UAT Testing: How User Acceptance Tests Catch Bugs Before Production

Test pipeline
User Acceptance Testing (UAT), also known as beta or end-user testing, is the process of having actual users or clients evaluate software to determine whether it meets acceptance criteria. This final testing phase is performed after functional, system, and regression testing are complete. The primary purpose of UAT is to validate the software against business requirements. This validation is carried out by end-users who have direct familiarity with the business rules and workflows the software must support. UAT, alpha testing, and beta testing are distinct types of acceptance testing. Because user acceptance testing is the last verification step before software goes live, it represents the final opportunity for the customer to evaluate the software and confirm it is fit for its intended purpose.

User Acceptance Tests bridge the gap between "feature-complete" and "production-ready" by surfacing requirement mismatches that automated unit and integration tests cannot detect. UAT is especially critical in domains with complex business logic -- such as financial reconciliation, healthcare workflows, or regulatory compliance -- where the cost of a post-release defect far exceeds the cost of an additional testing cycle. Teams that integrate UAT feedback loops into their CI/CD pipelines, rather than treating UAT as a one-time gate, tend to ship with significantly fewer escaped defects and shorter rollback cycles.

Software Development, Testing

Android vs iOS: How Android Captured 85% of Global Smartphone Sales

Android vs iOS
Samsung's success propelled the entire Android alliance forward. By 2012, Android phone sales had reached six times the level of two years earlier, outselling iPhones 4 to 1. While courtroom battles between Apple and Android proxies like Samsung were just beginning, it was becoming evident that Android was winning the market and bringing smartphones to a broader global audience. China was the next frontier. The government's drive for economic growth led to remarkably rapid deployment of fast mobile internet networks across the country. Samsung initially dominated in China before the booming urban middle class gravitated toward Apple, but the biggest winners were domestic smartphone makers such as Huawei Technologies Co. and Xiaomi Corp., which built customized versions of Android without Google apps. Google's web services have been officially inaccessible in China since the company pulled back its operations from the country in 2010.

The Android vs iOS market share story is fundamentally about Android's open-source licensing model enabling OEM diversity at every price point, from sub-$100 devices in Southeast Asia to $1,000+ Samsung flagships. This contrasts sharply with iOS's single-vendor vertical integration. The strategic consequence is significant: Android's dominance in emerging markets (India, Africa, Latin America) means that the next billion internet users will overwhelmingly access the web through Android, shaping API design, Progressive Web App adoption, and mobile-first development priorities for the foreseeable future. China's fork of Android without Google Play Services also created a parallel app ecosystem that continues to challenge Western assumptions about mobile distribution.

Linux, Android, iOS

Linux 5.5 Filesystem Benchmark: XFS vs EXT4 vs F2FS vs Btrfs on SSD RAID

XFS vs EXT4 vs F2FS - various RAID options
To summarize the otherwise diverse results, the geometric mean of all Linux storage benchmarks shows that on a single Samsung 860 EVO SSD, XFS was the fastest filesystem, followed by F2FS and EXT4, while Btrfs with its default configuration (copy-on-write, etc.) was the slowest. In RAID0, Btrfs and F2FS performed best, while in RAID1, XFS was the standout performer. The results with RAID5 and RAID6 were fairly close, and when moving to RAID10, F2FS was substantially faster than the others.

These Linux 5.5 benchmarks reveal that no single filesystem dominates across all RAID configurations, which makes filesystem selection a workload-specific decision rather than a universal one. XFS excels on single-disk and RAID1 setups due to its mature allocation group parallelism and efficient metadata journaling. F2FS's strong RAID10 performance stems from its log-structured design optimized for NAND flash translation layers, reducing write amplification. Btrfs's RAID0 gains likely come from its built-in striping implementation bypassing the MD layer, though its copy-on-write overhead penalizes single-disk scenarios. For production servers, the choice between XFS and EXT4 often comes down to whether you need XFS's superior scalability beyond 16 TB volumes or EXT4's simpler recovery tooling.

Linux, XFS, F2FS, EXT4

Java 14 JFR Streaming: Real-Time JVM Monitoring with Under 1% Overhead

The HotSpot VM emits more than 500 data points using JFR, most of which are not available through other means besides parsing log files. To consume this data today, a user must start a recording, stop it, dump the contents to disk, and then parse the recording file. This approach works well for application profiling, where at least a minute of data is typically recorded at a time, but it is not suitable for monitoring purposes. An example of monitoring usage is a dashboard that displays dynamic, real-time updates of the data.

JEP 349 in Java 14 transforms Java Flight Recorder (JFR) from a post-mortem profiling tool into a live observability platform by enabling continuous event streaming without the dump-to-disk bottleneck. Previously, JFR and Java Mission Control (JMC) were proprietary Oracle commercial add-ons; their open-sourcing into OpenJDK democratized access to production-grade JVM telemetry. The streaming API allows direct integration with metrics pipelines like Prometheus or Grafana, exposing over 500 HotSpot data points -- including GC pause durations, thread contention events, and allocation rates -- at under 1% CPU overhead. This positions JFR streaming as a viable alternative to JMX polling for real-time dashboards, with the advantage of capturing transient events that polling intervals would miss entirely.

Java, Software Development

DevSecOps Explained: Shift-Left Security in the CI/CD Pipeline

Bart Copeland, ActiveState CEO: The purpose of DevSecOps is to create the mindset within the enterprise that everyone is responsible for security. The "Sec" means adopting security as a key component that is fully integrated throughout the software development process. That means making security a part of an application's DNA, starting from initial conception all the way through to release. The DevSecOps philosophy is that security should be embraced and improved upon by everyone within the organization. It should also be supported by those with the skills to contribute security value to the system. Security practices have to move at the speed of the rest of development. For example, developers must resolve common open source issues earlier in the software development life cycle, decreasing costs and speeding time to market. This means security considerations are moved up front, shifted-left as much as possible to the developer. In other words, security is baked into the development process. But does this mean that security is shifted entirely onto the already heavily loaded shoulders of the developer? Of course not. Security is all about protection in depth, from the edge of the network down through the application to the data layers, operating systems, and the people involved. However, it does mean that developers need the tools that can help automate and bake in as much security as possible. Ideally, baking in security at the time the code is written and when architectural decisions are being made.

DevSecOps codifies a principle that mature engineering organizations have practiced for years: security is a continuous process, not a final checkpoint. The practical implementation requires specific tooling at each pipeline stage -- static analysis (SAST) during code review, software composition analysis (SCA) for dependency vulnerabilities, dynamic analysis (DAST) in staging environments, and runtime application self-protection (RASP) in production. The shift-left model reduces remediation costs by roughly 100x compared to fixing vulnerabilities discovered post-deployment, according to NIST data. However, the challenge remains that most CI/CD pipelines still treat security scans as blocking gates rather than continuous feedback loops, creating the very bottleneck that DevSecOps aims to eliminate. Organizations that succeed embed security checks as non-blocking annotations in pull requests, escalating only critical findings to hard gates.

DevOps, Cloud, Security

Apple Dropped iCloud End-to-End Encryption After FBI Pressure

Apple dropped plans to let iPhone users fully encrypt backups of their devices in the company's iCloud service after the FBI complained that the move would harm investigations, six sources familiar with the matter told Reuters. The tech giant's reversal, about two years ago, has not previously been reported. It shows how much Apple has been willing to help U.S. law enforcement and intelligence agencies, despite taking a harder line in high-profile legal disputes with the government and casting itself as a defender of its customers' information.

This Reuters report exposed a critical gap between Apple's public privacy marketing and its actual data protection architecture. While Apple publicly fought the FBI over iPhone device unlocking in the 2016 San Bernardino case, it quietly abandoned end-to-end encryption for iCloud backups -- which contain Messages, Photos, and Health data -- making that data accessible to law enforcement via court orders. The technical implication is significant: iCloud backups served as a side-channel that bypassed the device-level encryption Apple promoted as its privacy differentiator. This pattern of cooperating with government agencies while marketing privacy as a premium feature is not unique to Apple; it reflects a structural tension faced by any company operating under national jurisdiction while serving a global user base. Users who genuinely require end-to-end encrypted cloud storage must evaluate solutions where the encryption keys never leave the client device.

Apple, iPhone, Governance

Edge and Fog Computing: Decentralizing IoT Data Processing

Fog computing refers to a decentralized computing structure where resources, including data and applications, are placed in logical locations between the data source and the cloud. It is also known by the terms "fogging" and "fog networking." The goal is to bring basic analytic services to the network edge, improving performance by positioning computing resources closer to where they are needed, thereby reducing the distance that data must be transported on the network and improving overall network efficiency and performance. Fog computing can also be deployed for security reasons, as it can segment bandwidth traffic and introduce additional firewalls to a network for higher security. [...] This lack of consistent access leads to situations where data is being created at a rate that exceeds how fast the network can move it for analysis. This also raises concerns over the security of the data being created, which is becoming increasingly common as Internet of Things devices become more commonplace.

Fog computing extends edge computing by adding an intermediate processing layer between IoT sensors and cloud data centers, enabling hierarchical data aggregation, filtering, and local decision-making. The architectural significance goes beyond latency reduction: fog nodes can enforce data sovereignty by processing sensitive telemetry within a geographic or regulatory boundary before forwarding only anonymized aggregates to the cloud. From an infrastructure perspective, the concentration of internet services among a handful of hyperscalers (AWS, Azure, GCP) has created an internet that is technically decentralized but organizationally centralized to an unprecedented degree. Edge and fog computing architectures offer independent service providers a path to reclaim both computational and organizational decentralization, particularly in sectors like industrial IoT, autonomous vehicles, and smart grids where sub-10ms response times and offline resilience are non-negotiable requirements.

IoT, Cloud

Block YouTube Ads: Ad-Free Viewing on Desktop and Mobile

On the desktop, the solution is straightforward: install one of many ad blocker extensions for your browser. Some of them also block YouTube ads. I am using AdBlock, which is probably the most popular option and works just fine for me -- it has for years. Mobile phone browsers do not allow installing extensions. And the YouTube phone app comes along with its ads as well. Does this mean there is no way to watch YouTube without ads?

The ad-blocking landscape has evolved considerably since browser extensions first appeared. uBlock Origin remains the gold standard for desktop browsers due to its efficient static filter compilation and low memory footprint compared to AdBlock Plus. On mobile, DNS-level blocking via tools like Pi-hole, NextDNS, or AdGuard DNS provides system-wide ad filtering without requiring browser extension support. The deeper question is sustainability: content creators depend on ad revenue, and the ad-blocker arms race has pushed platforms toward increasingly intrusive formats. The Brave browser's Basic Attention Token (BAT) micro-payment model attempted to solve this by compensating creators directly, though adoption has remained niche. YouTube Premium, at $13.99/month, is Google's own answer -- but it bundles services many users do not need.

Web, Culture, Ads

Escaping Google G Suite Lock-In: A 12-Year Migration Case Study

Google applies aggressive vendor lock-in practices to prevent G Suite subscribers from cancelling their subscriptions. G Suite subscription fees have steadily increased over the past years, now amounting to 10 EUR per user per month, and will increase yet again in the coming months. I was one of the early adopters of G Suite and started using it from the very first day it launched as "Google Apps," 12 years ago. Some years later, Microsoft's Office 365 SaaS offering tried to enter the SaaS era as well. I use Office 365 at work and it was and still is fundamentally broken in every possible way. I do not want to go into details, but if you doubt that Office 365 is broken in every way, try using it with Linux for a week.

Migrating away from G Suite (now Google Workspace) after a decade of dependency exposes the full scope of SaaS vendor lock-in: not just data portability, but identity coupling. Google ties your account identity to the subscription, meaning cancellation threatens your entire Google ecosystem -- Drive files, Gmail history, YouTube purchases, and Android app licenses. The practical migration path involves decoupling email via a low-cost hosting provider (Hetzner, Fastmail, or Migadu at 2-10 EUR/month) combined with custom domain DNS, then replacing productivity tools with self-hosted alternatives like OnlyOffice, Nextcloud, or ownCloud that keep data under your control. Domain registrar choice matters too: commodity registrars like Hetzner, Porkbun, or Cloudflare Registrar offer transparent pricing without the upsell tactics that inflate costs at providers like GoDaddy. The key lesson is that the earlier you decouple identity from subscription, the lower the switching cost.

Google, Office 365

Kubuntu Focus Laptop: A Linux Workstation Without the Windows Tax

The Kubuntu Focus is the result of a collaboration between The Kubuntu Council, Tuxedo Computers, and MindShareManagement. It is a high-powered, workflow-focused laptop which ships with Kubuntu installed. This is the first officially Kubuntu-branded computer. The concept is simple and compelling: We maintain the platform so our customers can focus on work and play. Complex workflows can be achieved "out of the box" without additional software or configuration. Our team has used Linux for development, high performance and high volume clusters, and in consumer products for decades. We use this experience and award-winning industrial design to deliver the Focus to you.

The Kubuntu Focus represents a desktop-replacement workstation with dedicated GPU rather than an ultraportable, but its significance lies in the OEM-validated hardware-software integration. Linux laptops from vendors like System76, Tuxedo, and now Kubuntu Focus address the core pain point of desktop Linux: driver compatibility and power management vary wildly across hardware configurations, particularly with NVIDIA GPUs where the proprietary driver, kernel module signing, and KDE compositor interactions create a matrix of potential failures. Pre-configured machines eliminate this lottery. The broader implication is that Linux desktop adoption depends less on kernel capabilities -- which are excellent -- and more on OEM partnerships that guarantee a cohesive out-of-the-box experience including suspend/resume reliability, correct HiDPI scaling, and thermal management tuned to the specific chassis.

Ubuntu, Linux