 

Mendix’ low-code platform found itself at the very top of the Gartner Magic Quadrant for low-code application platforms this year. It has an AI-assisted development bot called MxAssist, which provides guidance and enforces best practices during the development process, and also helps to remove inefficiencies from applications.  

Microsoft Power Apps is a platform for building and sharing business apps, and has a built-in AI copilot to help users get started with building apps faster. For experienced developers looking to do more with Power Apps, there is a way to extend it using Azure Functions and custom connectors to other systems. 

OutSystems currently has a connector to ChatGPT, knowledge from over 25 million anonymized patterns that can be incorporated into applications, the ability to predict what to do next, and an AI mentor that provides help, orientation, and knowledge. 

Quickbase provides a no-code operational agility platform that enables organizations to improve operations through real-time insights and automation across complex processes and disparate systems. The recently announced Quickbase AI enables customers to describe their business problems and have an application created for them.

Salesforce has a number of different platforms for different needs: the recently announced Einstein platform can be used for AI predictions and content generation, Flow helps with automations, and Lightning can be used to create user interfaces.

ServiceNow Creator Workflows, built on the Now Platform, equips creators of any skill level with the low‑code and AI tools needed to create and deploy workflow applications at scale. The offerings on the Now Platform, including recently released generative AI‑enabled Now Assist for Creator, can turn employees into app developers with guidance‑driven development flows, easy‑to‑adapt templates, and text‑to‑code capabilities. ServiceNow is the only vendor recognized as a Leader in the 2023 Gartner Magic Quadrant for Enterprise Low-Code Application Platforms that is also peer-recognized with the Gartner Peer Insights Customers’ Choice distinction.

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One software category that is particularly well-suited to being boosted by AI is low code, as that is already a market that has a goal of making things easier on developers. 

Just as low code lowered the bar to entry for development, generative AI will have a similar impact because of such things as code completion and workflow automation. But Kyle Davis, VP analyst at Gartner,  believes that the two technologies will interact in more of a collaborative effort than a competitive way, at least for citizen developers. “Even though you could use generative AI to generate code, if you don’t understand what the code is doing, there’s no way to validate that it’s correct,” he said. “Using low code, it’s declarative, so you can look at what’s there on the screen and say, ‘does that make sense?’”

RELATED CONTENT: A guide to low-code vendors that incorporate generative AI capabilities

However, Davis also says it’s really too new of a market to make any real predictions. “We’ve seen a lot of failure, we’ve seen a lot of success, because it’s so early days that, at best, you’re kind of experimenting with this now. But the hope is that it can offer a lot of potential,” he explained. 

According to Davis, there are three main ways AI is being incorporated into low-code platforms. 

First, there are generative AI capabilities that are designed to improve the developer experience.

Second, there are generative AI capabilities targeting the end users of the application created using low code. “So embedding like a Copilot or ChatGPT type control within the application. That way the user of the application can ask questions about the app’s data, as an example,” Davis said. 

Third, there are features related to process improvement. “When you’re creating workflows or automation, there’s usually a lot of steps that are very human-centric, when it comes to generating data or categorizing data or whatnot,” Davis said. “And so we’ve seen a lot of those steps being not displaced by a generative AI step, but rather kind of preceded by a generative AI step.”

He gave the example of a workflow that is designed to help hiring managers create requirements for a job position. Usually the hiring manager has to go in and manually add information, like the name of the position, the description, and other requirements. But, Davis said, “If generative AI were to step in first and do a draft of that, it allows the hiring manager to come in and just make refinements.” 

Davis believes that a major challenge experienced by these low-code vendors is the added work placed on them to enable this integration to work. Low code is very declarative and abstracted away, and the constructs that make up a low-code application are proprietary to the platform it belongs to, which requires the vendors to either have their own LLM or be able to take user prompts and create all the constructs within their platform to represent what was asked. 

“There’s a lot they can leverage from existing LLMs and, and generative AI vendors, but there’s still pieces that they have to do themselves,” he said. 

Using generative AI in testing is another promising area

Combining generative AI and testing is also a promising mashup, according to Arthur Hicken, chief evangelist at testing software company Parasoft. “We’re still at a relatively early stage, so it’ll be interesting to see how much of it is real and how much of it pans out,” he said. “It certainly shows a lot of promise in the ability to generate code, but perhaps more so in the ability to generate tests … I don’t believe we’re there yet, but we are seeing some pretty interesting capabilities that, you know, didn’t exist a year or two ago.”

The field of prompt engineering — phrasing generative AI requests in a way that will provide optimal results — is also an emerging practice, which will be crucial to how successful one is at getting good results from combining things like testing or low-code with AI, Hicken said.

He explained that those who have been working with tests for years will probably have a good chance of being a good prompt engineer. “That ability to look at something and break it into small component steps is what’s going to let the AI be most effective for you … You can’t go to one of these systems and say, ‘Hey, give me a bunch of tests for my application.’ It’s not going to work. You’ve got to be very, very detailed, and like working with a djinn or a genie, you can mess yourself up if you’re not very careful about what you ask for,” he said.

He likened this to how we see people interacting with search engines today. Some people claim they can find whatever they want in a search engine, because they know the queries to ask, while others will say they looked all over and couldn’t find what they were looking for. 

“It’s that ability to speak in a way that the AI can understand you, and the better you are at that the better answer you get back … The fact that you can just talk and ask for what you want is cool, but at the moment you better be pretty smart about what you’re asking because with these AIs the emphasis is on the A – the intelligence is very artificial,” said Hicken.

 This is why testing the outputs of these systems is crucial. Hicken said that he has spoken with folks who say they are going to use generative AI to generate both code and tests. “That’s really scary, right? Now we’ve got code a human didn’t review being checked by tests that weren’t reviewed by humans, like, are we going to compound the error?”

He advises against putting too much trust in these systems just yet.  “We’re already starting to see people jump back, they’re being bitten, because they’re trusting the system too early,” he said. “So I would encourage people not to blindly trust the system. It’s like hiring somebody and just letting them write your most important code without seeing first what they’re doing.”

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The US Bureau of Labor Statistics shows a 25% increase in employment opportunities for software developers, quality assurance analysts, and testers from 2021 to 2031¹. At the same time, analyst firm Gartner predicts that more than 70% of applications will be developed using low-code by 2025². OutSystems makes the #1 Low Code Platform® on the market and has experienced an increase in developer job postings of 174% from 2021 to 2022. With AI embedded into the OutSystems platform since 2018 and the integration of generative AI underway, the need for skilled OutSystems developers is at an all-time high. Nearly 400 OutSystems developer jobs are open today.

Feedback from former program participants includes:

• “OutSystems has been a game-changer for me as a developer, with its low-code approach and intuitive visual development environment. It empowers me to rapidly create robust applications, saving me time and allowing me to focus on delivering exceptional user experiences.”
• “The platform’s collaboration features, built-in automation, and supportive community have further enhanced my development process, enabling faster time-to-market and high-quality results. I encourage all developers to explore OutSystems and experience the transformative power of low-code development.”

“The OutSystems platform is the development foundation for many of the world’s leading companies, and their need for skilled developers is climbing fast,” said Miguel Baltazar, VP of Developers at OutSystems. “AI and automation embedded into the software development process are driving an explosion of flexible, creative, business-critical job opportunities that have changed the lives and careers of OutSystems developers. Our Developer Schools are designed to upskill and reskill professionals looking to capture these opportunities, grow their salaries, and future-proof their careers. OutSystems is a growing development platform with opportunities around the world.”

OutSystems Developer School Requirements, Schedule and Structure

The OutSystems Developer School is a two-week, hands-on, online training program designed to upskill current developers experienced in traditional coding languages. Conducted 3.5 hours per day, the classes run outside of working hours and give participants an opportunity to enhance their skills using the OutSystems platform – one of the leading low-code platforms in the world. The training is fully remote and free to participants.

The Developer School program is open to developers with at least two years of experience in application development, as well as newcomers looking to enter the low-code development space. Each course has a limited capacity of 25 seats. OutSystems has trained more than 2,500 developers since 2022, and is seeing a 44% increase in the number of trained developers since the start of 2023.

Program Structure:

• Certified Trainer-Led Sessions: Participants receive comprehensive practical training through expert-led sessions, ensuring a thorough understanding of the OutSystems platform.
• Free Access to Certification Exam: OutSystems offers free access to its technology certification exam, enabling participants to earn public validation for their expertise.
• Connection to Job Opportunities: Participants will be connected with competitive job opportunities from across OutSystems’ vast ecosystem of customers and partners, providing a direct pathway to exciting career prospects.

Program Schedule and Opportunities:

• Several new editions will run throughout September and October. Developers can see the full schedule and apply to attend at the OutSystems Developer Schools site.
• While courses this term are best suited for North American time zones, developers around the world can participate in the virtual courses.
• Developers should visit the OutSystems Community site to view nearly 400 open OutSystems developer jobs.

About OutSystems

OutSystems was founded in 2001 with the mission to give every organization the power to innovate through software. The OutSystems high-performance low-code platform gives technology leaders and developers the tools to rapidly build and deploy their own business-critical applications. The company’s network spans more than 600,000 community members, 400+ partners, and active customers in 87 countries across 22 industries. OutSystems is “The #1 Low-Code Platform®” and a recognized leader by analysts, IT executives, business leaders, and developers around the world. Some of the most well-known brands use OutSystems to turn their big ideas into software that moves their business, people, and the world forward. Learn more at www.outsystems.com.

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