Interfaces in TypeScript define the shape of objects, ensuring that objects conform to a specific structure and reducing errors in your React Native app. 🔍

Integrating TypeScript types and enums in your React Native navigation component ensures type safety and clarity throughout the application. 🛡️

Using TypeScript Types in Screen Components 📝

By incorporating TypeScript types in screen components, you ensure that data flowing through your React Native application adheres to the defined structure, reducing bugs and enhancing code maintainability.

Incorporating TypeScript's strong typing features, such as enums and interfaces, into your React Native navigation project significantly enhances code clarity, maintainability, and reliability. TypeScript not only catches potential errors at compile-time but also improves code documentation and developer productivity. Embrace TypeScript in your React Native projects to build robust and scalable mobile applications with confidence.

Implement these strategies in your own React Native navigation project to experience firsthand the benefits of type checking with TypeScript. Happy coding! 🎉

Type checking with TypeScript

Type Checking with TypeScript in React Native Navigation

📱 In the realm of mobile app development with React Native, ensuring code reliability and maintainability is paramount. TypeScript, a statically typed superset of JavaScript, proves invaluable in achieving these goals by providing robust type checking capabilities. Let’s explore how TypeScript can be effectively employed in a React Native navigation project, focusing on the following files within our navigation folder: 

: 🏠 This file serves as the main entry point or component for our navigation system in React Native.

: 📜 Contains TypeScript enums defining constants related to navigation, ensuring clarity and consistency in navigation types.

: 🖋️ Houses TypeScript interfaces defining the structure of navigation-related objects, enhancing code documentation and maintainability.

Implementing TypeScript in Your React Native Navigation Project  🚀

Let's delve into how TypeScript can be integrated across these files to leverage its powerful type checking capabilities effectively.

Enums in TypeScript are ideal for defining a set of named constants, providing descriptive names to numeric values and improving code readability.

Software Testing

Software Testing: Principles and Stages Simplified

In the world of software development, testing is not just a phase but a fundamental activity that ensures the success of a project. Effective software testing helps to identify and rectify issues, ensuring that the final product meets user expectations and performs as intended. This guide explores the essential aspects of software testing, including its principles, the distinction between Quality Assurance (QA) and Quality Control (QC), various testing stages, requirements attributes, and the bug life cycle.

Understanding the core principles of software testing can significantly enhance the effectiveness of your testing efforts:

 Testing should begin as early as possible in the development lifecycle. Early testing helps in identifying defects at a stage where they are less costly to fix. This principle underscores the importance of integrating testing activities into the development process from the start.

 This principle suggests that a small number of modules often contain the majority of the defects. Therefore, focusing your testing efforts on these high-risk areas can be more efficient and effective. Understanding where defects are likely to cluster helps in prioritizing testing activities.

 Running the same set of tests will not keep finding new defects. This principle emphasizes the need for regularly updating and diversifying test cases to uncover new issues and adapt to changes in the software.

 Testing can demonstrate the presence of defects but cannot guarantee that all defects have been found. It is essential to understand that testing aims to find defects rather than ensuring a defect-free product.

 Even if no defects are found, it does not mean the software is ready for release. The software may still not meet user needs or business requirements. Testing should focus on ensuring that the software meets the intended purpose and user expectations.

2. Quality Assurance (QA) vs. Quality Control (QC) 

To ensure the quality of software, it’s crucial to understand the distinction between Quality Assurance (QA) and Quality Control (QC):

 QA is a proactive process aimed at improving and establishing processes to prevent defects. It involves setting up quality standards, process improvements, and ensuring that best practices are followed throughout the development lifecycle. QA focuses on creating an environment and processes that support the creation of quality software.

 QC, on the other hand, is a reactive process that involves identifying defects in the software. It is concerned with testing the actual product to find and fix defects. QC focuses on the detection of defects after the product has been developed, ensuring that the final product meets quality standards.

Software testing is a multi-stage process, each stage focusing on different aspects of the application:

 Unit testing involves testing individual components or modules of the software to ensure they work as intended. This stage is crucial for validating the functionality of specific pieces of code and is typically performed by developers.

 This stage tests the interaction between different modules or components. The goal is to ensure that integrated components work together correctly and that data is passed accurately between them.

 System testing involves testing the complete and integrated software system to verify that it meets specified requirements. This stage focuses on the overall behavior of the system and its compliance with user requirements.

 Acceptance testing is conducted by end-users to validate that the system meets their needs and requirements. It is the final phase of testing before the software is released to production.

 Regression testing ensures that new changes or enhancements to the software do not adversely affect existing functionality. It involves re-running previous test cases to verify that previously fixed issues remain resolved.

Effective software testing relies on well-defined requirements. Key attributes of good requirements include:

 Requirements should accurately reflect the needs and expectations of users.

 Requirements must be clear, unambiguous, and easily understandable.

 All necessary requirements should be included to ensure the software meets its intended purpose.

 Requirements should be free from conflicts and inconsistencies.

 Requirements should be realistic and achievable within the project constraints.

 Each requirement should be traceable to corresponding test cases to ensure coverage and validation.

The bug life cycle outlines the various stages a defect goes through from identification to resolution:

 The defect has been identified but not yet reviewed by the team.

 The defect is assigned to a developer or team member for fixing.

 The defect has been fixed and is awaiting verification.

 The fix has been verified by the testing team.

 The defect is resolved and no further action is required.

 If the defect persists or reappears, it is reopened for further investigation and resolution.

Software testing is a vital aspect of the software development process that helps ensure the delivery of high-quality products. By understanding and applying core testing principles, distinguishing between QA and QC, navigating through various testing stages, managing requirements effectively, and overseeing the bug life cycle, teams can significantly enhance the quality and reliability of their software. Effective testing not only helps in identifying and fixing defects but also ensures that the software meets user expectations and business objectives.

Core Web Vitals: The Technical (Dark) Side of SEO

Practical tips for developers improving SEO

Last year, I worked on a website where my main goal was to improve its SEO. The challenge? There was no SEO expert on the team. That meant I had to learn everything myself—reading, experimenting, and breaking things along the way.

After a year of trial and error, I realized that optimizing SEO isn't just about metadata and keywords. 

Here’s what I wish I knew when I started.

 as three key performance metrics that directly impact user experience and search rankings:

 – Measures visual stability. A low CLS means fewer unexpected shifts in page content.

 – Measures loading performance. The time it takes for the largest visible element to load.

 – Measures interactivity. The time it takes for the page to respond to the first user action. 

(Note: FID is being replaced by Interaction to Next Paint (INP) in 2024.)

Lessons Learned: Tips for Developers Improving SEO

 give a single snapshot of performance. The problem? They test on one device, one connection, one specific moment. 

field data (Chrome UX Report, PageSpeed Web Vitals API)

2. Performance Optimizations Can Break SEO

lazy loading images, deferring scripts, or reducing DOM complexity

 might break SEO-critical features (like structured data).

Always test SEO impact after making performance changes.

3. Improving One Metric Often Improves Others

 means a better user experience, reducing bounce rates.

 often improves multiple metrics at once.

SEO is a long game. Google needs time to 

 performance changes. Some improvements can take 

5. There’s No Universal Fix—Always Measure Impact

There’s no "one-size-fits-all" solution.

Make a change → Measure impact → Adjust

6. Mobile Performance is More Important Than Desktop

Cumulative Layout Shift (CLS) isn’t just about page load—it also happens 

 but shift when new elements appear late.

—because that’s what Google actually uses for ranking.

SEO isn't just about keywords and backlinks—

. If you’re working on improving SEO, start with 

, measure real-world impact, and be patient.

Google Search Central: Core Web Vitals & SEO

Computer science has a racism problem: these researchers want to fix it

Black and Hispanic people face huge hurdles at technology companies and in computer-science education in the United States, with far-reaching consequences for science and all of society.

Juan Gilbert felt alone as he pursued a PhD in computer science in the 1990s. It was a familiar sense that had followed him throughout his university years, as a student from modest means and the first of his family to pursue higher education.

But there was something else that weighed on him during his doctoral training at the Ohio State University in Columbus, one of the largest academic institutions in the United States. Despite the size of the university, there were no people who looked like him in the computer-science graduate programme or among the department’s faculty members.

“I didn’t see another Black person in computer science until more than a decade after high school,” says Gilbert.

He contemplated leaving the field entirely until a Black professor at another university encouraged him to find another programme instead.


Gilbert transferred to the University of Cincinnati in Ohio, where he built a community of other Black PhD students. But his ‘a-ha’ moment came when he joined the computer-science faculty at Auburn University in Alabama, where there was one other Black faculty member and two Black PhD students in the department. It clarified something for him he feels should have been obvious: all across the country, there were Black PhDs just like him who were struggling with isolation just like him, enduring microaggressions just like him and fighting the urge to quit, just like him.

With that realization, he pledged to help create a more supportive and inclusive computer-science environment for Black students, and has continued that work as chair of the Department of Computer & Information Science & Engineering at the University of Florida in Gainesville. He thinks that vision is why the university ranks top in terms of the number of computer science PhDs awarded to Black students, and why it boasts the largest proportion of Black computer-science faculty members at any predominantly white institution in the United States.

There are diversity gaps in computer science and in science, technology, engineering and mathematics (STEM) subjects in many countries. The problem is particularly acute in the United States, which spends much more than any other country on research and development and is home to many of the largest technology companies. Despite being a global leader in computer science, the United States has long struggled to increase diversity in this area.

Computer occupations make up one of the fastest-growing employment sectors in the United States, and the US Bureau of Labor Statistics projects that the number of jobs in this area will increase three times faster than the average — and faster than many other fields in STEM. But Black, Latino and Indigenous people remain under-represented in computing jobs. Black and Hispanic people make up almost 13% and 18% of the US workforce, but they hold only 7% and 8%, respectively, of the jobs in computing. (The US government considers ‘Hispanic or Latino’ an ethnicity and that people of Hispanic or Latino origin may be of any race.)

The diversity gap is growing at universities. For bachelor’s degrees, the primary degree granted by US universities, the proportion of computer-science degrees going to Black students has dropped from more than 11% in 2013 to less than 9% in 2020 (see ‘Computing losses’).

That’s a problem, says Gilbert, because computer science is now so fundamental to everything.

“Computing is in health, transportation, education, finance, you name it — computing is there,” he says. “And recent reports have found that, when you have a lack of diversity, computing implementations can have a bias that disproportionately affects certain [marginalized] people in a very dramatic way.”

That belief also drives Shaundra Daily, an electrical and computing engineer at Duke University in Durham, North Carolina. “When you leave people out of the thought, design, development and policy — and all the pieces that are important to make sure that we have equitable technologies — you end up with technology either not helping or effectively harming minoritized populations,” she says.

Algorithms are perhaps the clearest example. They are supposed to make predictions or complex decisions about everyday life without the taint of human emotion or prejudice, say computer-science specialists.

“All data science is political,” says James Mickens, a computer scientist at Harvard University in Cambridge, Massachusetts. “A lot of engineers and non-engineers think that math, science and engineering are somehow detached from values or, because we’re dealing with numbers, the decisions are morally neutral. That’s just not true.”

Based on who builds algorithms, how they’re developed and how they’re used, they can and sometimes do replicate the very biases that they were designed to overcome, says Mickens.

Beyond the issue of algorithmic bias, businesses are finding financial reasons for increasing diversity along gender, racial and ethnic lines. “The most diverse companies are now more likely than ever to outperform non-diverse companies on profitability”, according to a 2020 report by the global management-consulting firm McKinsey and Company

. Diverse teams are also more productive and creative, and bring different perspectives, says Mickens. Famously, non-diverse teams gave us seat belts that are not designed for pregnant people; facial recognition software that doesn’t recognize or misidentifies people of colour; and autonomous vehicles that cannot recognize darker-skinned pedestrians.

In the past few years, however, commitments and pledges from the tech industry on diversity have amounted to very little, according to a 2022 report

 from the non-profit Kapor Center and the NAACP, a civil-rights organization headquartered in Baltimore, Maryland. The current share of Black talent in technical roles has increased just 0.6% since 2018, it notes.

Freeman Hrabowski, former president of the University of Maryland, Baltimore County (UMBC) in Baltimore, gained an international reputation for success in preparing students from under-represented backgrounds to enter STEM fields. Of all US universities, UMBC awarded the most undergraduate degrees to Black students who went on to earn doctorates in this area.

In 2011, Hrabowski chaired a panel for the National Academies of Sciences, Engineering, and Medicine, and at that time only 2.2% of the PhDs in the natural sciences and engineering went to Black people. Ten years later, he says, that number is 2.3%. “With all the initiatives, we still are not at 3%. We have failed to move the needle,” says Hrabowski.

These much-vaunted initiatives have also failed to diversify the computing and technology work sector. Hrabowski spoke to about 2,000 employees this year at a major tech company, where the few employees of colour were doing the kinds of menial job that their parents would have done 50 years earlier. “For the most part, they were on the bottom rung. They were not professionals. I kept it real when I spoke to them and repeated what we all know now, which is that there are far too few people of colour in those companies.”

Hrabowski, Daily and Gilbert are among the many educators and computer scientists working to change the trajectory at universities and tech companies for Black people and other under-represented minority groups. They are realistic about the challenges but think that change is crucial — and possible.

Hrabowski says structural racism in academia hinders the nation’s ability to deal with some of its biggest challenges. To remedy or even mediate tech’s diversity problem, he says, academics and stakeholders must first acknowledge and then confront bias in all aspects, from university admissions to recruitment, hiring and promotion.

When it comes to succeeding in computer science, the tech talent pipeline starts early — and so do the disparities in access and outcomes. Many students from under-represented groups face structural and social barriers in both exposure and access to computer science from the beginning of their educational voyage (see ‘Missing students’). The Kapor–NAACP study reports that almost one-quarter of Black students lack access to computers or reliable high-speed Internet at home, and that just 75% of Black students go to schools that offer foundational computer-science courses. These kinds of courses, says Mickens, are essential to improving the number who graduate with STEM degrees.

Dwinner

Author

Core Web Vitals: The Technical (Dark) Side of SEO

What You’ll Learn...

Dwinner

Author

Core Web Vitals: The Technical (Dark) Side of SEO

What You’ll Learn...

Melba Newsome

Author

Computer science has a racism problem: these resea

Black and Hispanic people face huge hurdles at technology companies and in computer-science education in the United Stat...

Core Web Vitals: The Technical (Dark) Side of SEO

5 March, 2025

29 mins

Type Checking with TypeScript in React Native Navi...

15 July, 2024

5 mins

Software Testing: Principles and Stages Simplified...

5 March, 2025

8 mins

Core Web Vitals: The Technical (Dark) Side of SEO

5 March, 2025

29 mins

Core Web Vitals: The Technical (Dark) Side of SEO

5 March, 2025

29 mins

Category

Computer Science

Dwinner

Author

Core Web Vitals: The Technical (Dark) Side of SEO

What You’ll Learn...

Dwinner

Author

Core Web Vitals: The Technical (Dark) Side of SEO

What You’ll Learn...

Melba Newsome

Author

Computer science has a racism problem: these resea

Black and Hispanic people face huge hurdles at technology companies and in computer-science education in the United Stat...

Core Web Vitals: The Technical (Dark) Side of SEO

5 March, 2025

29 mins

Type Checking with TypeScript in React Native Navi...

15 July, 2024

5 mins

Software Testing: Principles and Stages Simplified...

5 March, 2025

8 mins

Core Web Vitals: The Technical (Dark) Side of SEO

5 March, 2025

29 mins

Core Web Vitals: The Technical (Dark) Side of SEO

5 March, 2025

29 mins