#performance  testing

Technical debt is often discussed as an engineering issue caused by rushed code, poor testing, or outdated systems. But in reality, technical debt is rarely created by engineers alone. It is the outcome of leadership decisions, priorities, and trade-offs made over time. That’s why technical debt is fundamentally a leadership problem, not just an engineering one. 

As organizations scale digital products, adopt cloud computing, and integrate artificial intelligence into core systems, unmanaged technical debt quietly becomes a barrier to growth, innovation, and resilience. 

Before exploring why leadership plays such a central role, it helps to clarify what technical debt really means in today’s enterprise context. 

What Technical Debt Really Looks Like Today 

Technical debt is not just messy code or legacy systems. In modern enterprises, it shows up as: 

Rigid architectures that slow down product modernization 

Fragile APIs that limit integration with AI tools or analytics platforms 

Manual processes that resist DevOps and automation 

Outdated software development life cycle practices that delay releases and increase risk 

These issues directly affect software engineering velocity, system reliability, and the ability to scale platforms using cloud-native and hybrid architectures. Over time, they increase operational costs and reduce an organization’s ability to respond to market changes. 

Many of these challenges emerge when teams operate without structured software development life cycle models that balance speed, quality, and long-term maintainability. 

However, engineers rarely choose to build fragile systems intentionally. The real drivers sit higher up. 

How Leadership Decisions Create Technical Debt 

Technical debt usually starts with leadership trade-offs, not technical incompetence. 

Common leadership-driven causes include: 

Prioritizing short-term delivery over long-term scalability 

Deferring platform engineering investments to hit business deadlines 

Treating refactoring and testing as “optional” work 

Pushing rapid prototyping into production without governance 

Underfunding data engineering and security foundations 

These decisions may look practical now, especially under pressure to launch mobile applications, CRM features, or AI chatbot initiatives quickly. But over time, they accumulate debt that slows teams down and increases risk. 

However, recognizing how technical debt is created is only half the story; the real challenge lies in understanding why engineering teams cannot resolve it on their own. 

Why Engineers Can’t Fix Technical Debt Alone 

Engineering teams are often asked to “clean things up” while still delivering new features. This creates an impossible situation. 

Without leadership support, engineers face constraints such as: 

No time allocated for refactoring or architectural improvements 

KPIs focused only on feature output, not system health 

Limited authority to modernize legacy ERP or CRM systems 

Pressure to adopt AI tools or LLMs on unstable foundations 

As a result, technical debt continues to grow even when teams are highly skilled and motivated. 

This is why addressing technical debt requires leadership-level ownership, not isolated engineering effort. 

Technical Debt in the Age of AI and Cloud 

Technical debt becomes even more visible when organizations adopt artificial intelligence, cloud-native platforms, and data analytics. 

AI systems depend on: 

Clean data pipelines and strong data modeling standards 

Reliable APIs and authentication mechanisms 

Scalable cloud computing infrastructure 

Secure SDLC practices 

When these foundations are weak, AI initiatives fail to scale. Leaders often interpret this as an AI problem, when it is actually an architectural and governance issue rooted in accumulated technical debt. 

What Leadership Ownership Looks Like in Practice 

When leaders treat technical debt as a strategic issue, priorities shift. 

Effective leadership actions include: 

Allocating time and budget for refactoring and modernization 

Embedding quality, testing, and security into SDLC goals 

Measuring system health alongside delivery velocity 

Supporting DevOps, automation, and cloud-native adoption 

Aligning product roadmaps with long-term architecture goals 

This mindset enables teams to deliver faster over time, not slower. 

Leadership teams that embrace digital transformation strategies and modern software development life cycle models create systems that evolve gracefully instead of degrading under pressure. 

Reframing Technical Debt as a Business Risk 

Technical debt is not just a technical inconvenience it is a business risk. Unchecked debt leads to: 

Slower time-to-market 

Higher operational and cloud costs 

Increased cybersecurity exposure 

Reduced ability to adopt AI, analytics, and automation 

By reframing technical debt as a leadership responsibility, organizations can move from reactive fixes to proactive system design. 

The Way Forward 

Technical debt does not disappear on its own. It either gets managed intentionally or compounds silently. 

When leaders take ownership by investing in architecture, data engineering, platform engineering, and modern SDLC practices, engineering teams are empowered to build scalable, secure, and future-ready systems. 

If your organization is struggling with slow delivery, fragile platforms, or stalling AI initiatives, it may be time to address technical debt at the leadership level. 

In this paper, we will explore the key principles of agile testing, its benefits, and how it can be implemented successfully in organizations.

Agile testing is an approach to software testing that emphasizes the need for continuous testing and feedback throughout the software development lifecycle. This approach is based on the principles of the Agile Manifesto, which emphasizes collaboration, flexibility, and customer satisfaction. Agile testing has become increasingly popular in recent years as organizations seek to deliver…

How to Choose the Right Performance Testing Tool

Guys!!! Performance Testing!!! Okay so I started it yesterday morning and nailed the curl-ups because I only had to go up to where my elbows were touching my thighs, not my knees like when I had practiced.

Then Sit and Reach was great, like the only thing I knew I could pass, and got 25 inches and saw 30 on the standards and was like hold up I didn’t pass? but no it was 30 cm and I got 48 cm like the weirdly flexible punk I am.

Then pushups were rough as heck. Like I do pushups from my knees cause I can do them with good form that way, but I just kinda sucked it up for this one time and got 30 (the goal) and the last ones had awful form but whatever I did it.

Shuttle run I did this afternoon and was close to the time but like I didn’t want to redo it a million times. Ugh them mile running, okay so previously my best was 9:30 and I tried really hard and I’m pretty happy with my time.

By Sapan Panigrahi and Deepesh Mittal

Today, we are pleased to offer Daytona, an open-source framework for automated performance testing and analysis, to the community. Daytona is an application-agnostic framework to conduct integrated performance testing and analysis with repeatable test execution, standardized reporting, and built-in profiling support.

Daytona gives you the capability to build a customized test harness in a single, unified framework to test and analyze the performance of any application. You’ll get easy repeatability, consistent reporting, and the ability to capture trends. Daytona’s UI accepts a performance testing script that can run on a command line. This includes websites, databases, networks, or any workload you need to test and tune for performance. You can submit tests to the scheduler queue from the Daytona UI or from your CI/CD tool. You can deploy Daytona as a hosted service in your on-prem environment or on the public cloud of your choice. In fact, you can even host test harnesses for multiple applications with a single centralized service so that developers, architects, and systems engineers from different parts of your organization can work together on a unified view and manage your performance analysis on a continuous basis.

Daytona’s differentiation lies in its ability to aggregate and present essential aspects of application, system, and hardware performance metrics with a simple and unified user interface. This helps you maintain your focus on performance analysis without changing context across various sources and formats of data. The overall goal of performance analysis is to find ways of maximizing application throughput with minimum hardware resource and the best user experience. Metrics and insights from Daytona help achieve this objective.

Prior to Daytona, we created multiple, heterogenous performance tools to meet the specific needs of various applications. This meant that we often stored test results inconsistently, making it harder to analyze performance in a comprehensive manner. We had a difficult time sharing results and analyzing differences in test runs in a standard manner, which could lead to confusion.

With Daytona, we are now able to integrate all our load testing tools under a single framework and aggregate test results in one common central repository. We are gaining insight into the performance characteristics of many of our applications on a continuous basis. These insights help us optimize our applications which results in better utilization of our hardware resources and helps improve user experience by reducing the latency to serve end-user requests. Ultimately, Daytona helps us reduce capital expenditure on our large-scale infrastructure and makes our applications more robust under load. Sharing performance results in a common format encourages the use of common optimization techniques that we can leverage across many different applications.

Daytona was built knowing that we would want to publish it as open source and share the technology with the community for validation and improvement of the framework. We hope the community can help extend its use cases and make it suitable for an even broader set of applications and workloads.

Architecture

Daytona is comprised of a centralized scheduler, a distributed set of agents running on SUTs (systems under test), a MySQL database to store all metadata for tests, and a PHP-based UI. A test harness can be customized by answering a simple set of questions about the application/workload. A test can be submitted to Daytona’s queue through the UI or through a CLI (Command Line Interface) from the CI/CD system. The scheduler process polls the database for a test to be run and sends all the actions associated with the execution of the test to the agent running on a SUT. An agent process executes the test, collects application and system performance metrics, and sends the metrics back as a package to the scheduler. The scheduler saves the test metadata in the database and test results in the local file system. Tests from multiple harnesses proceed concurrently.

Architecture and Life Cycle Of A Test

Looking Forward

No memory leaks.

Okayyy so actual wrestling stuff is coming up this week. Monday there’s a meeting which I’m kinda nervous about cause like half the kids don’t know this is something I’m doing. Um then I have performance testing sometime between Wednesday and Friday.

Ugh I’m actually pretty nervous, mostly about pull-ups and the mile run, I’m just worried I’m not gonna be able to preform up to standards.