In April 1999, a $433 million Air Force rocket inexplicably malfunctioned almost immediately after liftoff, causing the permanent loss of an $800 million military communications satellite. This $1.2 billion disaster remains one of the costliest accidents in human history.


You might wonder if scientists ever found out what caused this misfiring. They sure did! And the answer is a software bug.


This accident alone is a testament to the importance of software testing.


Although you can probably deduce the software testing definition, let’s also review it together.


So, what is software testing?


Software testing refers to running a software program before putting it on the market to determine whether it behaves as expected and displays no defects.


While testing itself isn’t free, these expenses are cost-effective compared to potential money loss resulting from software failure. And this is just one of the benefits of this process. Others include improving performance, preventing human and equipment loss, and increasing stakeholder confidence.


Now that you understand why software testing is such a big deal, let’s inspect this process in more detail.


Software Testing Fundamentals


We’ll start with the basics – what are the fundamentals of testing in software engineering? In other words, what exactly is its end goal, and which principles underlie it?


Regarding the objectives of software testing, there are three distinct ones aiming to answer crucial questions about the software.


  • Verification and validation. Does the software meet all the necessary requirements? And does it satisfy the end customer?
  • Defects and errors identification. Does the software have any defects or errors? What is their scope and impact? And did they cause related issues?
  • Software quality assurance. Is the software performing at optimal levels? Can the software engineering process be further optimized?

As for principles of software testing, there are seven of them, and they go as follows:


  1. Testing shows the presence of defects. With everything we’ve written about software testing, this sounds like a given. But this principle emphasizes that testing can only confirm the presence of defects. It can’t confirm their absence. So, even if no flaws are found, it doesn’t mean the system has none.
  2. Exhaustive testing is impossible. Given how vital software testing is, this process should ideally test all the possible scenarios to confirm the program is defect-free without a shadow of a doubt. Unfortunately, this is impossible to achieve in practice. There’s simply not enough time, money, or space to conduct such testing. Instead, test analysts can only base the testing amount on risk assessment. In other words, they’ll primarily test elements that are most likely to fail.
  3. Testing should start as early as possible. Catching defects in the early stages of software development makes all the difference for the final product. It also saves lots of money in the process. For this reason, software testing should start from the moment its requirements are defined.
  4. Most defects are within a small number of modules. This principle, known as defect clustering, follows the Pareto principle or the 80/20 rule. The rule states that approximately 80% of issues can be found in 20% of modules.
  5. Repetitive software testing is useless. Known as the Pesticide Paradox, this principle warns that conducting the same tests to discover new defects is a losing endeavor. Like insects become resistant to a repeatedly used pesticide mix, the tested software will become “immune” to the same tests.
  6. Testing is context-dependent. The same set of tests can rarely be used on two separate software programs. You’ll need to switch testing techniques, methodologies, and approaches based on the program’s application.
  7. The software program isn’t necessarily usable, even without defects. This principle is known as the absence of errors fallacy. Just because a system is error-free doesn’t mean it meets the customer’s business needs. In software testing objectives, software validation is as important as verification.

Types of Software Testing


There are dozens (if not hundreds) types of testing in software engineering. Of course, not all of these tests apply to all systems. Choosing the suitable types of testing in software testing boils down to your project’s nature and scope.


All of these testing types can be broadly classified into three categories.


Functional Testing


Functional software testing types examine the system to ensure it performs in accordance with the pre-determined functional requirements. We’ll explain each of these types using e-commerce as an example.


  • Unit Testing – Checking whether each software unit (the smallest system component that can be tested) performs as expected. (Does the “Add to Cart” button work?)
  • Integration Testing – Ensuring that all software components interact correctly within the system. (Is the product catalog seamlessly integrated with the shopping cart?)
  • System Testing – Verifying that a system produces the desired output. (Can you complete a purchase?)
  • Acceptance Testing – Ensuring that the entire system meets the end users’ needs. (Is all the information accurate and easy to access?)

Non-Functional Testing


Non-functional types of testing in software engineering deal with the general characteristics of a system beyond its functionality. Let’s go through the most common non-functional tests, continuing the e-commerce analogy.


  • Performance Testing – Evaluating how a system performs under a specific workload. (Can the e-commerce shop handle a massive spike in traffic without crashing?)
  • Usability Testing – Checking the customer’s ability to use the system effectively. (How quickly can you check out?)
  • Security Testing – Identifying the system’s security vulnerabilities. (Will sensitive credit card information be stored securely?)
  • Compatibility Testing – Verifying if the system can run on different platforms and devices. (Can you complete a purchase using your mobile phone?)
  • Localization Testing – Checking the system’s behavior in different locations and regions. (Will time-sensitive discounts take time zones into account?)

Maintenance Testing


Maintenance testing takes place after the system has been produced. It checks whether (or how) the changes made to fix issues or add new features have affected the system.


  • Regression Testing – Checking whether the changes have affected the system’s functionality. (Does the e-commerce shop work seamlessly after integrating a new payment gateway?)
  • Smoke Testing – Verifying the system’s basic functionality before conducting more extensive (and expensive!) tests. (Can the new product be added to the cart?)
  • Sanity Testing – Determining whether the new functionality operates as expected. (Does the new search filter select products adequately?)

Levels of Software Testing


Software testing isn’t done all at once. There are levels to it. Four, to be exact. Each level contains different types of tests, grouped by their position in the software development process.


Read about the four levels of testing in software testing here.


Level 1: Unit Testing


Unit testing helps developers determine whether individual system components (or units) work properly. Since it takes place at the lowest level, this testing sets the tone for the rest of the software development process.


This testing plays a crucial role in test-driven development (TDD). In this methodology, developers perform test cases first and worry about writing the code for software development later.


Level 2: Integration Testing


Integration testing focuses on the software’s inner workings, checking how different units and components interact. After all, you can’t test the system as a whole if it isn’t coherent from the start.


During this phase, testers use two approaches to integration testing: top-down (starting with the highest-level units) and bottom-up (integrating the lowest-level units first).


Level 3: System Testing


After integration testing, the system can now be evaluated as a whole. And that’s exactly what system testing does.


System testing methods are usually classified as white-box or black-box testing. The primary difference is whether the testers are familiar with the system’s internal code structure. In white-box testing, they are.


Level 4: Acceptance Testing


Acceptance testing determines whether the system delivers on its promises. Two groups are usually tasked with acceptance testing: quality assessment experts (alpha testing before the software launches) and a limited number of users (beta testing in a real-time environment).



Software Testing Process


Although some variations might exist, the software testing process typically follows the same pattern.


Step 1: Planning the Test


This step entails developing the following:


  • Test strategy for outlining testing approaches
  • Test plan for detailing testing objectives, priorities, and processes
  • Test estimation for calculating the time and resources needed to complete the testing process

Step 2: Designing the Test


In the design phase, testers create the following:


  • Test scenarios (hypothetical situations used to test the system)
  • Test cases (instructions on how the system should be tested)
  • Test data (set of values used to test the system)

Step 3: Executing the Test


Text execution refers to performing (and monitoring) the planned and designed tests. This phase begins with setting up the test environment and ends with writing detailed reports on the findings.


Step 4: Closing the Test


After completing the testing, testers generate relevant metrics and create a summary report on their efforts. At this point, they have enough information to determine whether the tested software is ready to be released.


High-Quality Testing for High-Quality Software


Think of different types of software testing as individual pieces of a puzzle that come together to form a beautiful picture. Performing software testing hierarchically (from Level 1 to Level 4) ensures no stone is left unturned, and the tested software won’t let anyone down.


With this in mind, it’s easy to conclude that you should only attempt software development projects if you implement effective software testing practices first.

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OPIT’s Peer Career Mentoring Program
OPIT - Open Institute of Technology
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The Open Institute of Technology (OPIT) is the perfect place for those looking to master the core skills and gain the fundamental knowledge they need to enter the exciting and dynamic environment of the tech industry. While OPIT’s various degrees and courses unlock the doors to numerous careers, students may not know exactly which line of work they wish to enter, or how, exactly, to take the next steps.

That’s why, as well as providing exceptional online education in fields like Responsible AI, Computer Science, and Digital Business, OPIT also offers an array of career-related services, like the Peer Career Mentoring Program. Designed to provide the expert advice and support students need, this program helps students and alumni gain inspiration and insight to map out their future careers.

Introducing the OPIT Peer Career Mentoring Program

As the name implies, OPIT’s Peer Career Mentoring Program is about connecting students and alumni with experienced peers to provide insights, guidance, and mentorship and support their next steps on both a personal and professional level.

It provides a highly supportive and empowering space in which current and former learners can receive career-related advice and guidance, harnessing the rich and varied experiences of the OPIT community to accelerate growth and development.

Meet the Mentors

Plenty of experienced, expert mentors have already signed up to play their part in the Peer Career Mentoring Program at OPIT. They include managers, analysts, researchers, and more, all ready and eager to share the benefits of their experience and their unique perspectives on the tech industry, careers in tech, and the educational experience at OPIT.

Examples include:

  • Marco Lorenzi: Having graduated from the MSc in Applied Data Science and AI program at OPIT, Marco has since progressed to a role as a Prompt Engineer at RWS Group and is passionate about supporting younger learners as they take their first steps into the workforce or seek career evolution.
  • Antonio Amendolagine: Antonio graduated from the OPIT MSc in Applied Data Science and AI and currently works as a Product Marketing and CRM Manager with MER MEC SpA, focusing on international B2B businesses. Like other mentors in the program, he enjoys helping students feel more confident about achieving their future aims.
  • Asya Mantovani: Asya took the MSc in Responsible AI program at OPIT before taking the next steps in her career as a Software Engineer with Accenture, one of the largest IT companies in the world, and a trusted partner of the institute. With a firm belief in knowledge-sharing and mutual support, she’s eager to help students progress and succeed.

The Value of the Peer Mentoring Program

The OPIT Peer Career Mentoring Program is an invaluable source of support, inspiration, motivation, and guidance for the many students and graduates of OPIT who feel the need for a helping hand or guiding light to help them find the way or make the right decisions moving forward. It’s a program built around the sharing of wisdom, skills, and insights, designed to empower all who take part.

Every student is different. Some have very clear, fixed, and firm objectives in mind for their futures. Others may have a slightly more vague outline of where they want to go and what they want to do. Others live more in the moment, focusing purely on the here and now, but not thinking too far ahead. All of these different types of people may need guidance and support from time to time, and peer mentoring provides that.

This program is also just one of many ways in which OPIT bridges the gaps between learners around the world, creating a whole community of students and educators, linked together by their shared passions for technology and development. So, even though you may study remotely at OPIT, you never need to feel alone or isolated from your peers.

Additional Career Services Offered by OPIT

The Peer Career Mentoring Program is just one part of the larger array of career services that students enjoy at the Open Institute of Technology.

  • Career Coaching and Support: Students can schedule one-to-one sessions with the institute’s experts to receive insightful feedback, flexibly customized to their exact needs and situation. They can request resume audits, hone their interview skills, and develop action plans for the future, all with the help of experienced, expert coaches.
  • Resource Hub: Maybe you need help differentiating between various career paths, or seeing where your degree might take you. Or you need a bit of assistance in handling the challenges of the job-hunting process. Either way, the OPIT Resource Hub contains the in-depth guides you need to get ahead and gain practical skills to confidently move forward.
  • Career Events: Regularly, OPIT hosts online career event sessions with industry experts and leaders as guest speakers about the topics that most interest today’s tech students and graduates. You can join workshops to sharpen your skills and become a better prospect in the job market, or just listen to the lessons and insights of the pros.
  • Internship Opportunities: There are few better ways to begin your professional journey than an internship at a top-tier company. OPIT unlocks the doors to numerous internship roles with trusted institute partners, as well as additional professional and project opportunities where you can get hands-on work experience at a high level.

In addition to the above, OPIT also teams up with an array of leading organizations around the world, including some of the biggest names, including AWS, Accenture, and Hype. Through this network of trust, OPIT facilitates students’ steps into the world of work.

Start Your Study Journey Today

As well as the Peer Career Mentoring Program, OPIT provides numerous other exciting advantages for those who enroll, including progressive assessments, round-the-clock support, affordable rates, and a team of international professors from top universities with real-world experience in technology. In short, it’s the perfect place to push forward and get the knowledge you need to succeed.

So, if you’re eager to become a tech leader of tomorrow, learn more about OPIT today.

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The Impact of Artificial Intelligence in Our Society
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Oct 24, 2025 6 min read

The world has entered the age of artificial intelligence (AI), and this exciting new technology is already changing the face of society in an ever-growing number of ways. It’s influencing a plethora of industries and sectors, from healthcare and education to finance and urban planning. This guide explores AI’s impact on three of the core pillars of life: business, education, and sustainability.

AI in Business: Unlocking Unprecedented Opportunities

In the world of business, the number of uses of AI is growing by the day. Whether it’s in sales, marketing, customer relations, operational optimization, cybersecurity, data management, or some other aspect of organizational life, there are so many ways this technology can unlock new opportunities or expedite existing processes.

Take data as an example. Many businesses now collect and use large amounts of data to inform their decisions in areas like product development or marketing strategy. But they have, up to now, been limited in how they can structure, visualize, and analyze their data. AI changes all that, as it can dig into vast databases with ease, extracting insights to drive actionable decisions in no time.

AI also bridges gaps in communications. It has the power to speak in most major languages, translating audio or written text with astonishing accuracy in an instant. In a globalized world, where many businesses buy and sell with partners, suppliers, investors, and other stakeholders from other nations, AI can help them communicate and exchange information more easily and reliably.

AI in Education: Democratizing and Accelerating the Learning Process

In the educational sector, AI is solving problems that have plagued this industry for generations and transforming the ways in which students learn and teachers teach. It can be used, for example, to personalize a student’s learning plan or adapt content to align with each learner’s favored learning style, making it easier for them to soak up and retain information and skills.

AI’s generative capabilities are also proving useful in the education sector. Teachers, for example, can turn to generative AI models to create lesson plans or supplementary content to support their courses, such as tables, charts, infographics, and images. This all helps to make the learning experience more diverse, dynamic, and engaging for every kind of learner.

On a broader level, there’s clear potential for AI to democratize education across the globe, making learning more accessible to all. That includes those in developing nations who may normally lack opportunities to gain knowledge and skills to achieve their ambitions. If harnessed correctly and responsibly, this technology could elevate education to whole new heights.

AI in Sustainability: Smarter Cities and Next-Level Efficiency

Sustainability is one of the sticking points when talking about AI, as many critics of the technology point to the fact that it involves huge amounts of energy and relies heavily on large and costly data centers to operate. At the same time, AI could also solve many of the sustainability crises facing the world today, uncovering solutions and innovations that may have previously taken decades to develop.

It’s already proving its value in this domain. For instance, DeepMind developed an AI system that was actually able to optimize data center energy efficiency, cutting the amount of energy used to cool data center hardware by a whopping 40% and improving energy efficiency in certain centers by 15%. That’s just one example, and it’s only the start of what AI could do from an environmental perspective.

This tech is also making cities smarter, more efficient, and more pleasant in which to live through AI-powered navigation aids or traffic redistribution systems. It also holds potential for future urban planning, city development, and infrastructure construction, provided the correct systems and frameworks can be established to make the best use of AI’s advantages.

The Ethical Challenges and Risks of AI

Despite its almost countless advantages and possible applications, AI is not without its flaws. This technology brings challenges and risks to go along with its opportunities, and five leading examples include:

  • Bias: Algorithmic bias is an issue that has already presented itself during the relatively brief existence of AI so far. Some systems, for example, have issued responses or generated content that could be classified as discriminatory or prejudiced, due to the training data they were given.
  • Privacy: There are fears among populations and analysts about the amount of data being fed into AI systems and how such data could be misused, potentially violating people’s rights of privacy and falling foul of data privacy regulations, such as GDPR.
  • Misuse: Like so many game-changing technologies, AI has the potential to be used for both benevolent and malicious purposes. It may be used to spread misinformation and “fake news,” influence public opinion, or even in cyber-attacks, for instance.
  • Over-reliance: AI is so powerful, with the capacity to carry out tasks with remarkable precision and speed, that it will be tempting for organizations to integrate it into many of their workflows and decision-making processes. But AI cannot be treated as a substitute for human judgment.
  • Sustainability: There are also fears about the energy costs associated with AI and the data centers needed to power it, plus the fact that some elements of the burgeoning AI industry may exploit workers in poorer nations worldwide.

Solving These Challenges: Regulation and Responsible Use of AI

With the right approach, it is possible to solve all the above challenges, and more, making AI the most valuable and beneficial new technology the world has seen since the advent of the internet. This will require a two-pronged strategy focusing on both regulation and responsible usage.

Europe is already leading the way in the first aspect. It has introduced the AI Act – a world-first regulatory framework related to artificial intelligence, laying out how it should be used to drive innovation without infringing on the fundamental rights of workers and the larger public.

Educational institutions like the OPIT – Open Institute of Technology are also leading the way in the second aspect, educating people around the world on how to work with AI in a responsible, ethical way, through programs like the MSc in Responsible Artificial Intelligence.

By establishing rules and regulations about AI’s usage and educating the tech leaders of tomorrow in how to work with AI in a fair and responsible way, the future is bright for this exciting and extraordinary new technology.

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