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Tag: implementation

Rethinking AI Software Development With a Three-Tiered Framework

Artificial intelligence (AI) is transforming software development into a more efficient and augmented process, prompting developers and their peers to rethink how they produce value. Nearly 100% of the code will likely be supported, generated and/or tested by AI by the end of this year for new code, allowing developers to focus more on design, architecture and problem-solving as they offload routine coding tasks.

Despite growth in implementation, AI-driven software development is not a one-size-fits-all endeavor, and a tiered, robust framework is essential to maximize output while minimizing risk.

This can be done by following a three-tiered framework for AI-driven development, where each facet performs a specific function.

1. Rapid AI-Generated Solutions

The first tier prioritizes speed above all else, with the primary objective of delivering value quickly. AI generates code almost entirely autonomously to rapidly create prototypes, internal tools, or short-term solutions that solve immediate business problems. Quick-generated software is the developers’ playground, underpinned by high experimentation, low friction and an increase in opportunities for “vibe coding” (collaborating with AI agents to develop working applications in hours rather than weeks). Throw-away code becomes common. Long-tail support of application needs has now become a reality. There is less concern for scalability or long-term maintenance, as it can be recreated quickly.

2. Collaborative AI-Human Development

The second layer, focused on collaborative AI-human development, is ideal for larger business-critical software that demands more governance, reliability and collaboration between humans and machines. AI doesn’t fully take over the keyboard; it evolves into a trusted co-pilot that writes test cases, suggests code snippets, writes selected portions, flags vulnerabilities and helps with documentation. Some agentic automation is expected while human-in-the-loop is enforced. But crucially, human developers still drive architectural decisions, co-validate outputs and maintain accountability. Humans are expected to refine their context curation and intent framing skills to enhance alignment between expectations and outcomes. This tier reflects a partnership that blends AI’s speed with human judgment to strike a balance between innovation and risk.

3. High-Reliability, Long-Term Software

The top of the pyramid is reserved for the most strategic, long-lived, mission-critical systems — those underpinning enterprise-wide processes or customer-facing platforms. These solutions must be durable, secure and compliant, often for extended periods due to the sheer size of their codebases, or the human-life impact of their functionality. While AI has a place here, its role is on average more assistive than generative. Agentic ecosystems play a critical role in ensuring higher tiers of quality, security, scalability and maintainability. It might automate ancillary repetitive tasks or surface patterns in telemetry data, but the bulk of development remains human-centric. This software is about resilience and quality, not just results. Eventually, this category will also evolve, but we still have time. A lot more maturing of these new tools is needed.

Operational Shifts to Support AI Integration

Adopting a three-tiered approach is only one piece of the puzzle. Enterprises must also undergo meaningful operational change to scale AI-led development across these layers. Legacy delivery models rooted in static requirements and monolithic releases won’t suffice. Enterprises need to shift toward agile, product-centric agentic structures that emphasize rapid iteration, measurable outcomes and continuous feedback.

Equally important is the modernization of IT infrastructure. Agent-native architectures, modular platforms that can be easily swapped, non-dependency-inducing vendor solution usage and scalable pipelines are essential for supporting the dynamic needs of AI-powered tooling, as is a robust data strategy. AI’s effectiveness hinges on access to clean, contextualized and trustworthy data. This means that enterprises must invest in robust data governance, interoperability standards and mechanisms for real-time data sharing across teams. The more accessible and high-quality the data, the more valuable and accurate the results become. However, as I keep reminding my peers, dependency on high-quality data sets has been dramatically reduced with this new generation of models since the hard work has been encapsulated in prêt-à-porter models.

Lastly, the human element cannot be overlooked. Developers, testers and product teams require new skill sets, including context curation and intent framing, to work effectively in AI-augmented environments. From understanding prompt engineering and model behavior to interpreting AI-generated insights, continuous learning must be embedded in the culture to absorb the compounding effects of technological changes. AI will not necessarily replace developers, but developers who understand AI will outpace those who don’t.

Better, Faster and More Precise

What’s emerging is not a world where AI builds all the software, but one where software gets built faster, better and more precisely. Just as cloud computing redefined IT infrastructure over the past decade, AI is redefining the software lifecycle.

By adopting these AI-led software development strategies, enterprises can optimize today’s processes and future-proof their entire application ecosystem. They should foster a mindset of continuous learning and a lifelong commitment to innovation, a necessity for staying at the cutting edge of technology.

May 22, 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
How Open Source Can Help Your CD Chain
What can you do to make continuous delivery work better? Consider moving to open source software solutions. Here’s why.

Many DevOps teams already may be using open-source platforms for reasons not related to continuous delivery. They might like the cost-efficiency or interoperability that usually come with open source. Or maybe they just like the open-source philosophy.

Typically, however, open source is not viewed as a requirement for doing DevOps or achieving continuous delivery.

How Open Source Speeds Continuous Delivery

But maybe it should be. Consider the following ways in which open-source code can better facilitate a continuous delivery pipeline:
- It’s easy to tweak. Because anyone can view and modify the source code of an open-source program, you can change it to work as efficiently as possible. Custom enhancements could make the open-source tools you use work more efficiently within your delivery chain.
- No vendor lock-in. Most open-source tools and platforms are designed based on open standards. The threat of vendor lock-in is minimal. For that reason, adopting open source helps prevent surprises that could otherwise hold up your continuous delivery pipeline in the event that a vendor decided to stop supporting the tools you are using.
- It’s well-known. Mainstream open-source tools are widely used. Chances are good that the people who are currently on your team, or will join it in the future, already know open-source tools. That’s an advantage for continuous delivery because it means you won’t have to spend time training staff—and risk introducing delays to your continuous delivery chain while the staff are being brought up to speed.
- It gives you options. With open source, you usually have lots of implementation options. Want to deploy on Linux? There are about a dozen mainstream distributions you can choose. Want to run your cloud with OpenStack? There are lots of distributions of that, too. Having choice is a good thing for continuous delivery because it allows you to switch easily between different frameworks or tool implementations as your needs change. That agility prevents you from being locked into a specific solution.
This is not to say that open source is a silver bullet. Achieving continuous delivery requires attention a lot of other factors; it’s not as simple as using a certain type of technology (if it were, everyone would be delivering continuously). But adopting open-source solutions when possible certainly can’t hurt—and it just might help as you seek to deliver software continuously.

— Chris Tozzi
January 13, 2017
DevOps doesn’t end at deployment

If you ask five IT admins the question “What is DevOps?” you maybe get eight different answers. We’ve attempted to define and explain the term DevOps here on devopsy.kinsta.cloud a few different ways, but a precise definition is still elusive. One thing is certain, though, DevOps is about more than just automating virtual server deployment and configuration.

At Velocity 2009, John Allspaw and Paul Hammond presented a session titled “10 Deploys Per Day: Dev & Ops Cooperation at Flickr”, which some credit with launching the concept of integrating development and operations—at least to mainstream IT. For many, though—especially those who are still trying to understand what DevOps is, and what it means for their organization—DevOps amounts to tools that simplify the process of implementing tens or hundreds of virtual server instances, and automating the initial configuration.

Automating IT

Tools like those offered by Chef and Puppet Labs are useful for automating routine tasks associated with deploying and configuring the IT infrastructure, as well as deploying and updating applications. These vendors can also help monitor and maintain a consistent, stable environment by enforcing a designated configuration.

To be honest, that sort of automation is more or less table stakes for DevOps. It’s just one small facet of a much larger concept that involves the entire scope of development and operations—agile development and rapid iteration of new versions and features combined with an automated and easily-scalable infrastructure.

Once the virtual server environment is up and running, though, and once the applications are deployed, the work is really just beginning. Part of merging development and operations, and keeping up with a more rapid pace of IT involves ensuring that everything is running smoothly. With frequent updates and rollouts, it becomes even more important to have tools in place to automate monitoring and alert IT admins when something goes awry.

DevOps also involves tools like those from Stackify—tools that keep an eye on the apps running in your environment and let you know when things don’t quite seem to be firing on all cylinders. When something goes wrong, you want to know about it in a few seconds, not a few hours or days. Stackify can monitor and trend important metrics like CPU, memory, and disk utilization on your servers, and alert via email or SMS text when problems arise.

Automating the creation, deployment, and initial configuration of servers is awesome—but you also need tools to address the challenges of maintaining and updating thousands of server instances as well. You need to be able to add or change user privileges, apply patches and updates to the operating systems or applications, change security configurations, enforce compliance policies, and more. When you’re talking about hundreds, thousands, or tens of thousands of servers, it’s imperative that you have tools like JumpCloud to help automate those tasks across the entire environment.

More than just tools

As great as all of the various DevOps platforms and tools are, though, DevOps is not just a matter of buying or implementing an array of tools to manage your IT environment. As the name implies, and as Allspaw and Hammond spelled out in 2009, it’s about instilling a culture that breaks down traditional barriers and enables developers and operations to work cooperatively and collaboratively.

The traditional roles of developers and ops impose unnecessary friction that gets in the way of being effective. Developers blame problems on the implementation or IT infrastructure. Operations personnel blame problems on poorly developed code. Creating an environment where all appropriate parties work together, and where everyone takes responsibility rather than assigning blame creates an entirely different dynamic for productivity.

When it comes down to it, it’s the culture of DevOps that accelerates the speed of changes and updates that necessitates tools and platforms to automate deployment and infrastructure management. The culture drives the tools, not the other way around.

So, yes, one of the most visible components of DevOps may be tools that automate the deployment and configuration of servers, but that is just one element of DevOps, and it’s just the beginning. Everything that happens after that to monitor, maintain, and update that environment once it’s deployed, and everything that happens outside of the IT infrastructure to streamline productivity and increase efficiency is all part of DevOps as well.

June 23, 2014