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Empower Developers to Preserve Human Agency in Today’s World of Automation
A pressing issue facing the tech industry is how to strike the right balance between humans and technology regarding AI.

As we know, AI is transforming industries, streamlining processes and bringing about significant advancements, with developers propelled to the forefront of such innovation and responsibility.

Yet, while AI and automation unlock significant opportunities, they also introduce unique challenges that reinforce some irreplaceable values such as human creativity, critical thinking and decision-making abilities. This ensures the benefits of AI are harnessed in a controlled, ethical and secure manner — and education is critical to empowering developers to navigate this.

The Growing Influence of AI in Software Development

AI tools and automation have already substantially transformed software development, enabling tasks to be completed faster and more efficiently. Whether automating repetitive codes or enhancing debugging with intelligent algorithms, developers now have a powerful tool at their fingertips. Nevertheless, these advancements come with challenges:
- Security Risks: Though automated tools can speed up numerous processes, they may inadvertently introduce detrimental vulnerabilities if misused.
- Ethical concerns: AI systems often reflect the biases of their creators. To avoid this, developers must build with fairness and inclusivity in mind.
- Human Oversight: With automation growing, developers must retain control over all critical decision-making processes.
Developers’ role in designing and maintaining secure, ethical and resilient systems is critical. While AI is an incredible tool and assistant, developers must ensure that systems are built with integrity and aligned with human values.

The Importance of Educating Developers

Without proper and continuous education, developers would not have the necessary foundation to leverage AI effectively or responsibly. In today’s world, where automation substantially reduces tasks to algorithms, ongoing learning helps developers:
- Strengthen Secure Code Practices: Even though AI can assist in writing code, a well-educated developer will be able to identify and mitigate security vulnerabilities.
- Understand the Ethical Implications of AI: Education equips developers to question biases and consider the broader social impact of the technologies they create.
- Adapt to Evolving Technologies: The speed of technological change means developers must continuously learn to stay ahead of threats and opportunities.
- Retain Human Agency: Developers maintain control and accountability over the systems they design by understanding the tools they use and their limitations.
Secure Code Matters

Secure code matters as it protects applications from potential vulnerabilities and malicious attacks. Organizations can promote secure practices by empowering developers with secure coding education, which goes beyond simply teaching basics. This could include:
- Practical Coding Exercises: Providing hands-on practice, enabling gaps between theory and real-world application to be filled.
- Focus on Secure Development: Training should not just teach developers how to code securely but also help them think critically about the impact of their work.
The Future of AI and Education

AI should not replace human intelligence but rather enhance it. When combined with education, AI has tremendous power to drive innovation while simultaneously preserving security, fairness, control and accountability. This intersection facilitates responsible innovation aligned with organizational values, with developers creating systems that serve humanity, not the other way around.
February 18, 2025
Open Source Software Security Concerns with Spike Curtis

Spike Curtis, principal engineer for Coder Technologies, dives into why open source software security concerns are valid, and why the only viable option is to invest more in securing software supply chains to mitigate potential threats. While open source benefits from community oversight, organizations must vet the projects they rely on to avoid security gaps.

Curtis also takes a look at AI’s role in software security. While AI has potential for detecting vulnerabilities, current tools aren’t yet reliable for systematic security checks. He warns that AI-generated code can introduce more security flaws if developers prioritize speed over careful review. Addressing concerns about enterprise contributions, he notes that while some companies actively support open-source projects, many simply consume without giving back. Organizations depending on underfunded tools should consider dedicating resources to their maintenance rather than assuming the community will sustain them.

February 6, 2025
Objective-Based Testing: Transforming Test Design With Natural Language Prompts
In the rapidly evolving world of software testing, objective-based testing, a revolutionary approach, is gaining traction. This innovative methodology promises to streamline your testing process, improve communication among stakeholders and ensure your tests truly align with your software’s goals.

Objective-based testing is not just another buzzword to add to your LinkedIn profile, it is a mind shift and a way to make testing more intuitive and comprehensive.

What is Objective-Based Testing?

Instead of diving headfirst into a sea of technical jargon, we start with a simple, clear objective. It’s like telling your GPS you want to go to ‘that place with the amazing tacos’ instead of rattling off coordinates. It is all about using natural language prompts to determine the goal to map out your testing journey.

Objective-based testing is a paradigm shift in how we initiate and design test cases. At its core, this approach uses natural language prompts that describe the objective or goal of a feature to automatically generate or guide the creation of a set of tests.

Instead of starting with technical specifications or predetermined test cases, objective-based testing begins with a clear, concise statement of what the software should achieve. This objective, expressed in natural language prompts, serves as the foundation for deriving relevant test scenarios.

For example, an objective might be: ‘Ensure users can complete a purchase with various payment methods’. This simple prompt would then initiate the creation of multiple test cases, covering different payment scenarios, error conditions and user flows.

Why Objective-Based Testing Will Change Your Testing Life

1.     Bridging the Communication Gap

Using natural language prompts, objective-based testing creates a common ground for all stakeholders. Developers, testers, product managers and non-technical team members can easily understand and contribute to defining test objectives.

2.        Ensuring Relevance and Coverage

When tests are derived from clearly stated objectives, you’re more likely to cover all crucial aspects of functionality. This approach naturally leads to more comprehensive test coverage that’s directly tied to your software’s intended purpose.

3.        Adaptability and Scalability

As your software evolves, you can quickly adapt your testing strategy by modifying or adding new objective prompts. This flexibility allows your testing to scale effortlessly with your product’s growth. New feature? New objective. Boom. Done. No need to rewrite your entire test suite every time.

4.        Improved Test Automation

Imagine whispering your heart’s desires to an AI and having it spit out a fully formed test suite. Natural language objectives can be used to guide automated test generation, leveraging artificial intelligence (AI) and machine learning (ML) to create more intelligent and context-aware test suites.

5.        Focus on User-Centric Quality

By focusing on objectives, you’re inherently putting yourself in the users’ shoes. This ensures that your testing efforts are always aligned with delivering value to end users.

Implementing Objective-Based Testing

To adopt this transformative approach:

Start with Clear Objectives: Work with stakeholders to articulate clear, concise objectives for each feature or functionality in natural language.

Use Objectives as Prompts: Utilize these natural language objectives as prompts to initiate the test design process or guide automated test generation.

Expand Test Scenarios: From each objective, derive multiple test scenarios that cover various aspects of the stated goal.

Iterate and Refine: Continuously refine your objectives and resulting test cases based on feedback and new insights.

Integrate with Existing Tools: Find ways to incorporate objective-based prompts into your current testing tools and frameworks.

Practical Example

Objective: ‘Ensure users can easily reset their password and regain account access’.

This single objective could spawn the following test cases:
- Verify the password reset link is sent to the registered email
- Test the password reset with valid and invalid tokens
- Ensure the new password meets security requirements
- Ensure users can log in with the new password
- Test edge cases such as expired reset links or multiple reset requests
Conclusion

Objective-based testing, with its focus on natural language prompts to initiate test design, is more than just a new technique. It is a fundamental reimagining of how we approach software quality assurance. By starting with clear, user-focused objectives, we can create more relevant, comprehensive and adaptable test suites.

As the complexity of software systems continues to increase, those who embrace objective-based testing will find themselves better equipped to ensure quality, improve stakeholder communication and deliver software that truly meets user needs. The future of testing is objective-driven. Are you ready to transform your testing life?
November 26, 2024
CortexClick Leverages Generative AI to Automate Software Documentation

CortexClick today launched a content generation platform based on large language models (LLMs) that have been specifically trained to create documentation, tutorials and technical blog posts, including screenshots, for organizations building software.

Company CEO Evan Boyle said the overall goal is to make it simpler for organizations to create documentation using AI agents trained using multiple models that are capable of understanding the functions of the underlying code that has been created.

While there is no shortage of AI writing tools, the software-as-a-service (SaaS) platform created by CortexClick goes a step further by providing access to multiple agents that challenge the output created, said Boyle. In effect, one agent acts as an editor that challenges the veracity of the content created by another agent, he noted.

Additionally, CortexClick has provided integration with the widely used GitHub software repository in addition to making available a developer application programming interface (API) and software development kit (SDK) to make it simpler to integrate other platforms.

The company in addition to providing access to governance tools makes it possible to require the output generated to adhere to specific guidelines, such as the brand voice of the organization or other rules that organizations define, said Boyle.

Few developers and the DevOps teams that support them enjoy creating documentation. One of the issues that often makes technical support challenging is the lack of available documentation showing how an application functions. In the absence of that documentation, IT teams are forced to create support tickets that inevitably developers wind up needing to respond. Every minute spent responding to those requests is, of course, that much less time that could have been spent on writing code.

The CortexClick platform jumpstarts the documentation process by rather than requiring developers to start with a blank page instead generate content they can edit as they need, said Boyle. That’s critical because many developers are not naturally gifted writers who especially enjoy creating expansive amounts of content, he added. In many cases, the current level of documentation provided by software development teams today is so sparse as to be nearly useless.

At the same time, marketing teams that may not have much technical expertise can use the platform to also create technical content that development teams by more easily review, noted Boyle. That content in a world where most sales of software is driven by online engagement is critical, he added.

It’s not clear how many application development teams are already using AI tools to generate documentation. The challenge is most of those general-purpose platforms have limited to no understanding of technical jargon.

Of course, being able to generate more documentation doesn’t automatically make it useful. Developers will still need to carefully review documentation before sharing it with organizations that are consuming their software, However, with any luck the number of technical support calls that DevOps teams wind up fielding should decline as documentation becomes more accessible.

In the meantime, it’s become clear documentation has emerged as one of the best use cases for generative AI. The challenge is now getting developers to focus more on providing better documentation using AI tools that should reduce the level of drudgery far too many associated with performing that task today.

September 18, 2024
Study Finds No DevOps Productivity Gains from Generative AI

A study of 800 software developers working on large engineering teams that have adopted the GitHub Copilot generative artificial intelligence (AI) tool finds limited gains in productivity are being achieved.

Conducted by Uplevel Data Labs, a provider of a software engineering intelligence platform, the study of a test and control group published today also notes that the number of vulnerabilities being created by developers using GitHub Copilot increased.

Specifically, developers using GitHub Copilot have not seen any improvement or decrease in pull request (PR) cycle time or overall throughput in terms of the number of pull requests merged.

Developers, however, have experienced a 41% increase in bugs within pull requests.

Matt Hoffman, a product manager and data analyst at Uplevel, said those results suggest that overall code quality is being adversely impacted by an AI tool that was trained using code of varying quality that OpenAI collected from across the Web. GitHub Copilot is based on a large language model (LLM) originally developed by OpenAI that GitHub licenses through its parent company Microsoft.

At the same time, the study suggests that burnout as measured by the amount of time developers spend working outside of standard hours is decreasing. However, that reduction isn’t attributable to generative AI because both the control and test groups saw a decline in the amount of time spent working outside of standard hours, with those that did not have access to GitHub Copilot seeing a 28% reduction compared to a 17% decline for developers that did have access.

It’s still early days when it comes to using generative AI tools to write code. The first generation of these tools is steadily going to be replaced by another wave of these tools that have been trained using code that has been vetted for quality. In addition, those tools will also take advantage of more advanced reasoning engines and AI agents to automate workflows.

In the meantime, however, it’s clear that human developers need to scrutinize the code being generated by the first generation of these tools closely. In addition to injecting more vulnerabilities into code, the code being generated may not run as efficiently as code created by a professional developer, resulting in increased costs.

Of course, generative AI tools also make coding more accessible to a wider range of developers who previously may have stopped coding simply because the effort required was too tedious. Overall, the amount of code being created by developers has increased. It’s just not all that code has impacted PR cycle times for better or worse, according to the Uplevel study. It does, however, remain to be seen if an increased number of vulnerabilities in the code created does lead to an increase in application security incidents.

Ultimately, each organization and the developers that work for them will need to determine how much to rely on generative AI tools. After all, it’s not the machines that created code used in an application that will be held accountable for its overall quality but rather the DevOps teams that allowed it to become incorporated into the software build in the first place.

September 17, 2024
Has Splitting Into ‘Inner’ and ‘Outer’ Loops Sent DevOps to the Dark Ages?
The pressure on software delivery leaders is palpable. Innovation is the competitive currency in decelerating markets, while legislation such as DORA and fines for poor business resilience reflect the regulatory scrutiny of software’s role in infrastructure and lives.

Continuous improvement in enterprise software delivery is not a ‘nice-to-have’; its alternative is stagnation, negative risk and existential threat to the enterprise. However, despite all the fine-tuning and hard decisions, enterprise software delivery’s productivity and quality are fast declining.

Even more surprising: A lack of investment is not the problem. Investment has fueled the problems it sought to solve.

DevOps Imbalances

To unpack this, we must consider where enterprises have focused investment in productivity. Let’s start with the DevOps infinity loop:

Often mistaken for a solution in itself (rather than a conceptualization of how software delivery should flow), enterprises have not invested proportionately in DevOps activities. They have focused on developer productivity.

Let’s consider the evolution of software developer productivity tools — while code complete, package managers and build automation enabled developers to write and package code faster than ever, co-pilots are now generating code. Meanwhile, the most common tools among BAs and testers hark to the 1980s word processors and spreadsheets.

Software Delivery’s Inner and Software Development’s Outer Loops

This imbalanced investment in productivity has siloed development into what Jyoti Bansal and I are calling software delivery’s ‘inner loop’:

The inner loop is the world of software developers, producing and maintaining code. With today’s accelerators and productivity tools, developers have the potential to deliver faster than ever.

However, enterprise software delivery extends far beyond development. The inner loop exists within an ecosystem of symbiotically related functions.

The outer loop surrounds the development. It drives ideation, feeds the inner loop with key assets and ensures that developed software is robust, reliable and ready for real-world use. The outer loop is the domain of the business analyst, product owner, tester and data engineer, who gather and refine requirements while integrating, testing, deploying and maintaining software.

Every inner and outer loop activity is interdependent. The quality and pace of delivery hinges on the productivity and efficacy of every part. Yet, enterprises have for a long time invested disproportionately in inner loop productivity. Battling a shared set of pain points, the outer loop has drifted from the inner loop.

Symptoms of Inner/Outer Loop Imbalance

The imbalance between potential productivity in the inner and outer loop is responsible for the concerns that I hear from enterprise software leaders today:
1. Slowing productivity
2. Suboptimal quality
3. High talent turnover
These executive-level worries are the culmination of pain points felt ‘on the ground’, in outer loop teams:

‘Toil’, Manual Maintenance and Technical Debt

With unequal accelerators and productivity tools, a ‘quick’ and ‘simple’ change in the inner loop generates disproportionately greater outer loop labor.

A myriad of outer loop artifacts must be created and maintained, as code changes ripple across systems, corresponding to numerous requirements documents, test cases, data, automation and more.

Then there is the raft of outer loop processes to perform, from diverse forms of quality engineering to requirements refinement and the provisioning of data and environments.

Outer loop work is rarely formally traceable to inner loop changes or business requirements. Therefore, outer loop practitioners must perform this mammoth workload manually, with limited risk-based prioritization. This is an impossible task, creating out-of-date artifacts, technical debt and unfinished processes.

The lack of formal traceability within the outer loop and to the inner loop further prevents accurate measuring of outer loop efforts. This leads to gaps and an inability to say when testing is ‘done’ or when requirements have accurately captured a change request. There is no formal measure of release readiness, as outer loop gaps threaten release quality.

Knowledge Debt, Rework and a ‘Hero Culture’

As inner and outer loop teams drift, silos emerge. Knowledge retreats into people’s heads as artefacts grow increasingly out-of-date. Knowledge gaps and debt emerge as people leave the enterprise.

This creates uncertainty, miscommunication and rework. A ‘hero culture’ further emerges, as over-worked individuals fill gaps in processes, artifacts and knowledge. These individuals inevitably grow tired and seek opportunities elsewhere, further exacerbating outer loop challenges.

False Friends and Silver Bullets

Hiring heroes to toil harder will not solve software delivery’s productivity and quality problems, as the relationship between the inner and outer loop remains broken. Nor will a ‘hands to the pump’ mentality stem the tide of outer loop debt, bottlenecks and uncertainty.

AI will not provide a silver bullet, either. In fact, as Jyoti Bansal points out, AI acceleration in the inner loop will exacerbate the issue.

The Only Solution

The only remedy to imbalanced investment in the inner loop is its inverse: Outer loop investment.

Yet, continuous improvement in the outer loop cannot focus on one area over others, as this will create new imbalances. The investment must be holistic, hiring individuals with skills such as product management, integration and quality engineering. Moreover, building diverse teams can improve every facet of delivery.

Enterprises must similarly avoid treating or measuring teams in silos. They must improve the relationships that exist across ecosystems, investing in communication, collaboration and traceability. You should measure an ecosystem’s overall output — its ‘product’, not individual teams. Concepts such as flow and value streams are valuable for optimizing ecosystem health.

Investment in outer loop processes and tools should similarly prioritize the overall flow. Current practices range from shifting quality left and creating ‘living documentation’ in ubiquitous languages to artifact generation, continuous quality and on demand data provisioning.

Go Slow to Go Fast

There are no quick fixes to deep-set, fundamental challenges. To rebalance ecosystems, enterprises must ‘go slow to go fast’. Painkillers that mask symptoms only protract the road to catastrophe.

DevOps’ split into inner and outer loops has created a negative feedback loop. Fortunately, the inverse is also true: Investing in outer loop productivity delivers increasingly visible results, as the imbalance is redressed.

By nurturing a healthy ecosystem, every constituent stakeholder will grow increasingly productive, and happy with the software they produce and the way they produce it.
September 10, 2024
4 Reasons Why Tech Leaders Should Prioritize the Testing & Mocking Phase for Better Development

The 2023 Stack Overflow Developer Survey reports that 60% of developers use automated testing. But the truth is that automated testing and mocking need to be the most prioritized area in your SDLC to eliminate friction for your developer team.

It is not just about having the tools in place but about cultivating a culture where testing is seen as an integral part of the development process, not an afterthought. This shift in mindset can dramatically improve software quality, reduce costs and enhance overall business performance. Here are the four reasons why testing should be the more prioritized area of your SDLC.

1. Testing Guarantees High-Quality Software

As a prior dev turned CEO, I firmly believe that the cornerstone of delivering an exceptional user experience is ensuring the quality of our software. Our top priority is, and always should be, to enhance the satisfaction and trust of our end users, which means implementing a robust testing framework is crucial in our quest for quality.

This framework is your first line of defense, helping you identify and fix bugs early in the development process, thereby minimizing the risk of major issues when the software goes live. It is about ensuring that your products perform flawlessly and meet the expectations of the end users, which is essential for maintaining reliability and boosting customer satisfaction.

There is also a feed-forward effect — maintaining high code quality and functionality standards. With integrated testing, code is always at the highest standard required, anything lower fails testing and is rejected. Thus, your codebase becomes, definitionally, high-standard, and any new developer on your team can see the standard and what’s required to hit it.

By emphasizing the importance of testing, mocking and resolving issues before they impact your users, you safeguard your reputation and enhance customer retention. This proactive approach significantly cuts down the costs that would otherwise be incurred in addressing problems after the product’s release.

In summary, as tech leaders, our commitment to rigorous testing is non-negotiable. It is not just about finding bugs; it is about building trust, ensuring reliability and fostering a culture of excellence that permeates every level of our organization.

2. Automated Testing -> Happier Devs

Testing in software development can sometimes be viewed as a daunting task by developers, primarily because traditional testing methods require significant manual input. Developers often find themselves writing or updating numerous tests for even minor code changes, and the responsibility of maintaining these tests can be overwhelming and produce friction.

However, when testing is approached correctly with automation, it transforms into a powerful tool that enhances developer productivity and creates a friction-free coding environment. Prioritizing automated testing creates developers who will no longer dread the process but embrace it. This allows you to remove barriers and reduce the tedious aspects of the development process, allowing developers to focus on what they do best: Creating and improving software.

For starters, automated testing instills confidence in developers about the changes they implement. The fear of introducing bugs into production can lead developers to spend excessive amounts of time manually verifying their work. Automated tests act as a reliable safety net, giving developers the freedom to refactor and innovate without the anxiety of disrupting existing functionalities. This confidence allows for more creative and bold approaches to product development.

Also, automating testing significantly improves the efficiency of debugging. When issues do arise, automated testing frameworks help pinpoint the problem quickly and accurately. This streamlined process not only reduces the time spent on debugging but also minimizes downtime, thus allowing developers to focus more on enhancing the product rather than fixing it.

3. Better Testing Further Supports Agile & DevOps Practices

Continuous feedback is the lifeblood of agile development. Automated testing and mocking integrate with CI/CD pipelines for rapid feedback for the team. This means issues are caught early, often within minutes of code being committed.

It is not just about finding bugs; it is about creating a development environment where quality is constantly monitored and improved. This iterative development becomes possible with robust testing practices. Without comprehensive testing, each iteration risks introducing new bugs or regressions. With it, teams can confidently push updates, knowing that the core functionality will remain intact.

These practices foster a DevOps culture where development and operations work hand in hand and where these traditionally siloed departments are aligned on what constitutes ‘working software’. By prioritizing automated testing and mocking within your agile and DevOps practices, you create an ecosystem where quality is built from the start, developers are empowered to innovate rapidly and your organization can adapt swiftly to changing business needs.

4. Testing Improves Collaboration and Knowledge Sharing

This might seem an odd one. How can adding testing help devs collaborate and share knowledge?

Easy. Tests are documentation. They are the best type of documentation as they are the expected outcome from any function or component. They are live documentation of code behavior, thereby helping new team members understand the system. Tests provide clear documentation of how different parts of the system should work, serving as a reliable reference point for developers.

This shared understanding fostered by comprehensive tests can significantly reduce misunderstandings and conflicts within the team, leading to smoother collaboration. When developers write tests, they also uncover edge cases or potential issues that might not have been apparent otherwise, further enhancing the team’s collective knowledge about the system’s intricacies.

Tests also help build a consistent development environment within your engineering organization. Mocking creates a predictable environment for development and testing, enhancing team collaboration. This consistency allows developers to confidently work on different parts of the system, knowing that their changes won’t unexpectedly break other components.

When new team members join or developers switch between projects, comprehensive tests and mocks significantly reduce the learning curve, allowing them to become more productive and contribute effectively to the codebase.

Conclusion

If I leave you with anything, it is that it’s important to note that testing is required for any modern software architecture. Microservices and cloud-native applications are so complex today that testing is necessary to ensure every service is working and interacting as expected.

Prioritizing testing and mocking throughout the development lifecycle (and shifting it left) is essential — it transcends best practices to become a competitive imperative. Tech leaders who advocate for a thorough testing culture equip their organizations to produce superior products, swiftly adapt to market changes and develop sustainable, scalable systems.

Thus, the real question has shifted from whether you can afford to prioritize testing to whether you can afford to overlook it.

July 18, 2024
Harness Survey Surfaces Raft of DevOps Challenges

A survey of 500 software engineering leaders (38%) and practitioners finds nearly half reporting they can’t release code to production without risking failures, with 39% noting their code fails to push to production at least half the time.

Conducted by Wakefield Research on behalf of Harness, a provider of a continuous integration/continuous delivery (CI/CD) platform, the survey also finds that when code does need to be rolled back more than two-thirds resort to manual processes.

Overall, 60% of respondents also noted they are still releasing code on a monthly or quarterly basis. A full 59% of developers said application security requirements limit their ability to release code frequently.

Nick Durkin, Field CTO for Harness, said one of the primary reasons that security remains a bottleneck is mainly because too much responsibility for it has been shifted left toward developers, versus relying on DevSecOps platforms infused with artificial intelligence (AI) to automatically identify and resolve issues in a way that empowers everyone involved in software development.

In general, AI should be used to eliminate toil such as creating and running tests as part of a larger effort to eliminate the need to babysit tools and platforms, added Durkin.

For example, 42% of the practitioners surveyed said deploying code isn’t fast or efficient, with 44% noting that testing code end-to-end isn’t efficient. More than two-thirds (67%) said they need to wait a week to completely test code and, even then, 32% said there isn’t high unit test coverage.

More than half (52%) of the developers surveyed attributed burnout as a primary reason their peers left their jobs, with just under a quarter (23%) working overtime for at least 10 days a month. A full 97% said they regularly switch contexts between on average 14 different tools.

Well over half (54%) reported it takes longer than a week to learn a new tool. A full 59% said it takes a week to build internal tooling. More than a quarter (28%) said it takes a day to build an artifact.

Nearly two-thirds (62%) of developers have also experienced scope creep as requirements expand, which makes them less confident to execute.

Report Finds Onboarding New Hires Takes Too Much Time

New hires don’t seem to provide any immediate relief either, because onboarding them takes an average of 100 days, according to 71% of respondents.

Additionally, 40% of developers said their organization doesn’t enforce good security and governance policies. A total of 41% don’t have automated security and governance policies, while 42% said they don’t have robust identity and access management policies.

A full 62% of the executives surveyed said they would prefer to address these issues by adopting an integrated DevOps platform but the rate at which that transition is occurring is unclear. Many organizations have invested heavily in legacy DevOps platforms that they are often reluctant to rip and replace. In many cases, organizations are transitioning to a modern DevOps platform for new application development projects while continuing to employ legacy platforms to update existing applications.

Very few would argue that software development workflows are optimal. The challenge is determining how best to move forward from where most organizations are today, in a way that minimizes as much disruption as possible at a time when organizations have never been more dependent on software.

May 17, 2024
The Rise of Low-Code/No-Code in DevOps

The ever-evolving landscape of technology has ushered in a paradigm shift in software development and deployment. One of the prominent trends gaining momentum is the integration of low-code/no-code platforms within the DevOps ecosystem. This article explores how these platforms are revolutionizing the way applications are developed, deployed and managed, fostering collaboration between developers and operations teams.

1. Democratizing Development:

Low-code/no-code platforms empower individuals with limited coding experience to actively participate in the application development process. This democratization of development accelerates the delivery of software by allowing business analysts, designers and other non-developers—sometimes referred to as citizen developers—to contribute directly to the creation of applications.

2. Accelerating Time-to-Market:

The traditional software development life cycle often involves lengthy coding processes, leading to delayed releases. Low-code/no-code platforms enable rapid prototyping and development through visual interfaces, significantly reducing the time required to bring applications from concept to production. This acceleration aligns with the principles of DevOps, promoting continuous delivery and faster time-to-market.

3. Enhancing Collaboration:

DevOps is all about breaking down silos and fostering collaboration between development and operations teams. Low-code/no-code platforms bridge the gap between these traditionally distinct roles by providing a common ground where both technical and non-technical stakeholders can collaborate seamlessly. This collaborative environment promotes better communication, understanding, and alignment of objectives.

4. Flexibility in Deployment:

Low-code/no-code solutions are designed to be platform-agnostic, allowing for the deployment of applications across various environments, including on-premises and cloud. This flexibility aligns with the DevOps principle of infrastructure-as-code (IaC), enabling teams to deploy and manage applications consistently across diverse infrastructures.

5. Reducing Dependency on Specialized Skills:

In a DevOps environment, reducing bottlenecks and dependencies on specific individuals or roles is crucial. Low-code/no-code platforms enable teams to be more self-sufficient, reducing the reliance on specialized coding skills or DevOps Training. This, in turn, contributes to a more resilient and adaptable DevOps culture.

6. Challenges and Considerations:

While the adoption of low-code/no-code in DevOps brings numerous benefits, it also introduces challenges. This section discusses considerations such as security, scalability and the potential need for customization beyond the platform’s capabilities.

7. Real-World Use Cases:

Explore real-world examples of organizations successfully incorporating low-code/no-code in their DevOps workflows. Highlight the positive outcomes, lessons learned, and the impact on their overall software development life cycle.

Conclusion:

The rise of low-code/no-code platforms in DevOps is reshaping the way software is developed and deployed. By fostering collaboration, accelerating development cycles, and reducing dependencies on specialized skills, these platforms align seamlessly with the principles of DevOps. As organizations continue to embrace this trend, the synergy between low-code/no-code and DevOps is set to redefine the future of application delivery.

March 29, 2024
What Does Build Quality Look Like in Software Design?
With rapid developments in artificial intelligence (AI), mobile applications and modern software in the last year, the enterprise software market is expected to grow exponentially. Market revenue is projected to reach $292 billion in 2024, and developers are constantly implementing innovative measures into the software development life cycle (SDLC) to ensure a seamless, high-quality creation process. One of the best ways to achieve this is by leveraging modern build health tools to monitor a multi-faceted project’s overall quality and performance.

Build health tools automate the creation of executable applications from source code and encompass several areas to ensure the stability and health of the software build process. From automated testing to security scanning, developers rely on these tools and processes when defining a modern approach to software development. Before diving into the intricacies of build health quality, it is necessary to grasp the complete significance of the SDLC when attaining the final product in a global marketplace.

The Significance of the SDLC

The SDLC is the process in which developers plan, write, modify and maintain software. This methodology applies to software developed for computers, mobile phone applications, video games and other systems. Adhering to this process allows developers to reach the optimal outcome for the final product. The life cycle includes everything from the planning and brainstorming stage to setting up maintenance and operations after the product has launched. It ensures success by providing a more structured approach to building and developing software. This becomes increasingly important as the software market’s value continues to rise.

Since 2020, the demand for software engineers has doubled. Because it’s essential for business leaders and developers to keep up with the latest trends and innovations, the need for software knowledge will continue to grow exponentially. Currently, software is typically delivered in one of two ways:
- On-premises. End users install and run the software themselves on the desired device. This serves as a more secure delivery method for highly-regulated industries.
- Software-as-a-service (SaaS). This cloud-based method allows end users to access the software online rather than through a traditional download/upload. This enables providers to offer a subscription-based/pay-as-you-go structure for users. The provider is responsible for the hosting and maintenance.
Developers rely heavily on build health tools to ensure a smoothly running life cycle. Build health tools play a huge role in determining a seamless development process.

The Role of Build Health Tools in Modern Software Development

Build health tools essentially fill the gaps in the development process and enhance the software build quality. These tools deliver automated testing methods by creating multiple testing scenarios to ensure full functionality. They help developers see the different data points necessary for garnering a 360-degree view of the software and its status.

The tools also impact resource utilization and response times. Using key performance indicators (KPIs), they monitor the build quality while enhancing build production to ensure it is better than what was previously developed. In that way, it is constantly growing and improving to guarantee the project reaches a smooth and seamless completion for the developer. From there, the tools can create multiple datasets in less time, which significantly reduces overall software development time.

Common Challenges of Build Health Tools in Software Design Projects

There are several challenges with using build health tools. The most common obstacle is that automated tools and testing lack the human touch that comes from developers and their manual work. Although build health tools can perform simultaneous tasks quicker than a more manual approach, they overlook the full spectrum of available resources to improve the build. These tools are so focused on functionality that they fail to incorporate the prior research and available resources that often serve as key building blocks for a successful development project. For this reason, it is important to have developers working in conjunction with build health tools to provide a balance between manual and automated software testing.

Developers can leverage build health tools to seek out the KPIs that determine the software’s quality at a given stage of the SDLC, from initial development and quality assurance (QA) to the final phases of staging and production. Some of these KPIs include:
- Exceptions. Make sure there are no new exceptions from the previous build versus the production stage.
- Test coverage. Ensure that adequate testing is performed on the current build by defining what application programming interfaces (APIs) need to be included for certification, along with the number of executions required.
- Automated data validation. Make sure data is accurate and useful while remaining enforceable by the designated standards.
- API testing. Test the overall interface for smooth performance with trustworthy data to advance the program/project. It is also important to compare performances during different SDLC stages to confirm the status of the API, dependency, database, and functionality.
- Memory/CPU usage. Measure the overall usage of the central processing unit (CPU) and its memory to ensure stability and efficiency.
- Requests per minute. Evaluate the rate at which the software can receive and execute certain requests delivered by the end user to ensure optimal efficiency.
- SQL coding standards. Enforce standards and limitations for the overall coding within the interface by measuring quality, security, efficiency, and sustainability.
While these are only a few of the KPIs detectable with the aid of build health tools, it is essential to note how these tools can benefit a successful software release. Developers can compare the differences between current and prior builds throughout the SDLC to fully gauge any changes, developments, improvements, or downgrades during production to a successful launch.

Current Events and the Future of Build Health Quality for Developers

There are many examples of current organizations effectively leveraging available build health tools for project success. Software developers will continue to adopt new and innovative methods using AI and automation capabilities to ensure more efficient testing and higher build quality.

The future is bright for contemporary software engineering. As unexpected as the AI boom of 2023 was for the tech world, the future of modern software development may hold even more surprising developments. Build health tools are expanding and perfecting the creation process for enterprises and developers worldwide. As build quality improves on the back end, the customer experience for global industries will improve on the front end. The potential for software enhancements and innovations in this way is what makes the journey exciting.
February 22, 2024