#software testing

TLDR: It's become obvious I need to dig deeper into Android app development.

My first smartphone (2014) was an LG930 Android. I chose Android because I wanted to write apps for it. I still carry an Android, and I've been publishing Android versions of my libraries since 2020, but somehow I never got around to writing and installing my own apps.

This goes against my stated philosophy of not publishing software I can't test. I knew people were using my libraries in Android apps, and I trusted they would inform me if there were serious issues. Apparently I was mistaken.

Back in October I finally created a very simple Android test app for my Jolt JNI library, and it seemed to work, so I relaxed. Then last week I wrote a more thorough test and found a major problem with my library that would cause any realistic Android app to fail: JVM code could invoke native C++ code, but if native code tried to call back into the JVM, the app would usually crash.

I hastily published a new release (v3.8.0) that solves the callback issue. Mea culpa.

I strongly suspect my older Libbulletjme library has an analogous bug, likely going back to 2020. But in order to be sure (and to test the fix, when I write one) I'll need an Android test app for Libbulletjme ASAP.

Technical debt is often discussed as an engineering issue caused by rushed code, poor testing, or outdated systems.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Outdated software development life cycle practices that delay releases and increase risk 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. 

So I've recently moved back into my own place after living on my parents and then my sisters couch for the last 9 months. There's been a fair few things that I've needed to buy, I've had birthdays, food and new bills etc. etc. to the point where I'm left with less than 150 until the 22nd. I'm not e-begging, I do not want your money in return for nothing

Instead I'm asking for you to hire me for something in my wheelhouse to earn the money.

Realistically I shouldn't need more than an extra 100 to last me, which would work out between 2 - 5 of these jobs (depending on their size)

I have experience and/or the knowledge to be able to do the following:

Test a Game/Website/Mobile app/Software/SaaS program 4+ years of experience as a QA Tester/Test Engineer), 2.5 of these as a sole tester on multiple projects

Voice over/voice acting work

Video editing - Podcast, Let's Play, Long-form YouTube Content, Stream Highlights

Writing and Editorial work, such as: - Expansion of notes into full works (blog post, report, essay, etc.) - Review of content drafts for grammar/readability/localisation accuracy/correct referencing styles - Resume/Cover letter creation and/or review - Transcription/Subtitling

Creation of Foley sound effects

I.T Support - Hardware + Software troubleshooting

I know that a lot of these posts are focused more so on art commissions - and my art is not good lmao - but thought it's worth shooting my shot.

In a world where financial accountability is non-negotiable, Atcuality provides tools that ensure your field collections are as reliable as your core banking or ERP systems. Designed for enterprises that operate across multiple regions or teams, our cash collection application empowers agents to accept, log, and report payments using just their mobile devices. With support for QR-based transactions, offline syncing, and instant reconciliation, it bridges the gap between field activities and central operations. Managers can monitor performance in real-time, automate reporting, and minimize fraud risks with tamper-proof digital records. Industries ranging from insurance to public sector utilities trust Atcuality to improve revenue assurance and accelerate their collection cycles. With API integrations, role-based access, and custom dashboards, our application becomes the single source of truth for your field finance workflows.

In a rapidly changing technology environment, headlines like those below are becoming too familiar:

TCS lays off 12,000+ employees due to skill mismatch.

Infosys to hire 20,000 freshers who have been trained in AI, GenAI & Cloud.

Capgemini India will hire up to 45,000 in 2025; AI-ready workforce.

This highlights a split in strategic approaches within the Indian IT sector but this isn’t about company strategy, it’s about skills.

TCS Layoffs: A Wake-Up Call for Outdated Skillsets

TCS’s new workforce changes were apparently due to “skill mismatches,” not downsizing. Traditional manual testing roles, legacy systems, and old software stack roles are being eliminated for the introduction of new age technology. If your resume is still from 2015, you might already be in trouble.

Infosys & Capgemini: Leading the AI-First Revolution

While TCS cuts down, Infosys is reskilling over 2.75 lakh employees in futuristic domains like:

Generative AI (GenAI)

Cloud-Native Development

Robotic Process Automation

Prompt Engineering

AI-Powered Software Testing

Meanwhile, Capgemini is looking to employ 40,000–45,000 in 2025, with close to 40% lateral hires, as they are looking to future-proof their business with an AI-skilled workforce. This is not just recruitment — it’s strategic skill acquisition.

So, to summarize: Jobs are not disappearing, Skills are changing rapidly

Companies will recruit based on degrees and experience, moving on to recruit based on relevant, future-proof skills.

Here’s how the industry is shifting:

Manual Testing is now being replaced by AI-powered automation, enabling faster, smarter, and more accurate testing.

Legacy systems are giving way to cloud-native DevOps, making development and deployment more agile.

Traditional QA processes are evolving into GenAI-integrated testing, where AI assists in test case generation, defect detection, and optimization.

Predictable, single-role positions are transforming into multi-disciplinary skillsets, where professionals are expected to handle testing, automation, AI tools, and cross-functional collaboration.

Whether you’re a recent graduate, a mid-level QA engineer, or a senior-level architect, upskilling is no longer optional. It is your lifeline to remain employed and relevant.

Why Upskilling Matters More Than Ever

The Fourth Industrial Revolution has already arrived — and AI is at the center of it all. AI, GenAI, and LLMs (like ChatGPT, Gemini, and Claude) are taking over many of the critical roles and tasks of testing, development, and even project management. Job roles are evolving, as one might expect. If you don’t evolve, you will simply be replaced — not by chatbots or artificial intelligence but by practitioners who understand how to use AI.

The reality is?

It’s not your job being replaced. It’s your outdated skill set.

The Role of QA in the Age of GenAI

Quality Assurance is not about running manual test cases any longer or logging bugs. To be a quality assurance professional today, you should be:

AI-aware and automation-ready

Skilled in tools like Selenium, TOSCA, and Cypress

Familiar with Prompt Engineering and LLM-based test generation

Fluent in performance and API testing

Able to collaborate with AI to test faster and smarter

That’s where TestoMeter steps in.

TestoMeter — Your Reskilling Partner in the AI Era

At TestoMeter, we don’t just teach tools — we empower professionals with job-ready, AI-powered skills.

What We Offer:

1. GenAI for Testers — Learn prompt engineering, LLM testing, and synthetic data generation 2. AI-Powered Automation Testing — Master Selenium + AI, TOSCA, API, Cypress 3. Industry-Ready Certifications — ISTQB CT-GenAI, CTFL, CT-AI, PSM, PSPO, etc. 4. Hands-on Labs & Real Projects — Experience-based learning guided by experts 5. Customized Learning Paths — For freshers, mid-level engineers, and QA leads

Live Sessions + Mock Exams + Placement Support

No fluff, No filler, Just future-ready training that gets you hired.

Top Trending Skills to Learn in 2025

These are the hot skills recruiters and CTOs are hiring for:

AI & GenAI in Software Testing

Prompt Engineering for QA & DevOps

Low-Code / No-Code Automation

API Testing with Postman & JMeter

Cloud-Based Test Infrastructure (AWS, Azure)

LLM-based Test Data Generation & Optimization

Who Needs to Act Now?

Freshers wondering if manual testing is still a safe choice.

Working professionals in testing roles who haven’t upskilled in 6–12 months.

Project Managers and Team Leads unsure how to integrate AI into testing.

Anyone impacted by layoffs or job insecurity.

Don’t Wait for a Layoff Email

The writing is on the wall.

The layoffs are real.

The AI shift is irreversible.

But you still have a choice: Choose to grow, Choose to evolve, Choose TestoMeter.

developers trying and reiterating that i should retest differently instead of actually trying to test it themselves

developers that have never, ever followed steps to reproduce bugs because it's easier to assume it's not a problem, blame me for doing it wrong, or insisting it's a data issue, or ask me to do it again

and when it's a data issue, it's Somebody Else's Problem and it's Not Related to the issue and So We Can Close The Defect? And Also Please Close Your Other Tasks Because We Don't See You As Capable Enough To Do Your Own Housekeeping?

Since our last release, we've introduced several significant updates, with one of the key enhancements being proposals to change group settings. This enables group members to collectively shape the group's overall functionality and tailor it to better suit their needs and preferences as the group evolves over time.

In addition to the ability to propose changes, we've introduced the following group settings:

Decision Making Model - You can now choose from consensus or consent decision-making to determine how decisions are made in the group

Admin Model - Choose from No Admin or Standard to determine how the group is managed. No Admin ensures all changes are made through proposals, while Standard allows for customizable admin roles

Ratification Threshold - Set the minimum amount of positive votes required for a proposal to be ratified under consensus

Voting Time Limit - Set a time limit for how long proposals can be voted on before they're closed or ratified. This can be set with a dropdown or a date picker

Stand Asides and Reservations Limit - Set the acceptable number of stand asides or reservations for a proposal to pass

These updates aim to offer greater customization for groups, while also showcasing how software can empower communities to collectively shape the digital spaces they occupy.

For anyone interested in helping to test the new features, feel free to hit us up on here or Discord.