Category: Low-Code/No-Code

Low-Code/No-Code in Microsoft 365: How Power Platform is Redefining Enterprise DevOps

Low-code and no-code platforms have moved well beyond their early perception as tools for lightweight departmental apps. Today, they play a strategic role in enterprise DevOps, helping organizations modernize legacy systems, automate business processes, and deliver solutions at a pace traditional development models often struggle to match.

Within the Microsoft ecosystem, the Power Platform — Power Apps, Power Automate, Power BI, and Dataverse — has emerged as a mature, enterprise-grade low-code framework. Deeply integrated with Microsoft 365, Azure, and security and governance services, Power Platform is redefining how organizations think about application development and operations.

Why Low Code/No Code Matters in Enterprise DevOps

Modern enterprises face mounting pressure to deliver faster while managing growing technical debt, resource constraints, and increasingly complex compliance requirements. Traditional development pipelines often result in long backlogs of automation and application requests, especially for internal business processes.

Low-code and no-code platforms address these challenges by reducing development cycles from months to days, enabling business users to participate in solution design, and allowing IT teams to focus on architecture, governance, and scalability. However, the true value of these platforms emerges only when they are implemented within a structured DevOps and governance framework — rather than as isolated shadow IT tools.

Power Platform as an Enterprise Application Framework

What differentiates Power Platform from many low-code tools is its enterprise foundation. It is not simply a rapid app builder, but a unified platform designed to support secure, scalable, and governed application development.

Power Apps enables teams to rapidly build business applications using visual design and declarative logic. These apps commonly support approval workflows, request systems, data entry, and mobile workforce scenarios. With Dataverse and SharePoint as underlying data sources, Power Apps supports structured data models, role-based access, and enterprise authentication.

Power Automate serves as the automation backbone of the platform. Organizations use it to orchestrate approvals, synchronize data across systems, integrate SaaS and legacy platforms, and implement robotic process automation where APIs are unavailable. From a DevOps perspective, Power Automate introduces event-driven automation that reduces manual intervention and improves reliability.

Power BI completes the loop by providing real-time visibility into operational performance. When combined with Power Apps and Power Automate, analytics move beyond reporting to drive immediate action, enabling data-informed decision-making across the organization.

Low Code and DevOps: Complementary, Not Competitive

A common misconception is that low-code platforms bypass DevOps practices. In reality, they extend DevOps into areas traditionally underserved by software engineering teams.

Enterprises increasingly apply CI/CD principles to Power Platform solutions by separating environments, packaging solutions, integrating source control through Azure DevOps or GitHub, and automating deployments. Environment variables and configuration management ensure consistency across development, test, and production environments.

Governance and security are first-class concerns. Power Platform integrates with Microsoft Entra ID, Conditional Access, Data Loss Prevention policies, and Microsoft Purview. These controls allow organizations to scale low-code adoption without compromising security, regulatory compliance, or data protection standards.

AI and Copilot: Accelerating Low-Code Development

The introduction of Microsoft Copilot represents a significant shift in how low-code solutions are created. Copilot enables natural language generation of apps and workflows, assists with formula creation, and accelerates onboarding for non-technical users.

Rather than replacing developers, Copilot acts as a productivity multiplier. Architects and engineers can focus on system design, governance, and optimization while business users contribute more effectively to solution creation.

Real-World Enterprise Impact

In large organizations, Power Platform is now used to automate procurement and finance workflows, modernize SharePoint-based legacy systems, streamline external user access, and deliver executive dashboards. When implemented with proper governance, these solutions evolve into long-term enterprise assets rather than short-lived departmental tools.

Challenges and Best Practices

Despite its advantages, low-code adoption introduces new challenges. Uncontrolled app sprawl, inconsistent data models, and a lack of lifecycle management can quickly erode value. Successful organizations establish clear governance models, define environment strategies, implement Centers of Excellence, and foster collaboration between IT and business teams.

Conclusion

Low-code and no-code platforms like Microsoft Power Platform are reshaping enterprise DevOps. When combined with cloud architecture, DevOps discipline, and AI-driven assistance, they enable organizations to deliver solutions faster without sacrificing control.

The future of software development is not about replacing engineers, but augmenting them. Power Platform exemplifies this evolution, bridging business needs and technical execution in a scalable, secure, and intelligent way.

May 21, 2026
Gearset Extends Reach of DevOps Platform for Salesforce Applications

Gearset has extended the scope of the observability of Salesforce applications it provides to include software developed using low-code Flex and object-oriented Apex programming tools.

Busayo Longe, a product manager for Gearset, said Flow is gaining more traction as a tool that is more widely employed to build Salesforce applications, while more complex applications are typically built using Apex.

Gearset, a provider of a DevOps platform for Salesforce applications, is now able to surface and analyze Flow and Apex errors in real time.

Unlike other applications where issues are surfaced using alerts generated via agents embedded in an application, the Salesforce platform generates automated error emails whenever there is a failure. It’s then up to the IT team managing these applications to investigate the root cause.

As a result, IT teams can find themselves being inundated with a high volume of hard-to-interpret messages that lack context about what went wrong, who was affected, or how urgent the issue is.

In addition to providing that context, Gearset errors can be filtered by type and further investigated using a set of visualization tools provided.

IT teams can also configure the automated error emails generated by the Salesforce platform to be automatically routed to Slack or Microsoft Teams messaging platforms if they prefer.

Finally, the alerts generated by Salesforce can also be customized to enable IT teams to only be notified when an issue exceeds a specific threshold.

In general, the need for observability will become much more pronounced as more organizations build and deploy artificial intelligence (AI) agents on the platform. As the interactions between AI agents increase, the chances that there will be issues that IT teams will need to investigate will only continue to increase, noted Longe.

The challenge is that a recent Gearset survey finds 69% of the IT teams managing Salesforce applications don’t have access to an observability platform. According to Gearset, IT teams that have access to an observability platform are 50% more likely to catch bugs within a day and 48% more likely to fix them within a day, compared to teams without these tools. On the plus side, 81% of respondents said they have adopted a continuous integration/continuous deployment (CI/CD) platform. In fact, Gearset claims it added 800 new customers in 2024, bringing its total customer count to 2,900 organizations.

It’s not clear who is building the bulk of the applications deployed on the Salesforce platform. While there are a large number of so-called citizen developers building applications using low-code tools, many enterprises also rely on professional developers to build these applications. As a result, the quality of the applications deployed on a Salesforce platform can vary widely. The one certain thing is that the pace at which organizations are building custom applications that are deployed on the Salesforce platform only continues to accelerate.

The challenge that arises is that the Salesforce platform is based on a proprietary, unique mix of programming languages, formats and metadata that require a separate set of DevOps tools and platforms to manage application development and deployment.

Regardless of who or how Salesforce applications are developed, the one certain thing is that it will eventually fall to a DevOps team to troubleshoot them whenever an issue arises.

June 13, 2025
Agentic Process Automation This Way Comes to Software Development

The shift from robotic process automation (RPA) to agentic process automation (APA), prominently on display at Automation Anywhere’s Imagine conference in Orlando this week, promises significant implications for the software development community—particularly for low-code/no-code and citizen developers. Automation Anywhere, a provider of automation platforms and framework, articulated how APA, powered by agentic AI, will redefine the development landscape by providing unprecedented flexibility, intelligence and ease-of-use in application development. More than that, they are going all in on APA.

Historically, RPA has been widely employed to automate a range of routine and repetitive tasks. However, its limitations were around RPA requiring highly structured environments and precisely defined logic paths. Small deviations or variations in data inputs often meant manual intervention, limiting its scalability and efficiency in dynamic business processes.

APA, with its integration of advanced AI capabilities, overcomes these limitations. It offers a significant advancement—automation agents capable of understanding context, learning from experiences and adapting workflows dynamically. This intelligence liberates automation from rigid script-bound processes, providing immense potential to enhance productivity, especially in software development environments dominated by low-code/no-code tools.

One of APA’s most transformative impacts will be felt by citizen developers—individuals with limited or no formal coding training who build applications primarily through visual interfaces and drag-and-drop components. Historically, citizen developers faced significant hurdles as their applications became more complex, often necessitating the involvement of professional developers to handle the logic-intensive, conditional workflows beyond their skill sets.

With APA, citizen developers have access to a new class of tools—intelligent agents capable of automatically handling complex backend logic and decision-making processes. These agents don’t just automate routine tasks; they actively assist in managing more sophisticated operations typically reserved for professional developers. As a result, citizen developers can confidently create more robust, intelligent, and dynamic applications without becoming overwhelmed by complexity.

APA’s impact on low-code/no-code platforms will also be profound. These platforms have democratized application development, empowering non-technical users to address their unique business challenges rapidly. Integrating agentic automation into these platforms amplifies their capability significantly. APA agents can proactively identify patterns, suggest optimal workflows, troubleshoot issues autonomously, and dynamically adapt processes based on evolving data inputs.

Consider a typical scenario where a citizen developer creates a customer-facing app to streamline order processing. Traditionally, even low-code platforms require significant configuration to handle variations in customer behavior or unexpected data formats. APA agents, by contrast, autonomously manage these exceptions in real-time, learning from every interaction and continuously refining the process. This means less manual tweaking, fewer errors, and dramatically improved operational efficiency.

Moreover, the Imagine conference emphasized the importance of orchestration—a crucial consideration in the APA era. Organizations increasingly utilize diverse automation tools and multiple intelligent agents from different vendors. Effective orchestration ensures these various agents collaborate seamlessly, complementing rather than competing against each other. For citizen developers, simplified orchestration interfaces integrated directly within low-code/no-code platforms will be a game changer, dramatically reducing complexity and improving user experience.

This orchestrated approach mirrors strategic insights from industry giants like Salesforce and ServiceNow, who recognize orchestration as key to leveraging multi-vendor, multi-agent environments successfully. Automation Anywhere’s commitment to robust orchestration tools positions them well to lead this transformative shift.

Yet, as with any technological shift, the transition from RPA to APA must be handled strategically. Automation Anywhere’s 20 years of industry experience provides a significant advantage, enabling the company to anticipate potential pitfalls and smoothly manage the transition. Their established market credibility and deep industry relationships offer solid assurance to businesses hesitant about adopting radically new technologies.

From a broader AppDev perspective, APA will significantly influence software quality, delivery speed, and development agility. Intelligent automation agents support developers by rapidly handling mundane, repetitive tasks, freeing them to focus on more innovative, strategic initiatives. Consequently, APA facilitates faster innovation cycles, improved customer satisfaction and enhanced business agility.

As businesses adopt APA-driven low-code/no-code solutions, they’ll likely witness an acceleration in their digital transformation journeys. Citizen developers, empowered with intelligent APA agents, will increasingly contribute to critical business solutions without relying heavily on IT departments. This not only democratizes software development but also enables organizations to respond more rapidly and effectively to market demands.

In conclusion, Automation Anywhere’s strategic pivot from RPA to APA doesn’t merely represent an incremental upgrade—it heralds a new era of intelligent, flexible and powerful automation that is poised to revolutionize the AppDev landscape, empowering citizen developers and accelerating innovation.

May 16, 2025

Revolutionizing CI/CD with Low-Code/No-Code: Streamlining DevOps Workflows

Current software development paradigms are experiencing rapid transformation with CI/CD pipelines serving as core elements of modern DevOps methods. Implementing and handling CI/CD pipelines needs deep coding skills such as infrastructure understanding and a requirement for manual interventions in the traditional development setting. Meanwhile, low-code/no-code (LCNC) platforms now enable organizations to implement CI/CD using an automation system that enables people with little or no coding experience to build and modify applications.

What is Low-Code/No-Code in CI/CD?

By leveraging LCNC, end users can gain the ability to run automated software delivery procedures despite low or no programming expertise. The platforms make it easy to involve visual interfaces as well as pre-built integrations and drag-and-drop workflows to lower the requirement for custom scripting.

Low-Code CI/CD: The low-code management system combines brief manual coding methods with pre-configured modules to allow developers complete workflow customization.

No-Code CI/CD: The no-code CI/CD feature enables users to build entire CI/CD pipelines through graphical interfaces with pre-defined templates.

Benefits of Low-Code/No-Code CI/CD

Faster Deployment: The deployment process becomes faster through LCNC platforms because these platforms simplify pipeline setup and configuration.
Reduced Complexity: The automation of CI/CD processes through non-programming interfaces allows users with various skill levels such as QA engineers and business analysts to participate in development activities.
Improved Collaboration: Through LCNC platforms, non-developers can gain the ability to work on automation processes, which creates better teamwork between IT professionals and business personnel.
Lower Maintenance Costs: The use of pre-built components helps organizations lower their maintenance expenses as it reduces the requirement for custom scripts.
Enhanced Security and Compliance: The built-in security and compliance checks in many LCNC tools help organizations reduce the risks that stem from improperly configured pipelines.

Popular Low-Code/No-Code CI/CD Tools

The table below highlights some leading LCNC CI/CD tools and their key features:

Tool	Type	Key Features
GitHub Actions	Low-Code	YAML-based automation, seamless GitHub integration
CircleCI Orbs	Low-Code	Reusable packages, simplified pipeline configuration
Jenkins Blue Ocean	Low-Code	Visual pipeline editor, streamlined Jenkins workflows
Zapier	No-Code	Automates deployment-related tasks with drag-and-drop workflows
n8n	No-Code	Workflow automation with open-source extensibility
Harness	No-Code	AI/ML-powered deployment automation, security compliance

Challenges and Considerations

While LCNC in CI/CD offers numerous benefits, there are challenges to consider:

Customization Limits: Generalized workflow customization may still need scripting alongside custom plugins for overly complex procedures.

Vendor Lock-In: The use of proprietary solutions renders businesses to become limited in terms of flexibility as they develop increased dependability on those solutions.

Security Concerns: The monitoring of automated workflows must be done with care to prevent security vulnerabilities.

Scalability: Certain LCNC tools demonstrate restrictions when dealing with extensive enterprise requirements.

Conclusion

Low-code/no-code solutions are democratizing CI/CD automation, making DevOps more accessible to non-technical users while enhancing efficiency for developers. These platforms enable teams to build, evaluate and deploy applications with minimal manual intervention, allowing organizations to focus on innovation rather than infrastructure complexities. By reducing reliance on extensive coding expertise, LCNC solutions empower a broader range of users, including product managers and QA engineers, to contribute to the software development lifecycle.

As these platforms continue to mature, organizations must strike a balance between ease of use and the need for customization, ensuring they select the right tools for their specific CI/CD needs. While LCNC tools offer intuitive workflows and pre-built integrations, businesses must assess their long-term scalability, security and adaptability to evolving DevOps requirements. Selecting the right combination of tools can help organizations streamline operations, reduce technical debt and enhance overall productivity.

The future of software delivery is faster, simpler and more inclusive — thanks to LCNC in CI/CD. As automation and AI-driven capabilities become more prevalent, these solutions will further refine deployment processes, optimize performance and minimize errors, enabling companies to accelerate digital transformation and maintain a competitive edge in an ever-evolving market.

March 5, 2025

Scaleups are Turning to Low Code to Build Modern Internal App Ecosystems

For most engineering teams, internal tools are indispensable but notoriously resource-intensive. Whether it is admin panels for configuration, dashboards for analytics or operational systems, these tools demand continuous updates, customizations and maintenance. Due to the lack of developer capacity, long implementation times and the buildup of technical debt from hardcoded non-scalable solutions, traditional development methods frequently result in bottlenecks. That being said, low-code platforms are transforming this narrative. Choosing the right low-code platform for your team could mean enabling faster development, empowering junior developers and reducing the maintenance burden. Drawing from experiences shared by engineering leaders at PhonePe, Yubi and DronaHQ, this article explores how engineering leaders are adopting low code to reshape the development of internal tools and drive operational excellence.

Why Low-Code?

Addressing Legacy Issues and Bottlenecks

For any engineering team, dealing with legacy code and configurations can easily be a recurring theme. Shikhar Kapoor, Software Architect at PhonePe, described how a compliance-driven audit forced the team to update legacy consoles across the organization, “Low code is not just about speed; it is about letting developers move forward without being tied to the past. A developer should not be dragged back into legacy work. They should be solving new problems, not reconfiguring old ones.”

For a firm where compliance is paramount and scalability is a given, the mindset shift was essential. By centralizing updates through low code, PhonePe has avoided the time drain of manually updating multiple consoles, directly impacting the developers’ productivity.

Enabling Reconfigurability for Scalability

Software, ready-to-use or custom-built, will create challenges as your firm scales. Dinesh Kailash Kumar, VP of engineering of Yubi, shared how building hardcoded solutions for every new customer became unsustainable, “We discovered that building custom solutions is wasteful in terms of effort because you are tied and cannot scale. Configurability is key. With low code, we have been able to package configurations, enabling teams to build custom solutions that scale effortlessly.”

Yubi’s approach to low code focused on creating a headless, configurable architecture. This shift empowered teams to dynamically adjust workflows without needing developer intervention for every change. By enabling non-developers to make updates, Yubi significantly reduced their operational overhead and improved scalability.

Reducing Turnaround Times With Low-Code

Kapoor shared how adopting low code at PhonePe compressed project timelines from three months to just three weeks. “This is not just about efficiency; it is about creating a responsive and agile engineering environment,” he said.

By identifying bottlenecks in their traditional development process, PhonePe introduced low-code to centralize configuration management, streamline workflows and improve responsiveness to both internal and external requirements.

Overcoming Resistance and Building Trust

Adopting new technologies, especially something as fundamental as low-code, often meets resistance from within the organization. Developers accustomed to traditional workflows may view low code as a compromise on quality. To address this, Kapoor described a unique strategy PhonePe employed during a company-wide hackathon. “We established our low-code tool and encouraged teams to use it for their projects. Instead of pairing up with a UI developer, we asked them to try building front ends themselves. The hands-on experience helped evangelize the tool and build trust in its capabilities.”

Surprisingly, backend engineers adapted to low-code tools faster than front-end developers. Kapoor noted, “Backend engineers had fewer preconceived notions about how UI should be built, making them more open to experimentation.”

Choosing the Right Time to Invest in Low Code

When should you consider low code? Jinen Dedhia, co-founder of DronaHQ, suggested that the best time is when workloads begin to bottleneck, “There is always a point where you can see your workloads increasing. That is when you realize your team is becoming a bottleneck for growth.”

For organizations like Yubi, this realization came as they scaled rapidly and needed a solution that would not require constant developer intervention. By transitioning to low-code, they were able to address scaling needs without expanding their engineering headcount.

Enabling Customization Without Overburdening Developers

Customization often slows progress in traditional development, as developers must revisit and reconfigure settings repeatedly. Jinen explained how low code disrupts this cycle, “Building configurable platforms that do not rely on developers for every change means faster turnaround times and happier clients. With low-code, developers can move on to more complex problems without being dragged back into configuration tasks.”

Kapoor shared how low-code tools enabled product managers to make changes independently. “The introduction of a WYSIWYG editor meant product managers could sit alongside developers and make real-time changes to dashboards, reducing back-and-forths and shortening delivery timelines,” he said. Interestingly, this empowerment also encouraged some non-technical team members to learn basic JavaScript for further customizations.

The Future of Low Code in Scaling Engineering Teams

Looking ahead, low-code platforms are becoming integral to tech stacks. Kapoor shared PhonePe’s vision for expanding low-code usage, “We are looking to integrate low-code even further by making control panel features a part of our design stage, not just an afterthought.”

Yubi is exploring ways to templatize configurations, ensuring that 80% of a customer’s needs are pre-built. Dedhia explained, “By making tools modular and reusable, we can handle diverse requirements without starting from scratch, enabling faster and more consistent rollouts.”

Is Low Code Right for Your Organization?

Low-code platforms are not just about efficiency gains; they enable engineering teams to innovate, reduce repetitive tasks and scale solutions seamlessly. The decision to adopt low code should align with an organization’s growth trajectory and scalability needs. As Jinen advised, “Identify the bottlenecks, focus on scalability and do not underestimate the importance of training and education. Low-code platforms can be transformative, but only if the team is ready to embrace the change.”

January 28, 2025
Transforming the Mainframe Developer Experience With Generative AI: A Day in the Life of a Developer

Step into the world of Alex — a seasoned mainframe developer facing the formidable challenge of navigating through the labyrinth of complex, multiple-module code. Every day, Alex is tasked with deciphering the intricate logic embedded within legacy systems, where decades-old applications coexist with modern demands for agility and innovation.

As Alex settles into her workstation, she is met with the familiar sight of lines upon lines of code stretching endlessly across the screen. Each line represents a piece of the organization’s legacy, a testament to years of development and refinement. But beneath the surface lies a tangled web of dependencies and a legacy burdened by its own complexity.

Today, Alex’s mission is clear — to make changes to a critical module that underpins a key business process. However, the task is far from straightforward. The codebase, a sprawling behemoth of interwoven logic, lacks documentation and comments, making it a daunting challenge for even the most experienced developers. It could take Alex at least a day to understand what she is even looking at, before starting any real work.

Moreover, the organization’s prevailing “fear of change” culture about mainframe code — where management and developers alike are hesitant to make any modifications to the monolithic code out of fear of inadvertently breaking something — looms large over Alex’s efforts. It also stifles innovation and progress, leaving an organization in stagnation.

Generative AI — Transforming the Mainframe DevOps Ecosystem

But Alex doesn’t back down from a challenge. Armed with determination and the latest tools, she sets out to tackle the seemingly insurmountable task. Enter generative artificial intelligence (GenAI), a revolutionary technology that promises to transform how developers like Alex interact with the mainframe applications and improve the developer experience.

GenAI is fast becoming the quintessential solution for augmenting the application developer journey in mainframe DevOps due to its innate capacity to comprehend, guide and enhance code-related tasks. Unlike traditional AI methodologies, which primarily focus on system optimization and anomaly detection, GenAI transcends these boundaries by actively participating in the software development lifecycle.

GenAI is Alex’s guiding light through the maze of complexity. With its advanced natural language processing (NLP) capabilities, it offers unparalleled insights into the inner workings of the codebase, unraveling its mysteries with ease. Gone are the days of painstakingly deciphering undocumented logic; with GenAI, Alex can now navigate the codebase with confidence and clarity.

One of the most powerful features of GenAI is its code explain functionality. A simple right-click lets Alex access detailed explanations of any line of code, revealing its purpose and functionality. It’s a game-changer for Alex, alleviating her fears about making changes to a monolithic codebase and allowing her to get to work right away instead of wasting a day analyzing the code.

GenAI’s impact goes beyond code comprehension, review, and testing. It also plays a crucial role in ensuring coding standards and best practices adherance. With real-time feedback and suggestions, GenAI helps Alex maintain consistency and compliance across the codebase, reducing the risk of introducing errors or vulnerabilities.

GenAI — Instilling Confidence in Both Mainframe Developers and Management

As Alex delves deeper into the codebase, she uncovers opportunities for optimization and improvement, with GenAI, an invaluable ally, providing insights and recommendations that help streamline the development process and enhance the performance of critical business applications.

Perhaps most importantly, GenAI empowers Alex to challenge the prevailing “fear of change” within the organization. With its ability to provide clear and concise explanations of code changes, GenAI gives Alex the confidence she needs to help the organization shift toward a culture of innovation and progress.

As the day draws to a close, Alex reflects on the transformative impact of GenAI on her work. What was once a daunting and arduous task has become a journey of discovery and empowerment. With GenAI by her side, Alex feels emboldened to tackle even the most daunting challenges, knowing she has the tools and support she needs to succeed.

Key Use Cases of GenAI to Improve the Mainframe Developer Experience

1. Explain What the Code Does: GenAI offers in-depth explanations of code snippets and programs. Leveraging sophisticated NLP methods, it seamlessly integrates comments into code, improving readability and fostering collaboration among developers. This automated process enhances understanding and streamlines the development workflow, ensuring that developers can grasp the intricacies of the codebase faster and more efficiently, which helps accelerate the development cycle.
2. Receive Immediate Feedback on Code Standards: GenAI plays a pivotal role in code reviews, assisting developers with real-time feedback, guidance, and remediation suggestions that promote consistency and compliance across applications.
3. Improve the Delivery of Your Changes: GenAI can significantly streamline the code delivery process. By facilitating quicker root cause isolation and enhancing the resiliency and performance of continuous integration and continuous delivery (CI/CD) toolchains, it expedites the mean time to resolution (MTTR).
4. Give Every Developer a Personal Assistant: GenAI functions as an indispensable virtual coding assistant, equipped with extensive knowledge of best practices, design patterns, and syntax. By seamlessly integrating into developer workflows, it provides contextual recommendations and promotes adherence to industry and organizational standards, enhancing developer productivity and code quality.
5. Efficient Testing with Minimal Viable Data: GenAI revolutionizes the testing paradigm by intelligently analyzing source code and devising optimal testing and test data practices.

Paving the Way for a Future of Mainframe Innovation

In conclusion, the integration of GenAI represents a paradigm shift in improving the mainframe developer experience. By providing unparalleled insights into complex monolithic codebases, GenAI empowers developers to navigate the complexities of legacy systems with confidence and clarity. Moreover, by challenging the prevailing culture of “fear of change,” GenAI paves the way for a future of innovation and progress within the organization. As Alex looks toward the future, she does so with optimism and excitement, knowing that GenAI will continue to revolutionize how she works for years to come.

June 21, 2024
ContextQA Turns to IBM for AI to Automate Testing

ContextQA is integrating the IBM watsonx.ai platform for building, accessing and deploying artificial intelligence (AI) models into its low-code/nocode automation platform for testing the front end of applications.

Previously, ContextQA had initially relied on Amazon Web Services (AWS) but decided to switch to IBM to reduce costs.

ContextQA CEO Deep Barot said in addition to providing better support, IBM is working more closely with ContextQA to expand the number of use cases where AI can be applied.

Ultimately, the goal is not so much to increase the number of tests being run as much as it is to make it simpler for DevOps teams to run the right test at the right time, he added. Ultimately, AI models should be able to run 80% of the most common tests, leaving DevOps teams more time to run additional use case tests that previously might never have been run, noted Barot.

In theory, AI models should improve the overall quality of the applications being deployed. The issue, however, is not only making sure that more applications are thoroughly tested but also making certain tests are conducted in the right order. In effect, DevOps teams need to be able to leverage different classes of AI models to orchestrate smarter testing processes within the context of a larger DevOps workflow.

The more complex the application being built and deployed the more critical that orchestration capability becomes, said Barot. That’s especially critical because as more tests are conducted using AI models the overall cost of testing will tend to increase, he added. In addition, advances in AI should make it simpler to build and deploy more applications than ever, all of which should be thoroughly tested before being deployed in a production environment.

It’s not clear for how long DevOps teams are going to be interested in which specific AI models are being employed to perform a specific task, but for now, most IT teams are keenly interested in knowing which LLMs they might be exposing sensitive data to as security and compliance issues arise, noted Barot.

Identify Opportunities for Automation Enabled by AI Now

It may be a while before AI is pervasively applied across application testing but it’s now more a question of when rather if. Historically, any time a DevOps team fell behind schedule there was a natural tendency to reduce the amount of time allocated to testing. The trouble is over time that reduction in testing eventually results in an overall reduction in software quality, in an era where end users are less tolerant of bugs and flaws.

Hopefully, one day soon the number of issues that DevOps teams need to regularly troubleshoot will decline as the quality of the software being deployed improves. In the meantime, DevOps teams should evaluate existing DevOps workflows to identify opportunities for automation enabled by AI. It’s not so much that AI is going to replace the need for DevOps professionals any time soon, as much as it is determining which tasks to offload to machines in a way that enables DevOps teams to build and deploy applications at a level of unprecedented scale.

June 11, 2024
No Country for No-Code: Are We Heading Towards a Wild West of Software Security?

Nearly everything we use is built on code, from cars to smart fridges to doorbells. In businesses, countless applications keep devices, workflows and operations running. So, when early no-code development platforms launched in 2010, promising more accessible application development for citizen developers, its success felt inevitable.

It’s hard to deny the success of no-code. These platforms flatten the learning curve for would-be developers, allowing organizations to innovate and automate with useful applications despite a developer skills shortage. Plus, the out-of-the-box applications offered by no-code platforms expedite the application development process in a world where speed-to-market is king. Last year, Forrester found that 87% of enterprise developers use low-code and no-code tools or platforms for at least some of their workload.

But functionality is not the only sign of success. Unintentionally, the same trends that pushed for application development to be democratized have led to a wild west of insecure applications and misconfigurations that expose a whole host of organizations to cyberthreats.

The Importance of Security

While these platforms democratize development, they must be used with caution. The OWASP Top 10 highlights factors such as misconfiguration and using vulnerable components as common security threats. Yet, a reliance on no-code development could introduce un-spotted vulnerabilities directly into an organization.

Forrester has long warned of the risk of no- and low-code, featuring the vulnerability in its predictions for the coming years. The spectrer of an untrained employee creating applications is especially alarming: These platforms empower employees with no application security knowledge to develop programs that security teams are often unaware of.

Organizations must gain real oversight into who is responsible for developing software, whether professional developers leveraging no-code platforms as tools, or citizen developers creating applications for smaller teams and projects. It is no secret that CVEs are rising sharply. They hit a record 28,092 last year and are projected to increase by 25% throughout 2024. Last December, Microsoft revealed a high-severity CVE that affected low-code and no-code users.

When businesses are facing a tide of new exploits each day, skills such as vulnerability detection and remediation are critical to any new software development project.

Software development needs to become more flexible in its roles, but never at the expense of security. By fostering a culture of “security by design” across the organization, security leaders can ensure that all roles in the software development lifecycle (SDLC) understand their responsibilities in their security posture — including citizen developers.

Empowering Human Intervention

Just one in five organizations are confident in their ability to detect a vulnerability before an application is released, according to Security Journey research, meaning that the security knowledge in most SLDCs is insufficient. Developers need to be trained to create secure software and to sniff out insecure code in the rest of the code base – responding and fixing it quickly – before it gets to production. Without this, application security and security teams are left with an unnecessary burden, which ultimately requires more time, expense, and potential for business risk. Securing vulnerabilities after the fact, through regular security scanning and patching, should not be the norm.

Instead, organizations have an opportunity to empower developers with security skills and knowledge throughout their careers. These skills allow them to deliver a higher quality of output and play a vital role in securing the organization itself. And yet, just one in three organizations (36%) train developers to write secure code, which ensures that software security starts at the end of the development process 64% of the time. The same goes for citizen developers, whose work often shows a passion for problem-solving and innovation that should be harnessed and supported with the right training.

A Problem for the Future

It would be reductive to suggest that software security risk is rising purely because of no-code and low-code platforms, or even that citizen developers are a majority contributor to CVEs. The very culture of software development has been forcing the industry down a path of speed and ease over security for almost two decades. The surge in AI-generated coding proves this more than anything.

So long as developers and the project managers above them are incentivized to move fast, each new solution will be valued based on its ability to create functional code quickly. This will be the case until security is made a priority by regulators and, importantly, by executives and the board.

The cultural disconnection at the heart of the software security crisis is human-made. No matter which tool is in the spotlight, building secure software will require human solutions. While large language models (LLMs) aren’t yet equipped to tackle the complex considerations that developers face daily, the biggest risk will come from complacency, overreliance and blind trust from their users. Human developers, equipped to perform rigorous code reviews and remediate any vulnerabilities they find, are central to the safe implementation of any of these technologies, whether no-code, low-code or AI-generated. Without this, the tools run the risk of introducing more vulnerabilities with less critical supervision.

No matter what tools arise, knowledgeable human users will always need to understand what the tool is providing and can act as a stopgap for quality and security.

May 14, 2024
Spotify to Provide Opinionated Instance of Backstage IDP

Spotify today announced it is making commercially available an instance of its internal developer portal (IDP) based on the open source Backstage platform the company earlier contributed to the Cloud Native Computing Foundation (CNCF).

Pia Nilsson, senior director of engineering for Spotify, said Spotify Portal for Backstage, available as a private beta today, provides platform engineering teams with low-code/no-code framework for setting up an IDP.

In addition, Spotify is now making enterprise support available for organizations that adopted Backstage, along with additional plug-ins that simplify integrations with tools and platforms from third-party vendors such as New Relic, Atlassian, Snyk and Datadog.

Finally, Spotify is adding capabilities to manage hackathons and surface usage insights. A forthcoming plug-in also promises to make it possible to add data entities to a software catalog residing in Backstage.

Backstage has been gaining traction as an IDP mainly because it provides an extensible framework for centralizing the management of developer environments.

Spotify Portal Implementation

Some organizations have found it challenging to build and deploy Backstage. So Spotify is now making available a more opinionated Spotify Portal implementation that is simpler to deploy, configure and manage using templates, noted Nilsson.

As applications have become more challenging to build there is more focus on improving developer productivity. At the same time, IDPs enable platform engineering teams to streamline the number of tools being employed in a way that serves to reduce the total cost of software development.

Many developers want to add tools as they best see fit, so DevOps teams need to find a way to strike a balance between reducing the cognitive load developers encounter and what could become an overly heavy-handed approach to centralizing DevOps workflows. If talented developers start heading for the exits because they don’t like the experience being provided, IT leaders will find it difficult to replace them.

Giving DevOps Teams More time

The hope is that IDPs, when used within the context of platform engineering, will give developers more time to focus on writing business logic. However, application development is as much art as it is science. Just because developers have more time it does not necessarily follow that the insights and inspiration needed to write code automatically ensue. IDPs and platform engineering, at the very least, however, create the opportunity for developers to write more business logic faster. The degree to which that occurs naturally varies from one organization to the next.

Each organization will need to decide to what degree to embrace platform engineering, but regardless of their level of commitment IDPs can play a critical role in making it simpler to onboard developers to new projects, noted Nillson. In addition, an IDP makes it simpler for DevOps teams to track metrics such as the rate at which code is being updated, she said.

IDPs, of course, are not necessarily ideal but with the rise of Backstage there is clearly now more standardization, as DevOps teams continue to seek to eliminate as much friction as possible.

April 30, 2024
Exploring Low/No-Code Platforms, GenAI, Copilots and Code Generators

The emergence of low/no-code platforms is challenging traditional notions of coding expertise. Gone are the days when coding was an exclusive skill set reserved for a knowledgeable few. Low/no-code platforms have democratized software development. They empower individuals from non-IT or technical backgrounds to translate their business ideas into applications without the need to master complex programming languages.

These low/no-code platforms, with their user-friendly drag-and-drop interfaces and pre-built components, have simplified application development. Imagine building a smart home application with just a few clicks and drags — low/no-code platforms are making it possible.

Add generative artificial intelligence (Gen AI) into the mix, and the new development practices change the field remarkably.

The Impact of Gen AI on Low-Code Platforms

Expect low-code platforms to evolve further, becoming more intelligent, adaptable and seamlessly integrated into the software development lifecycle. There will be a shift towards platforms that use Gen AI to understand user intent, automate routine tasks and generate complex code snippets.

Gartner predicts that, by 2026, over 80% of enterprises will use GenAI APIs and models or deploy GenAI-enabled applications in production environments, up from less than 5% in early 2023.

Combining low-code and Gen AI will empower developers by automating routine tasks and allowing them to focus on high-level logic and creativity. Gen AI acts as a multiplier that can accelerate and optimize developmental operations.
McKinsey estimates that generative AI could add $2.6 trillion to $4.4 trillion annually to the global economy by 2040.

Another Tool: AI Copilots and Code Generators

Another promising application for AI large language models (LLMs) is AI-powered assistants. Enter copilots and code generators — AI assistants that take the hassle out of coding.

While low/no-code platforms have transformed application development, copilots and code generators add an extra layer of efficiency.

AI copilots use a chat interface to simplify coding through a chat interface, where developers articulate their code requirements in natural language prompts, and the copilot generates the code accordingly. These assistants provide real-time assistance, offering contextual suggestions and code snippets, and they can anticipate potential bugs. Moreover, they can explain code blocks, generate unit tests and propose bug fixes, which enhances the coding process.
AI copilots are like trusted assistants, ready to aid you with specific coding tasks or generate complex code – and they do it swiftly.

The Big Question

As a result of low/no-code platforms gaining traction, the skill sets required in the tech industry will transform. Gone are the days when proficiency in multiple programming languages was a must-have.

With low/no-code tools taking center stage, does this mean coding is becoming obsolete?

A big no.

While basic programming knowledge remains valuable, it’s no longer the sole requirement for building innovative applications. With low/no-code platforms democratizing application development, individuals from diverse backgrounds can leverage technology to create impactful solutions.

Do GenAI, copilots, and code generators render low/no-code platforms obsolete?

Again, no.

While GenAI, copilots, and code generators excel at certain specific tasks, low/no-code platforms offer a holistic solution, combining visual interface design, database integration and more. They’re the Swiss army knives of development, catering to a wide range of needs beyond just code generation.

Gen AI, copilots and code generators are not here to replace low/no-code platforms or traditional coding practices. Instead, they are positioned to complement and enhance them. The fusion of these technologies holds great promise in simplifying software development, boosting efficiency, and unlocking new avenues for innovation.

The Final Word

With better code suggestions and automated testing capabilities, low/no-code platforms deliver high-quality solutions that meet the demands of today’s tech-savvy users.

With the thrilling combination of low/no-code platforms and GenAI, the future of software development is promising. While these advancements may disrupt traditional coding, they also present opportunities for innovation and creativity.

It’s time for us to embrace this change and build a brighter tomorrow, one line of code — or lack thereof — at a time.

April 18, 2024

Category: Low-Code/No-Code

Why Low Code/No Code Matters in Enterprise DevOps

Power Platform as an Enterprise Application Framework

Low Code and DevOps: Complementary, Not Competitive

AI and Copilot: Accelerating Low-Code Development

Real-World Enterprise Impact

Challenges and Best Practices

Conclusion

What is Low-Code/No-Code in CI/CD?

Benefits of Low-Code/No-Code CI/CD

Popular Low-Code/No-Code CI/CD Tools

Challenges and Considerations

Conclusion

Why Low-Code?

Addressing Legacy Issues and Bottlenecks

Enabling Reconfigurability for Scalability

Reducing Turnaround Times With Low-Code

Overcoming Resistance and Building Trust

Choosing the Right Time to Invest in Low Code

Enabling Customization Without Overburdening Developers

The Future of Low Code in Scaling Engineering Teams

Is Low Code Right for Your Organization?

Generative AI — Transforming the Mainframe DevOps Ecosystem

GenAI — Instilling Confidence in Both Mainframe Developers and Management

Key Use Cases of GenAI to Improve the Mainframe Developer Experience

Paving the Way for a Future of Mainframe Innovation

Identify Opportunities for Automation Enabled by AI Now

The Importance of Security

Empowering Human Intervention

A Problem for the Future

Spotify Portal Implementation

Giving DevOps Teams More time

The Impact of Gen AI on Low-Code Platforms

Another Tool: AI Copilots and Code Generators

The Big Question

The Final Word