DevOps is a fundamental element of digital transformation, closely integrating with overarching corporate strategies to drive maximum operational efficiency. The collaboration between development and operations teams facilitates a culture of continuous improvement and innovation, enabling corporations and organizations to respond swiftly to market demands and customer needs.

Our blog has DevOps references, providing informational and historical context on the practices shaping this field. However, this post intends to offer a contemporary view of the practice’s value and how to leverage working with a trusted agency to strengthen your technical infrastructure.

Key DevOps Trends for 2025

Business-Level Strategy and DevOps Alignment

This is not a bold prediction when we say that, in 2025, DevOps will continue it’s role as a fundamental aspect of business-level strategy, ensuring alignment between operational capabilities and overarching corporate goals. What are the ongoing benefits?:

DevOps delivery enables:

• Agility and Scalability: Rapid response to market demands.
• Operational Efficiency: Streamlined workflows and reduced bottlenecks.
• Cost Optimization: Efficient resource allocation through nearshore models.

Digital Platform Strategy in DevOps

Digital platform strategies are pivotal to DevOps practices. Businesses prioritize platform-centric approaches which enable:

• Centralized Operations: Unified platforms for development, testing, and deployment.
• Enhanced Collaboration: Seamless communication across geographically dispersed teams.
• Innovative Ecosystems: Integration with cutting-edge technologies like AI and machine learning.

Process Optimization Through Automation

Understanding what is process optimization is crucial for achieving peak efficiency. Automation, a cornerstone of DevOps, drives this optimization by:

• Reducing Manual Errors: Automated testing, deployment, and monitoring.
• Accelerating Time-to-Market: Faster releases with CI/CD pipelines.
• Improving Resource Utilization: Dynamic scaling in cloud environments.

Nearshore Software Development Centers

Nearshore software development centers have become strategic hubs for DevOps outsourcing. Key benefits include:

• Access to Global Talent: Expertise in niche technologies and methodologies.
• Cost Efficiency: Lower operational costs compared to traditional alternatives.
• Timezone Alignment: Enhanced collaboration with nearshore centers.

Leveraging SaaS App Development Companies

SaaS app development companies now play a critical role in DevOps ecosystems. They offer:

• Tailored Solutions: Custom SaaS applications that integrate with DevOps pipelines.
• Scalability: Cloud-based tools that grow with business needs.
• Continuous Innovation: Regular updates and enhancements aligned with industry trends.

Advanced Profiling Tools for Performance Optimization

Profiling tools are indispensable for identifying performance bottlenecks and optimizing applications. Modern profiling solutions provide:

• Real-Time Insights: Monitoring and analytics for swift issue resolution.
• Deep Code Analysis: Identifying inefficiencies at the source code level.
• Integration with CI/CD: Automated performance checks during development.

Open Policy Agent for Security and Compliance

Security remains a top priority, and Open Policy Agent (OPA) has emerged as a leader in enforcing policies across complex systems. Benefits include:

• Unified Policy Management: Centralized control over access and compliance.
• Scalable Security: Policies that adapt to dynamic environments.
• Seamless Integration: Compatibility with Kubernetes, Terraform, and other tools.

Building a DevOps Practice: Finding the Right Balance Between In-House, Augmented, and Bespoke Solutions

Whether building an internal DevOps team, augmenting your capabilities with a service company, or outsourcing entirely to a vendor for a bespoke solution, each path has unique advantages. At the core of any decision is trust—trust in your team, your partners, and the processes you establish.

Building an Internal DevOps Practice

If your company has an IT infrastructure and the leadership to create a DevOps practice, it will help realize long-term benefits. Keeping scalability in mind, you can take advantage of the following:

Ownership and Control

When you build your DevOps team in-house, you have complete control over the processes, tools, and workflows. When business rules and technical infrastructure align closely with your company’s unique needs and culture, little is lost in translation.

Deep Integration with Culture

An internal team will align with the company’s mission and understand ship decisions and intent. They understand the intricacies of your operations, making them well-suited to drive DevOps initiatives that seamlessly integrate with existing departments.

Challenges

However, building a DevOps practice from scratch requires significant investment in hiring skilled professionals, training, and tooling. Maintaining momentum and staying updated with evolving technologies can also be a heavy lift for smaller teams.

Augmenting Your DevOps Practice with a Services Company

Sometimes, the best way forward isn’t to do it all yourself. That’s where partnering with a services company comes in.

Flexibility and Scalability

Need a senior cloud architect for a critical project or a CI/CD expert to streamline your pipelines? Services companies can quickly provide skilled professionals who plug gaps in your internal team.

Access to Expertise

DevOps is a broad discipline that spans everything from automation to security to infrastructure as code. Augmenting your practice with a trusted partner gives you access to expertise in niche areas that might otherwise be hard to recruit for in-house.

Cost-Effective Solutions

Instead of hiring full-time employees for temporary needs, working with a services company offers flexibility without the long-term overhead.

Trust Is Key

Trust is a theme in this post. Choosing the right partner hinges on it. You must ensure they align with your goals and are committed to your success, not just their bottom line.

Having a Vendor Build a Bespoke DevOps Practice

Engaging a vendor to create a custom DevOps practice is often the most efficient route for organizations looking to fast-track their DevOps journey.

Turnkey Solutions

A vendor comes with the experience, tools, and methodologies needed to build a DevOps practice from the ground up. They’ve done it before and can tailor a solution to your needs.

Ongoing Support and Iteration

The right vendor doesn’t just deliver and disappear. They remain partners, iterating and refining the practice as your needs evolve.

Building Trust

Trust becomes the cornerstone of success when outsourcing something as critical as DevOps. Clear communication, regular updates, and goal alignment maintain the relationship.

The Value of Accountability

No matter which approach you choose, confidence in the transparency and collaboration with your teams is the glue that holds your DevOps practice together:

• Empower your internal team to make decisions and innovate. When they feel trusted, they’re more likely to deliver exceptional results.
• Whether you’re augmenting your team or outsourcing entirely, your partners must share your vision and commitment to quality.
• A well-built DevOps practice thrives on processes. Trusting the frameworks and automation you implement will help your team focus on higher-value tasks.

When you are hiring an internal team, they will be vetted through interviews and potentially tech assessments, coupled with additional assessment criteria. If augmenting your team with a services provider, check with them that the same process be afforded to your technical leadership and get a feel for skills and cultural alignment. When you have established trust with the right partner, you can build capacity with the conviction that scaling a team as needed will be expertly handled.

We Know DevOps

Building a successful DevOps practice isn’t a one-size-fits-all journey. Whether you opt for an internal team, augmented resources, or a bespoke solution, the key is to find the balance that fits your organization’s needs, culture, and goals.

Whether you’re looking to build, augment, or outsource your DevOps practice, having the right partner can make all the difference. Let’s talk about how we can help you create a scalable, efficient, and trustworthy DevOps framework tailored to your business goals. Reach out to Unosquare today to start the conversation!

By Austin Aldrich

My name is Austin Aldrich and I’m a Lead Software Developer at unosquare with a ton of experience in both backend and frontend development. Over the years, I’ve had the opportunity to manage complex projects and witness firsthand how ‘rotting’ backlogs can become major obstacles; on the flipside, I’ve seen how a few simple changes in approach can give operations a much-needed facelift. So begins the inspiration for this two-part series…

One of my favorite podcast episodes ever made is a cautionary tale about the Sultan of Brunei’s bizarre obsession with collecting cars. As documented by Michael Sheehan, the oil-rich ruler owns an uncountable but estimated at 7,000 vehicles. His numerous garages include 400 Bentleys, 600 Rolls Royces, 200 BMWs, and 200 Ferraris. He even keeps a Rolls Royce running in front of his palace at all times. The whole story is fascinating, if not sad and, frankly, irritating. According to Michael, the air conditioning in several of the buildings was not functioning. In the tropical climate of Brunei, that’s disastrous for the health of the cars. To add insult to injury, many of the vehicles had not been started in five years. This means a sizeable chunk of his collection is slowly rotting.

That’s a sad story, but I promised a cautionary tale, and it’s this: your backlog is a rotting parking garage of tickets. Some of them you likely haven’t looked at in five years. I can almost guarantee you there are numerous outdated or duplicated entries that are doing no one any good. Traditional solutions like backlog refinement are often wishful thinking – falsely promising us that one day we might get our backlog into some semblance of a usable state. Radical solutions like eliminating the backlog altogether might not be feasible if your team is tethered to something like a Scrum-based workflow where it’s intrinsic to the process and is how your project management team has been trained to work.

Instead of giving you a “hot take” where I suggest the absolute answer of whether you should keep or ditch your backlog, I want to offer some helpful insights that I’ve learned from years of software development as well as some advice I gleaned from Basecamp and Lucas Costa. I don’t agree with their conclusion that eliminating a backlog altogether always makes sense (especially if you work in a consulting agency like we do here at unosquare). Still, I’ve done my best in this article to capture the spirit of their observations and apply them in a reasonable manner to help teams work more efficiently without radically altering their existing workflows.

Why Do Backlogs Exist?

First thing’s first, though. Why do backlogs exist at all? Simply put, they are a place to store future work that will be groomed and put into a later sprint. The idea likely evolved somewhere in the evolution of the Scrum framework in the mid-1990’s.[1] According to the Agile Alliance, it’s defined as “a list of the new features, changes to existing features, bug fixes, infrastructure changes, or other activities that a team may deliver in order to achieve a specific outcome.”[2] So, it’s a place of “might be’s” As your project matures, those ideas and bugs will continue to pile up. The traditional Scrum answer to this is that you should use backlog refinement sessions to clean out things that are no longer relevant.[3] And I agree, this does work for items near the top of the queue. But fast forward a year or two. Add several customers, a couple of project managers, developers, QA engineers, and support staff. Now the influx of items will certainly outpace the team’s ability to maintain them over time. You’ll never get to the bottom of your backlog.

You might be saying, “So what? Of course they will grow. That’s why we have search features in our ticketing system.” But I want to challenge the assertion that boundless space is truly free and harmless so long as you can fish out what you need. That is the philosophy that allowed the Sultan’s car collection to become so unwieldy. Unfortunately, unbounded backlogs come with some drawbacks that are not good for your team or your customers.

The Problems of a Bottomless Backlog

Dampened Team Morale

The first negative effect of large backlogs is that it dampens the team’s morale. Like it or not, we productivity workers have been trained to treat any kind of list as something that must be completed. And we are taught by agile methodologies like Scrum that an item should be picked from the backlog and moved methodically into a development cycle until it’s done. But what does that imply about all the other items that don’t get picked? That they will never be done! And that’s not a good feeling when a considerable number of those items are bugs. “Our software just keeps getting more and more bugs…will we ever make a dent in these?”

Misleading Metrics

Secondly, bottomless backlogs tell a deceiving story. If you are unfortunate enough to work in an organization that weaponizes agile metrics to gauge performance, you may find them unfairly examining the product backlog to identify defect ratios, the team’s velocity on feature development, or any number of flawed statistical strategies that end up making teams feel and function worse than if they’d had no backlog at all.

Atrophy

The third problem is atrophy. Remember how the Sultan’s cars fell into disrepair because he didn’t perform periodic maintenance? The same will happen to your backlog items if you just record an idea, bug, or feature request then abandon the ticket. It will be compressed down like a fossilized layer from the weight of decisions made after it. I can’t tell you how many bug tickets we pull out of the backlog that were either fixed long ago in another release or we can’t even remember what exactly the problem was, so we just delete the ticket. It can be tempting to think, “I just want to capture this so we can come back to it in the future.” That sounds nice–the responsible, organized thing to do. I get it! But by the time the future comes, your context window will have changed. Features that made sense when you had 10 customers, and your product was focused on market segment X are irrelevant when you have 500 customers and are focused on market segment Y.

Now What?

By now, you’ve probably recognized some of these issues in your team’s backlog, so what’s the solution? In the second part of this series, we’ll explore practical strategies for managing bugs, using alternative tools, and scheduling releases to de-rot your backlog.

Stay tuned for Part 2.

web: bundle exec puma -C config/puma.rb
worker: bundle exec sidekiq -C config/sidekiq.yml

But whenever I tried to deploy, Heroku would refuse to recognize that it needed to spin up a worker dyno. I did a lot of digging to see what the issue was. Several people suggested that it could be that my Procfile wasn’t named correctly or that there was something wrong with the formatting inside the file. After confirming everything was correct and still running into the issue, I finally figured it out!

The solution was quite simple once I found it buried in the Heroku docs.

1. Rename my Dockerfile to Dockerfile.web.
2. Create a Dockerfile.worker that will run the sidekiq job. Mine looks something like this:

# Use Ruby 3.2.2 as the base image
FROM ruby:3.2.2

# Set the working directory to /app
WORKDIR /app

# Copy the Gemfile and Gemfile.lock from the app directory to the container
COPY Gemfile Gemfile.lock ./

# Install dependencies
RUN bundle install

# Used for video transcoding
RUN apt-get update -y && apt-get install -y ffmpeg

# Copy the rest of the application code to the container
COPY . .

# Set environment variables for build step
ENV AWS_ACCESS_KEY_ID=""
ENV AWS_SECRET_ACCESS_KEY=""
ENV AWS_REGION=us-east-1
ENV AWS_BUCKET="test-bucket"

# Start sidekiq
CMD ["bundle", "exec", "sidekiq", "-C", "config/sidekiq.yml"]

3. Create or update your heroku.yml file to look like this:

build:
  docker:
    web: Dockerfile.web
    worker: Dockerfile.worker

4. Now deploy! Heroku will use the heroku.yml contents to deploy a dyno for each entry within the “docker” section. It should look something like this in your Heroku project’s “Resources” tab:

NOTE: I was having trouble with sidekiq not processing my jobs even after everything was deployed correctly. It turns out Heroku was kind to me and didn’t toggle the worker dyno to the “on” position so that I wouldn’t get charged until I explicitly flipped the switch and set the number of dynos I wanted. Make sure you check this before you try to start processing jobs.

I hope this helped save someone else the same headaches I experienced. As you can see, it’s simple to deploy a worker dyno using a Docker container once you know what you’re doing!

Austin Aldrich

Unosquare Lead Software Development Professional

In this blog post, we’ll look at the importance of having a software license, the key factors to consider when choosing one, the most popular software licenses in use today, and finally, the four main types of software licenses available, each catering to different objectives and requirements of open-source projects. Let’s briefly touch on the risks of making the wrong decision up front.

Pitfalls of Choosing the Wrong License

Choosing an the wrong software license for an open-source project can lead to several significant drawbacks, potentially causing you to stumble out of the starting blocks:

1. It may inadvertently restrict the distribution and use of the software, thereby limiting its adoption and stifling innovation.
2. An inappropriate license can create legal ambiguities or conflicts, exposing the project to potential litigation, which could divert resources away from development.
3. The wrong license might need to align with the project’s community goals, such as encouraging contributions or ensuring commercial usability, thus hindering its growth and the collaborative potential of the open-source ecosystem.

How to Choose a Software License for Your Open-Source Project

If you have dedicated considerable effort and skill to crafting an open-source project, you have a critical step that you cannot overlook: the selection of a software license. While this may appear to be a trivial detail mired in legal jargon, choosing a suitable license is crucial. It safeguards your intellectual efforts and promotes the growth of the open-source community you aspire to support.

A well-chosen software license protects your intellectual property legally, preserving the creators’ original intentions. This protection includes stipulations on how the software can be used, modified, and redistributed. This protection helps prevent commercialization or closed-source derivatives that could undermine the project’s original ethos.

The right license also plays a crucial role in supporting the open-source community. It encourages collaboration and contribution, making the project accessible and beneficial to a broad audience, including developers who want to build upon or incorporate it into other projects. Clear guidelines help maintain transparency and trust among community members, which are vital for the project’s growth and vitality. This supportive environment accelerates innovation and amplifies the project’s impact.

Why is it Important to Have a Software License?

A software license primarily serves as a legal framework that defines the terms under which the software can be used, modified, and distributed. This clarity is essential not only for protecting the original developer’s rights but also for guiding how the project can evolve in the hands of others. It prevents unauthorized versions of the project or commercial ventures that might deviate from the original vision, ensuring that the software remains true to its foundational goals.

Moreover, a clear licensing agreement sets explicit expectations for contributors. It outlines their rights and responsibilities, fostering a transparent and respectful collaboration environment. Setting these expectations maintains a healthy development culture, preventing disputes and misunderstandings arising from ambiguous or unspoken rules. 

By establishing these boundaries, the license ensures that all participants are aligned with the project’s objectives, promoting sustained software product growth. These guardrails, in turn, enhance the project’s credibility and reliability, attracting more contributors and assuring users of its stable and community-driven development pathway.

Things to consider when choosing a software license

Here are some key questions to ask yourself:

• Sharing vs. Control: Do you want others to be able to create proprietary versions of your code (permissive license), or do you want to ensure any modifications are also open-source (copyleft license)?
• Compatibility: Is your project part of a larger open-source ecosystem? Aligning your license with established choices within that community can streamline collaboration.
• Community Building: What kind of community do you want to create? Copyleft licenses encourage cooperation and shared improvements, while permissive licenses offer more flexibility for individual use.

Which is the Most Popular Software License

The MIT License is the most popular software license. Its popularity stems from its permissive nature, which provides a simple legal framework. This license is favored for its minimalistic approach, requiring only the retention of copyright and license notices in redistributed works. It grants extensive freedoms to users, allowing them to modify, merge, distribute, sublicense, or even sell copies of the software without substantial restrictions or complex compliance requirements.

Such broad rights encourage widespread use and adoption of the software across various sectors, including commercial enterprises, because they impose few limitations on how the software can be utilized or integrated into other projects. This openness not only accelerates innovation but also enhances the software’s accessibility and adaptability, making it a preferred choice for developers looking to maximize the impact and reach of their work in diverse application scenarios.

What are the Four Types of Software Licenses?

Open-source licenses fall into four main categories:

• Permissive Licenses: (e.g., MIT, Apache) These licenses grant users the most freedom, allowing them to modify and distribute the code for any purpose, including commercial use, without requiring them to share their modifications.
• Copyleft Licenses: (e.g., GNU General Public License (GPL)) These licenses ensure that any derivative works (modifications) are also distributed under an open-source license, fostering a culture of shared development.
• Copyleft with Linking Exception: (e.g., GNU Lesser General Public License (LGPL)) These licenses allow derivative works to be closed-source, as long as the modifications made to the open-source code remain open-source.
• Other Open Source Licenses: A few licenses fall outside these categories, with specific requirements for attribution, advertising, or specific use cases.

How Unosquare Can Help

While choosing a software license is fundamental to the success and integrity of your open-source project, navigating the complexities of legal frameworks and community expectations can be challenging. This is where our software engineering services come into play. Unosquare supports the entire software development lifecycle, from initial design and implementation to community building and long-term maintenance. Partnering with us ensures your project thrives in the open-source ecosystem, benefiting from our comprehensive support and expertise in creating innovative software solutions.

The Memory Maze: Why C/C++ Are Vulnerable

C and C++ are workhorses in the programming world, powering everything from operating systems to embedded systems. However, their power comes at a cost—they lack built-in memory safety features. This drawback means developers have more control over memory allocation and deallocation but also increases the risk of errors. Common memory-related vulnerabilities like buffer overflows and dangling pointers can be easily exploited by attackers, compromising entire systems.

Enter Rust: The Guardian of Memory

Rust, a relatively young language compared to C/C++, is designed with memory safety as a core principle. It employs a unique ownership system that prevents memory-related errors at compile time. Imagine a system where memory is automatically managed and cleaned up, eliminating the potential for human error. That’s the magic of Rust.

Here’s how Rust enhances security:

• Ownership System: Variables in Rust have clear ownership, ensuring memory is only accessed and used once. This feature eliminates dangling pointers and buffer overflows.
• Borrow Checker: Rust’s borrow checker ensures that memory is not accessed by multiple parts of the code simultaneously, preventing data races and other concurrency issues.

Beyond Security: Benefits of Rust

While security is the main focus, Rust offers other advantages:

• Performance: Rust code can be incredibly fast, often rivaling C and C++ performance in specific scenarios.
• Concurrency: Rust provides powerful features for building safe and efficient concurrent programs.
• Modern Features: Rust embraces modern programming concepts like functional programming and pattern matching, making it a joy to use.

The Road Ahead: A Gradual Shift

The transition from C/C++ to Rust will take time. C/C++ has a vast legacy codebase powering critical infrastructure. However, the ONCD report highlights the importance of adopting memory-safe languages for new projects. Additionally, integrating Rust modules into existing C/C++ codebases is a viable option.

The Takeaway: Secure Coding Starts with the Right Tools

The US government’s push for Rust is a wake-up call for developers. Secure coding practices are no longer a luxury; they’re a necessity. Engineers can build a more secure digital future by embracing memory-safe languages like Rust.

Call to Action:

• Explore Rust and other memory-safe languages for your next project.
• Consider integrating Rust modules into existing C/C++ codebases.
• Advocate for secure coding practices within your development teams.

Beyond Rust: A Look at Similar Security-Focused Transitions in Programming

The US government’s endorsement of Rust isn’t an isolated incident. It’s part of a broader movement within the tech industry towards languages that prioritize security and streamline development. Similar efforts are underway in other corners of the programming world, with established languages like Java and C# witnessing a shift towards newer, safer alternatives. 

Let’s explore these transitions and how they compare to the Rust movement, offering valuable insights for developers navigating this evolving landscape.

Java to Kotlin

This is a particularly relevant comparison for developers familiar with the Java ecosystem. Kotlin, a statically typed language designed for the JVM (Java Virtual Machine), offers significant improvements over Java regarding null safety, conciseness, and interoperability. Like Rust, Kotlin’s focus on type safety and reduced boilerplate code helps prevent errors and improves code maintainability. However, unlike Rust’s ownership system, Kotlin relies on the garbage collector for memory management.

C# to Swift 

This transition focuses on mobile development. Apple has been heavily promoting Swift as the language for iOS and iPadOS development, gradually moving away from Objective-C. Swift prioritizes safety and readability, offering features like automatic memory management and strong typing. While more relaxed than Rust’s ownership system, Swift’s design choices make it less prone to memory errors than Objective-C.

These transitions highlight a growing trend in software development: the emphasis on security and developer experience. Developers can create more robust and maintainable code by adopting languages with built-in safety features and modern syntax.

Here are some additional points to consider when comparing these transitions:

• Learning Curve: Rust’s unique ownership system causes its learning curve to be steeper than that of Kotlin or Swift.
• Existing Codebase: Java and C# have massive existing codebases, while Swift is primarily used for newer Apple developments, which can influence the pace of adoption of each language.
• Performance: Rust often excels in performance-critical applications, while Kotlin and Swift offer good performance while prioritizing developer experience.

The best choice depends on the specific project requirements and developer skillset. However, the push for languages like Rust and the success of similar transitions make it clear that secure coding practices are shaping the future of software development.

Unosquare is here to help…

The choice between prioritizing security with a language like Rust or balancing it with other factors like developer experience in established languages is complex. Understanding your project’s specific needs and risk profile is crucial. At Unosquare, our Engineering Services Centers of Excellence stay at the forefront of these industry trends. We can help you navigate the evolving landscape of programming languages and choose the right tech for your project. Contact us today and let our experts guide you toward building secure, robust, and future-proof software.

Defining Both Types

What is a Statically Typed Language?

Picture a strict teacher checking your homework before you submit it. That’s the essence of static typing. The compiler, the program’s translator, acts as this teacher, meticulously examining each variable’s data type (e.g., number, text) before execution. Like a pre-flight inspection, this ensures everything is in order, catching potential errors early on. Java, C++, and C# are solid examples of these languages.

What is a Dynamically Typed Language?

Envision a more laid-back scenario with a teacher who trusts you to do the right thing. That’s dynamic typing. Here, the data type is determined when the program runs at runtime. It’s like improvising on the fly, offering flexibility but demanding caution. Python, JavaScript, and Ruby embrace this dynamic approach.

A Quick Comparison of the Data Types:

What are these data types? They define the kind of information a variable can hold. Think of it like labeling boxes: “Numbers only,” “Text please,” or “Anything goes.” In static typing, you explicitly label each box upfront; in dynamic typing, the content dictates the label later.

Exploring the Typed Landscape

The Famous Faces in the Typed World

• Statically Typed: Java, C++, C#, TypeScript, and Go champion predictability and efficiency, often used for large-scale systems and performance-critical applications.
• Dynamically Typed: Python, JavaScript, Ruby, and PHP favor flexibility and rapid development, often seen in web applications, scripting, and data science.

When to Embrace the Static Path: Order & Control Reign Supreme

Static typing is necessary when precision and reliability are paramount, like in-system programming or security-sensitive applications. The concise structure and error detection of static typing offers a safety net. Here’s a closer look at its strengths: 

Early Error Detection: 

Imagine trying to fit a square peg into a round hole. Static typing acts like a pre-fitting tool, ensuring data types match expectations before execution and catching errors early on, preventing runtime crashes and unexpected behavior.

Clearer Code Structure: 

Static typing enforces explicit data types, making code more readable and maintainable. Imagine a well-organized pantry with labeled shelves versus a chaotic jumble. Static types act like those labels, clarifying what each variable holds and how it can be used.

Improved Tooling: 

Static typing enables powerful tools like static analysis and type checkers. These tools can identify issues like unused variables, incompatible types, and logical inconsistencies, further boosting code quality.

Performance Optimization: 

Static typing can lead to performance improvements in specific scenarios. Since the compiler knows the data types beforehand, it can generate more efficient code than dynamic languages, where type checks happen at runtime.

However, it’s important to remember that static typing isn’t a magic bullet:

Increased Development Time: 

Explicitly declaring data types can add initial development time, especially for complex projects.

Reduced Flexibility: 

Dynamic languages offer more flexibility in data manipulation, which can benefit rapid prototyping and exploration.

Potential for Over-engineering: 

More relaxed static typing can lead to unnecessary complexity and boilerplate code, hindering development speed.

The key takeaway is that static typing offers a trade-off: Precision and reliability come at the cost of some flexibility and development time.

When Flexibility Takes Center Stage

Think of creating a prototype or experimenting with data. This point is where dynamic typing thrives, helping you adapt quickly and allowing you to try different things. Its agility is perfect for rapid prototyping, scripting tasks, and web development, where adaptability is critical. Some unique advantages of dynamic typing: 

Rapid Prototyping and Experimentation: 

Imagine molding clay and shaping it according to your creative vision. That’s the essence of dynamic typing’s adaptability. You can quickly try different things, change data types on the fly, and explore unexpected possibilities without getting bogged down by strict type declarations.

Embrace Heterogeneous Data: 

Real-world data rarely fits neatly into predefined categories. Dynamic typing shines here, allowing you to work with diverse data structures effortlessly. Think of handling user input or integrating data from various sources – dynamic languages adapt seamlessly.

Metaprogramming Magic: 

Want to write programs that write themselves? That’s where dynamic typing’s power truly shines. You can create powerful metaprogramming techniques for code generation, customization, and introspection by manipulating code as data.

Scripting and Automation Freedom: 

Need to automate repetitive tasks or interact with dynamic environments? Dynamic languages like Python are the go-to choices. Their flexibility simplifies tasks like file manipulation, web scraping, and system administration.

However, remember that flexibility comes with its own set of challenges:

Runtime Errors

Since type checks happen at runtime, unexpected data types can lead to errors that might only be caught once the program runs, which demands greater vigilance and testing.

Debugging Challenges

Tracing issues in dynamically typed code can be trickier, as type information is only sometimes readily available, and that can slow down debugging processes.

Potential for Chaos:

Unrestricted flexibility can lead to code that’s difficult to understand and maintain, especially in large projects. Transparent coding practices and documentation become crucial.

Which Does Your Project Demand? 

Ultimately, the choice between static and dynamic typing isn’t a competition but a compelling interplay. Both static and dynamic typing continue to evolve to meet different needs. The choice often depends on project requirements, developer preferences, and the language ecosystem. We might see continued growth in hybrid languages that blend static and dynamic typing features, offering flexibility and predictability.

Growth Trends:

• Static Typing: Popular static languages like Python and TypeScript have seen tremendous adoption in recent years, particularly in data science, machine learning, and web development.
• Dynamic Typing: Dynamic languages like JavaScript remain dominant in front-end development and Node.js backend applications. New players like Dart and Kotlin are gaining traction with their robust typing features.

Factors to Consider:

• Specific language choices: While Python and JavaScript might represent the growth of dynamic typing, statically typed TypeScript adoption within the JavaScript ecosystem suggests nuanced trends within language families.
• Emerging Technologies: New trends like cloud computing and serverless functions often favor flexibility, potentially favoring dynamic languages. However, statically typed languages are increasingly optimized for these environments.
• Developer Preferences: Both typing styles have passionate advocates with solid arguments for their benefits. Developer preferences and familiarity with specific languages can influence adoption rates.

What Does Your Project Need?:

For rock-solid foundations and predictable behavior, lean towards static typing.

For agile exploration and rapid iteration, embrace the dynamic flow.

Remember, both approaches have their strengths and weaknesses. Understanding them empowers you to make informed decisions, crafting efficient and expressive code. So, delve deeper, experiment, and find the typing system that resonates with your coding style and project’s demands. 

Unosquare’s Expertise in Both Styles

At Unosquare, understand that choosing the right typing system isn’t a solo performance. It’s a harmonious symphony where melody and rhythm, static and dynamic, work together to create your coding masterpiece. Our expertise, honed over 13 years, ensures we’re not just players but also conductors orchestrating the perfect blend of precision and flexibility for your project. So, whether you envision the structured beauty of static typing or the improvisational flair of dynamic, we’ll be your trusted guides, helping you craft code that resonates with innovation and efficiency. Let’s rewrite the code of your project together in perfect key.

Which Programming Language is Better: Java or C#?

The debate over which language is superior, Java or C#, often centers on their performance, ecosystem, application areas, and who you ask. Java, renowned for its platform independence and robust community support, excels in cross-platform applications. On the other hand, C#, with its deep integration with the Microsoft ecosystem, offers a more streamlined experience for Windows-based applications.

Why Do People Use Java Instead of C#?

Java’s popularity stems from its versatility and widespread adoption. Being platform-independent, it allows for seamless operation across different operating systems, a feature highly valued in web and enterprise-level applications. Additionally, Java’s long-standing presence in the industry has led to a rich repository of libraries and frameworks, facilitating rapid development cycles.

Which Software Development Language is More Popular?

Recent reports indicate a notable shift in the programming landscape, with C# gaining momentum over Java in terms of usage and popularity. According to the Tiobe Index, as of October 2023, C# ranks 5th with a 7.71% rating, closely trailing Java’s 4th place at 8.92%. This gap, which was 1.96% in 2019, has significantly narrowed to 1.21%. Notably, C# has demonstrated the most substantial annual gain among programming languages, increasing by 3.29%, while Java has seen the most significant decline, dropping by 3.92%.

Other sources corroborate this trend. Indeed.com reports a marked increase in job postings for C# developers, contrasting with the relatively stable demand for Java developers. Stack Overflow Trends reveal that since 2018, C# has consistently outpaced Java in popularity among developers. Furthermore, GitHub data shows a faster growth rate for C# repositories than for Java.

Despite these trends favoring C#, it’s crucial to acknowledge that. 

Java still maintains a significant lead in overall popularity. The Java developer community remains of a more considerable size, and Java continues to be the predominant language for Android development. However, the rapid ascent of C# — especially its expanding footprint in mobile, web, and cloud computing — suggests a potential for it to overtake Java in the foreseeable future.

What’s Better for Future-Proofing?

In terms of future-proofing, both Java and C# are continually evolving. Java’s widespread use in enterprise environments and adaptability make it a safe choice for long-term projects. C#, with its strong backing from Microsoft and growing prominence in cloud and gaming applications, also presents a robust option. The decision should align with your project’s specific technological direction and requirements.

The Implications of Generative AI on Java and C#: Navigating the New Landscape

Let’s consider the present and the future of programming with both languages considering the Generative AI explosion in 2023. Generative AI, a revolutionary field leveraging artificial intelligence to create content, poses significant implications for programming languages like Java and C#. As these technologies continue to evolve, understanding their impact on Java and C# is crucial for developers, businesses, and the broader tech community.

Enhanced Development Processes

Generative AI stands to transform the development process for both Java and C#. By automating routine coding tasks, AI can reduce the time and effort required for coding, debugging, and testing. This shift accelerates the development cycle and allows developers to focus on more complex programming and foster innovation.

Impact on Language Evolution

Integrating generative AI into development tools can influence the evolution of Java and C#. As AI grows in its capacity to understand and generate code, it could drive the adoption of new coding patterns, frameworks, and best practices. This evolution might lead to a more streamlined, efficient, and AI-friendly syntax and library support in both languages.

Shift in Skill Requirements

With AI’s capabilities to handle more mundane coding tasks, the skill set required for Java and C# developers may shift. Developers must hone their skills in overseeing AI-generated code, understanding AI-driven development tools, and integrating AI into their applications. This shift emphasizes a blend of technical and soft skills, including critical thinking and strategic oversight.

Enhanced Cross-Language Integration

Generative AI could facilitate greater interoperability between Java and C#. By understanding and translating code from one language to another, AI tools can help bridge the gap between these languages, fostering a more integrated development environment. This capability would be particularly beneficial in complex systems where both languages are utilized. 

Are Java and C# Dying Languages?

Contrary to some opinions, Java and C# are far from dying languages. Considering Java’s immense popularity in enterprise applications and Android app development, its regular updates ensure its continued relevance in the programming world. The language’s adaptability and the community’s commitment to innovation keep Java alive and kicking.

C# has a robust integration in the Microsoft ecosystem, primarily through the .NET framework, and remains a top choice for desktop, web, and mobile application development. Its continuous evolution, marked by regular updates and growing community support, ensures its relevance in emerging areas like game development with Unity and cross-platform applications. The language’s adaptability and alignment with current and future technological trends solidify its position as a vibrant and essential tool in software development.

What type of Application is Java best for?

Java shines in various applications, especially large-scale enterprise solutions, Android mobile applications, and cross-platform desktop applications. Its robust architecture and reliable performance make it a go-to choice for high-traffic web applications and services. The language’s stability and scalability also make it ideal for cloud-based applications.

What type of Application is C# best for?

C# finds its strengths in Windows-based application development, including desktop applications, games using the Unity game engine, and enterprise solutions within the Microsoft ecosystem. The language’s seamless integration with the .NET Framework makes it a preferred choice for web applications on Windows servers. Additionally, C# is gaining traction in developing cross-platform mobile applications through frameworks like Xamarin.

Conclusion

Choosing between Java and C# depends on various factors, including the target platform, application type, and the specific needs of your project. Java offers unparalleled cross-platform flexibility and is ideal for enterprise-level applications, while C# excels in Windows-centric environments and game development. Both languages are robust, versatile, and future-proof, making them excellent choices for modern software development.

Reach out to us to learn more…

For deeper insights into the Java vs C# debate, consider exploring discussions and expert opinions on platforms like Reddit, comprehensive comparisons on Guru99, and performance-focused analysis on Medium. You can also find valuable information on courses and comparisons on Coursera.

Are you looking for more insights? Stay connected to the Unosquare blog for more helpful guides to modern software development, and get in touch today if you need added support for your next project from our experienced and talented development professionals.

How Do Software Developers Work with AI tools?

Integrating AI into the software development lifecycle has brought about a significant paradigm shift. Developers no longer rely solely on traditional coding, testing, and deployment methods. Instead, AI augments their abilities, enabling them to focus on more strategic tasks while automating repetitive and complex processes. AI tools assist in various stages of development, including requirement gathering, design, coding, testing, and maintenance.

AI-powered platforms facilitate enhanced code quality, predictive analytics, and automation that was once deemed unattainable. They introduce machine learning, natural language processing, and deep learning algorithms that accelerate development timelines and ensure the delivery of superior software products.

How is AI Used in Software Development?

The utilization of AI in software development is broad and varied. Below, we highlight ten must-have AI tools that have become indispensable for modern developers.

Automated Testing Tools: AI-driven tools like Appvance and Testim leverage machine learning to enhance test creation, execution, and maintenance, drastically reducing the time and effort required in testing phases.

Bug Prediction: Tools like DeepCode employ AI to predict and identify bugs and vulnerabilities in the code, offering real-time feedback and suggestions for improvement.

AI-Assisted Coding: AI-powered platforms such as TabNine provide real-time coding assistance, offering suggestions and auto-completions, leading to faster and more efficient coding.

Project Management: AI integrates with platforms like Jira and Trello to offer predictive analytics, risk assessment, and intelligent insights, improving project management and delivery timelines.

Requirement Analysis: AI tools process and analyze complex requirement data to ensure alignment between customer needs and software functionality, reducing ambiguities and enhancing software quality.

User Experience Design: Platforms like Pendo and WalkMe utilize AI to gather user data and offer insights into user behavior, aiding developers in creating intuitive and user-friendly interfaces.

Security: AI-driven security tools identify vulnerabilities and potential security breaches through predictive analytics, ensuring robust and secure software solutions.

Code Review: Tools like CodeGuru use machine learning algorithms to review and analyze code, offering suggestions for improvement and optimization.

Deployment: AI facilitates continuous integration and continuous deployment (CI/CD), automating the deployment process and ensuring efficient and reliable delivery of software products.

Maintenance and Support: AI tools predict system downtimes, automate routine maintenance tasks, and offer intelligent support solutions, reducing operational costs and enhancing software reliability.

Will AI Replace Programmers in 10 Years?

The integration of AI in software development does not signal the end for human programmers. Instead, it begins a symbiotic relationship where AI augments human capabilities. AI handles routine, monotonous tasks, allowing developers to focus on complex, creative, and innovative aspects of software development. Programmers will continue to play a crucial role in decision-making, strategic planning, and creative innovation. AI tools are here to enhance, not replace, the human element in software development.

Exploring Recent Insights on AI Integration in Software Development

Over the past six months alone, a crescendo of voices from futurists and journalists alike has offered diverse perspectives, echoing the optimism and caution surrounding AI’s burgeoning role. 

The Optimism: Amplified Efficiency and Innovation

Futurists have often lauded the transformative power of AI, and recent months have not been an exception. They project a future where the synergy between AI and human intelligence leads to an unprecedented era of innovation. The expectation is that AI tools for software development will continue to evolve, boasting enhanced predictive analytics, automation, and learning capabilities. Futurist Ben Ross depicts AI as an instrument of empowerment for developers, unlocking new frontiers of creativity and innovation.

Journalists covering the tech industry have underscored the proliferation of AI tools that automate mundane tasks, enabling software engineers to focus on strategic and creative aspects of development. A recent article in Wired magazine highlighted an AI-powered code review tool that identifies bugs and learns and adapts to the developers’ unique coding styles, promoting enhanced efficiency and personalized assistance.

The Caution: Ethical and Security Concerns

However, optimism is often tempered by caution. Ethical and security considerations have been at the forefront of discussions. Journalists have drawn attention to the potential biases and ethical dilemmas embedded in AI systems. A piece in The Verge recently scrutinized AI’s decision-making processes, emphasizing the need for transparency, accountability, and ethical guidelines to mitigate risks and unintended consequences.

Futurists, too, have echoed these concerns. In a comprehensive analysis by renowned futurist Amy Webb, the integration of AI in software development is depicted as a double-edged sword. While it harbors immense innovation potential, rigorous ethical frameworks and security protocols are necessary to avert exploitation and biases.

The Projection: A Collaborative Future

In navigating between optimism and caution, a consensus emerges – the future of AI in software development is neither bleak nor utopian. It’s collaborative. Futurists and journalists agree that AI is not a replacement but an augmentation of human capabilities. A recent feature in Forbes illustrated this with case studies of companies where AI and human intelligence coalesce seamlessly, leading to enhanced problem-solving, innovation, and productivity.

Concluding Thoughts

The discourse surrounding AI’s role in software development is as complex as it is dynamic. It encapsulates a spectrum of perspectives, each contributing to the multifaceted narrative of promise, potential, and precaution. As AI continues to weave itself into the fabric of software development, the insights of futurists and journalists will remain instrumental in shaping a future where technology and humanity coexist, collaborate, and innovate with a synergy that transcends the sum of its parts. The ethical, secure, and responsible integration of AI tools for software development remains the cardinal focus, ensuring that the journey ahead is marked by innovation that is both groundbreaking and grounded in ethical principles.

AI tools for software development are not just a trend but a significant advancement that is here to stay. They enhance efficiency, reduce costs, and ensure the delivery of high-quality, reliable software. The synergy between AI and human intelligence will continue to drive innovation, with AI handling complexity and volume and humans steering creativity and strategy. The future of software development lies in the harmonious integration of AI tools and human expertise, leading to unprecedented levels of innovation and excellence in the field.

Reach out to us today to speak with the leaders of Unosquare’s Software Development Centers of Excellence and find out how we use AI to enhance our capabilities and our world-class digital product development and design capabilities. 

What is the .NET Framework?

The .NET Framework is a software development platform designed for Microsoft Windows. It provides a runtime environment and a variety of programming languages like C#, VB.NET, and F#. The framework is designed for the development, deployment, and execution of applications and services that use .NET technologies. That includes desktop applications, web applications, and web services.

The Pros and Cons of the .NET Framework

Where do you start if you don’t know much about the framework? Next, we’ll explore some of the  advantages and disadvantages of the .NET Framework:

Advantages

Interoperability: One of the standout features of .NET is the ability to communicate effortlessly with software written in a variety of languages. This leads to smoother integration with existing systems.

Security: With features like code access security and role-based permissions, .NET’s multi-layered approach makes it a secure choice. Microsoft also frequently releases security updates and patches, which help developers protect against threats.

Scalability: The framework ensures that systems are efficient and maintainable in the long run. Built-in features like load balancing and caching optimize data and resource management make it easy to adapt, implement changes, and handle growth. 

Disadvantages

Windows-Centric Nature: The traditional .NET Framework leans heavily towards Windows. That may not always be ideal for cross-platform applications. However, the introduction of .NET Core does address that limitation. (More information on this is below.)

Memory Overhead: The .NET Framework requires more system resources for its runtime environment. This leads to increased RAM usage. That can make .NET applications less efficient.

The Learning Curve: The number of supported languages, libraries, and frameworks makes the learning curve for .NET a bit steep. The feature-rich environment creates many possibilities, but it also makes it difficult to jump into.

The Pillars of .NET Framework: Features and Components

Specific functionalities come together to help developers build robust, secure, and scalable applications in the .NET Framework. Here’s a closer look at what they are:

Common Language Runtime (CLR)

This is the virtual machine component of the .NET Framework responsible for execution. It handles runtime tasks like loading and executing code and memory management. The CLR also provides services like exception handling and debugging, so the application runs securely and efficiently.

Framework Class Library (FCL)

FCL is a comprehensive collection of reusable classes, interfaces, and data types. It provides core functionalities like file I/O, data serialization, and network communication. Developers can use these predefined classes to perform common tasks to accelerate the development process and reduce the chance of errors.

ASP.NET

This ecosystem within the framework is specifically designed for building web applications and services. ASP.NET also provides built-in security features like authentication and authorization.

ADO.NET

This is the data access component used for connecting to databases and manipulating data. It provides a set of classes for executing queries and handling results.

Windows Presentation Foundation & Windows Communication Foundation

WPF (Windows Presentation Foundation) is used for building rich desktop applications with advanced UI capabilities. WCF (Windows Communication Foundation) is a framework used for building service-oriented applications.

.NET Framework vs. .NET Core: Spotting the Differences

Need help making sense of .NET Framework and .NET Core? We’ve got you covered with a look at how they compare.

Cross Platform Capabilities

.NET Framework: The traditional .NET Framework is closely tied to Windows APIs and services, making cross-platform support a challenge.

.NET Core: In contrast, .NET Core is built on the principle of “code once, execute anywhere,” allowing for cross-platform compatibility with Windows, macOS, and Linux.

Modularity 

.NET Framework: This is a monolithic framework that typically installs as a single package, including a wide range of libraries and features.

.NET Core: Designed with modularity in mind, .NET Core lets you include just the libraries and modules your application needs.

Performance

.NET Framework: While it’s robust and feature-rich, the bulkiness of the framework can sometimes lead to slower application runtimes.

.NET Core: Offers optimized performance. This makes it more ideal for applications where performance is a critical factor, like microservices or large-scale web apps.

Support & Evolution

.NET Framework: Updates for the .NET Framework are mostly limited to security patches and bug fixes.

.NET CORE: This reflects a more current framework ecosystem, with active development and regular feature updates.

.NET Framework and C#: Two Sides of the Same Coin?

It’s a common misconception, but they aren’t the same. The .NET Framework is an overarching development platform. C# is a programming language. C# is one of the languages for .NET Framework, but it’s not the only language that’s compatible with the framework. 

When Should You Choose the .NET Framework?

Once you understand .NET and .NET CORE, how do you decide what to use for your project? Here’s a closer look at some of the factors to consider. 

Legacy Compatibility

If your enterprise is running older applications written in .NET Framework, sticking with it allows you to maintain, update, and extend older applications without the risks or costs associated with migrating to a new platform.

Windows-Oriented Development

For projects with a primary focus on the Windows ecosystem, the .NET Framework is ideal. This allows you to make full use of Windows-specific capabilities.

Compliance

For some industries where compliance is a concern, the fact that the .NET Framework is well-established may be more aligned with guidelines, making it a safer choice for meeting your obligations.

It’s Your Turn to Choose a Framework

Now you have the knowledge you need to determine if the .NET Framework is right for you or if you should go with .NET Core. The framework you choose impacts the development process as well as the scalability, security, and long-term maintenance of your application. There isn’t a one-size-fits-all solution, so select the one that will work best for your specific needs.

Are you looking for more insights? Stay connected to the Unosquare blog for more helpful guides to modern software development, and get in touch today if you need added support for your next project from our experienced and talented development professionals.

Definition of sprint planning: What it means in software development

Sprint planning is a cornerstone of Agile software development projects. It’s an event where the Scrum team comes together to plan work, called a “sprint” for the upcoming two to four weeks. Efficient sprint planning is essential to project success. It enables clear communication, fosters collaboration, and sets realistic objectives for the entire team.

Overview of sprint planning in the Agile Framework

Within the Agile framework, sprint planning is an event that kickstarts each sprint. It allows the team to decide what work needs to be accomplished. Sprint planning primarily aims to set a sprint goal and define a set of backlog items that support the goal. 

Sprint planning embraces flexibility and iterative progress. It breaks the project into manageable tasks, allowing for precision and adaptability in the software development landscape. Unlike fixed project plans, sprint planning evolves with the project.

Going through the sprint planning process helps facilitate growth and adjustment. That makes it an essential tool in changing expectations and environments. It also makes it easier to combat some of the overwhelm that can come along with the software industry. 

6 steps in solid sprint planning

Are you thinking about implementing sprint planning for your organization? Before you jump in, look at the steps that go into planning effective sprints for your team.

Backlog refinement

Review the list of tasks or user stories that may already exist. Doing this step helps ensure the backlog is always up to date.

Prioritizing

Rank tasks based on business value, risk, and dependencies. This empowers the team to focus on what’s most important or urgent.

Breaking down tasks

Divide larger tasks into smaller units of work. This way, your team can gain clarity on the specific steps they need to take. That makes the workload more manageable and estimations more accurate.

Defining a Sprint goal

Outline what the sprint will achieve to provide direction. The sprint goal should be in sync with overarching business objectives.

Capacity assessment

Consider the total available hours that your team has during the sprint. Assign tasks based on individual expertise and skills to maximize productivity and quality.

Task estimation

The entire team should participate in the estimation process. That way, they can leverage their collective expertise and come to a consensus about timelines.

Who takes responsibility in sprint planning?

Research shows that 86% of Scrum teams hold a sprint planning meeting, but who takes on what role? This role is what different members of your team can expect to do.

The Scrum Master facilitates the planning process. Their role is to ensure that the planning is productive and stays on track.

The development team actively participates in planning, estimating, and committing to work for the upcoming sprint.

The Product Owner brings the product backlog to the table, shares user stories, and helps the team understand each item’s business value and priority.

Best practices for sprint planning

Sprint planning can make or break your sprint’s outcome, and getting it right requires careful consideration. To ensure a smooth, efficient, and successful sprint planning session, let’s dive into some best practices.

Clear communication and collaboration

Communication is at the core of successful sprint planning. It’s crucial that every team member feels heard and understood. To support this, facilitate an environment that promotes open dialogue, active listening, and mutual respect. 

Then, clarify the sprint planning session’s purpose, process, and expected outcomes so that everyone is on the same page. This clarification will set the tone for planning and help the sprint go more smoothly.

Decision-making

An effective decision-making process is also inclusive. Each individual brings their understanding, experiences, and expertise. Leveraging these can result in more well-rounded decisions.

Involving everyone in the decision-making process creates a sense of ownership and commitment to the sprint goals. When team members feel their contributions matter, they’re more likely to be invested in the sprint’s success.

Finding balance

Tasks should be reasonable units of work with a clear execution path. Aim for a balance. All tasks should be manageable but comprehensive enough to deliver value.

Looking at dependencies and technical constraints

Understanding dependencies between tasks is critical. With this insight, you avoid encountering unexpected roadblocks mid-sprint. You also need to know about technical constraints that might affect the order of completion. 

Thinking through timelines

The importance of aligning on timing, and being reasonable should not be overlooked. Goals that are too ambitious can lead to compromised quality, increased stress, and burnout. Even with stellar project management, expect some hiccups along the way. Plan for that when you set your sprint goals. 

Conducting retrospectives

Retrospectives provide an opportunity for the team to reflect, discuss what went well, find out what could be improved, and determine actionable steps to make the next sprint even better. In fact, teams that have regular sprint retrospectives have 24% more responsiveness and 42% higher quality with less variability than teams with infrequent or no retrospectives.

Ready to start planning your next sprint?

Sprint planning, when done effectively, can transform your process. Just like an art, good sprint planning takes practice, but the benefits are worth the effort. When you adopt and adapt it to your team’s needs, you’ll see improvement in your agile development journey.

Looking for more insights? Stay connected to the Unosquare blog for more helpful guides to modern software development, and get in touch today if you need added support for your next project from our experienced and talented development professionals.