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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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Source:
- The Yuan, Published on October 25th, 2024.
By Zorina Alliata
ALEXANDRIA, VIRGINIA – In recent years, artificial intelligence (AI) has grown and developed into something much bigger than most people could have ever expected. Jokes about robots living among humans no longer seem so harmless, and the average person began to develop a new awareness of AI and all its uses. Unfortunately, however – as is often a human tendency – people became hyper-fixated on the negative aspects of AI, often forgetting about all the good it can do. One should therefore take a step back and remember that humanity is still only in the very early stages of developing real intelligence outside of the human brain, and so at this point AI is almost like a small child that humans are raising.
AI is still developing, growing, and adapting, and like any new tech it has its drawbacks. At one point, people had fears and doubts about electricity, calculators, and mobile phones – but now these have become ubiquitous aspects of everyday life, and it is not difficult to imagine a future in which this is the case for AI as well.
The development of AI certainly comes with relevant and real concerns that must be addressed – such as its controversial role in education, the potential job losses it might lead to, and its bias and inaccuracies. For every fear, however, there is also a ray of hope, and that is largely thanks to people and their ingenuity.
Looking at education, many educators around the world are worried about recent developments in AI. The frequently discussed ChatGPT – which is now on its fourth version – is a major red flag for many, causing concerns around plagiarism and creating fears that it will lead to the end of writing as people know it. This is one of the main factors that has increased the pessimistic reporting about AI that one so often sees in the media.
However, when one actually considers ChatGPT in its current state, it is safe to say that these fears are probably overblown. Can ChatGPT really replace the human mind, which is capable of so much that AI cannot replicate? As for educators, instead of assuming that all their students will want to cheat, they should instead consider the options for taking advantage of new tech to enhance the learning experience. Most people now know the tell-tale signs for identifying something that ChatGPT has written. Excessive use of numbered lists, repetitive language and poor comparison skills are just three ways to tell if a piece of writing is legitimate or if a bot is behind it. This author personally encourages the use of AI in the classes I teach. This is because it is better for students to understand what AI can do and how to use it as a tool in their learning instead of avoiding and fearing it, or being discouraged from using it no matter the circumstances.
Educators should therefore reframe the idea of ChatGPT in their minds, have open discussions with students about its uses, and help them understand that it is actually just another tool to help them learn more efficiently – and not a replacement for their own thoughts and words. Such frank discussions help students develop their critical thinking skills and start understanding their own influence on ChatGPT and other AI-powered tools.
By developing one’s understanding of AI’s actual capabilities, one can begin to understand its uses in everyday life. Some would have people believe that this means countless jobs will inevitably become obsolete, but that is not entirely true. Even if AI does replace some jobs, it will still need industry experts to guide it, meaning that entirely new jobs are being created at the same time as some older jobs are disappearing.
Adapting to AI is a new challenge for most industries, and it is certainly daunting at times. The reality, however, is that AI is not here to steal people’s jobs. If anything, it will change the nature of some jobs and may even improve them by making human workers more efficient and productive. If AI is to be a truly useful tool, it will still need humans. One should remember that humans working alongside AI and using it as a tool is key, because in most cases AI cannot do the job of a person by itself.
Is AI biased?
Why should one view AI as a tool and not a replacement? The main reason is because AI itself is still learning, and AI-powered tools such as ChatGPT do not understand bias. As a result, whenever ChatGPT is asked a question it will pull information from anywhere, and so it can easily repeat old biases. AI is learning from previous data, much of which is biased or out of date. Data about home ownership and mortgages, e.g., are often biased because non-white people in the United States could not get a mortgage until after the 1960s. The effect on data due to this lending discrimination is only now being fully understood.
AI is certainly biased at times, but that stems from human bias. Again, this just reinforces the need for humans to be in control of AI. AI is like a young child in that it is still absorbing what is happening around it. People must therefore not fear it, but instead guide it in the right direction.
For AI to be used as a tool, it must be treated as such. If one wanted to build a house, one would not expect one’s tools to be able to do the job alone – and AI must be viewed through a similar lens. By acknowledging this aspect of AI and taking control of humans’ role in its development, the world would be better placed to reap the benefits and quash the fears associated with AI. One should therefore not assume that all the doom and gloom one reads about AI is exactly as it seems. Instead, people should try experimenting with it and learning from it, and maybe soon they will realize that it was the best thing that could have happened to humanity.
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Source:
- The European Business Review, Published on October 27th, 2024.
By Lokesh Vij
Lokesh Vij is a Professor of BSc in Modern Computer Science & MSc in Applied Data Science & AI at Open Institute of Technology. With over 20 years of experience in cloud computing infrastructure, cybersecurity and cloud development, Professor Vij is an expert in all things related to data and modern computer science.
In today’s rapidly evolving technological landscape, the fields of blockchain and cloud computing are transforming industries, from finance to healthcare, and creating new opportunities for innovation. Integrating these technologies into education is not merely a trend but a necessity to equip students with the skills they need to thrive in the future workforce. Though both technologies are independently powerful, their potential for innovation and disruption is amplified when combined. This article explores the pressing questions surrounding the inclusion of blockchain and cloud computing in education, providing a comprehensive overview of their significance, benefits, and challenges.
The Technological Edge and Future Outlook
Cloud computing has revolutionized how businesses and individuals’ access and manage data and applications. Benefits like scalability, cost efficiency (including eliminating capital expenditure – CapEx), rapid innovation, and experimentation enable businesses to develop and deploy new applications and services quickly without the constraints of traditional on-premises infrastructure – thanks to managed services where cloud providers manage the operating system, runtime, and middleware, allowing businesses to focus on development and innovation. According to Statista, the cloud computing market is projected to reach a significant size of Euro 250 billion or even higher by 2028 (from Euro 110 billion in 2024), with a substantial Compound Annual Growth Rate (CAGR) of 22.78%. The widespread adoption of cloud computing by businesses of all sizes, coupled with the increasing demand for cloud-based services and applications, fuels the need for cloud computing professionals.
Blockchain, a distributed ledger technology, has paved the way by providing a secure, transparent, and tamper-proof way to record transactions (highly resistant to hacking and fraud). In 2021, European blockchain startups raised $1.5 billion in funding, indicating strong interest and growth potential. Reports suggest the European blockchain market could reach $39 billion by 2026, with a significant CAGR of over 47%. This growth is fueled by increasing adoption in sectors like finance, supply chain, and healthcare.
Addressing the Skills Gap
Reports from the World Economic Forum indicate that 85 million jobs may be displaced by a shift in the division of labor between humans and machines by 2025. However, 97 million new roles may emerge that are more adapted to the new division of labor between humans, machines, and algorithms, many of which will require proficiency in cloud computing and blockchain.
Furthermore, the World Economic Forum predicts that by 2027, 10% of the global GDP will be tokenized and stored on the blockchain. This massive shift means a surge in demand for blockchain professionals across various industries. Consider the implications of 10% of the global GDP being on the blockchain: it translates to a massive need for people who can build, secure, and manage these systems. We’re talking about potentially millions of jobs worldwide.
The European Blockchain Services Infrastructure (EBSI), an EU initiative, aims to deploy cross-border blockchain services across Europe, focusing on areas like digital identity, trusted data sharing, and diploma management. The EU’s MiCA (Crypto-Asset Regulation) regulation, expected to be fully implemented by 2025, will provide a clear legal framework for crypto-assets, fostering innovation and investment in the blockchain space. The projected growth and supportive regulatory environment point to a rising demand for blockchain professionals in Europe. Developing skills related to EBSI and its applications could be highly advantageous, given its potential impact on public sector blockchain adoption. Understanding the MiCA regulation will be crucial for blockchain roles related to crypto-assets and decentralized finance (DeFi).
Furthermore, European businesses are rapidly adopting digital technologies, with cloud computing as a core component of this transformation. GDPR (Data Protection Regulations) and other data protection laws push businesses to adopt secure and compliant cloud solutions. Many European countries invest heavily in cloud infrastructure and promote cloud adoption across various sectors. Artificial intelligence and machine learning will be deeply integrated into cloud platforms, enabling smarter automation, advanced analytics, and more efficient operations. This allows developers to focus on building applications without managing servers, leading to faster development cycles and increased scalability. Processing data closer to the source (like on devices or local servers) will become crucial for applications requiring real-time responses, such as IoT and autonomous vehicles.
The projected growth indicates a strong and continuous demand for blockchain and cloud professionals in Europe and worldwide. As we stand at the “crossroads of infinity,” there is a significant skill shortage, which will likely increase with the rapid adoption of these technologies. A 2023 study by SoftwareOne found that 95% of businesses globally face a cloud skills gap. Specific skills in high demand include cloud security, cloud-native development, and expertise in leading cloud platforms like AWS, Azure, and Google Cloud. The European Commission’s Digital Economy and Society Index (DESI) highlights a need for improved digital skills in areas like blockchain to support the EU’s digital transformation goals. A 2023 report by CasperLabs found that 90% of businesses in the US, UK, and China adopt blockchain, but knowledge gaps and interoperability challenges persist.
The Role of Educational Institutions
This surge in demand necessitates a corresponding increase in qualified individuals who can design, implement, and manage cloud-based and blockchain solutions. Educational institutions have a critical role to play in bridging this widening skills gap and ensuring a pipeline of talent ready to meet the demands of this burgeoning industry.
To effectively prepare the next generation of cloud computing and blockchain experts, educational institutions need to adopt a multi-pronged approach. This includes enhancing curricula with specialized programs, integrating cloud and blockchain concepts into existing courses, and providing hands-on experience with leading technology platforms.
Furthermore, investing in faculty development to ensure they possess up-to-date knowledge and expertise is crucial. Collaboration with industry partners through internships, co-teach programs, joint research projects, and mentorship programs can provide students with invaluable real-world experience and insights.
Beyond formal education, fostering a culture of lifelong learning is essential. Offering continuing education courses, boot camps, and online resources enables professionals to upskill or reskill and stay abreast of the latest advancements in cloud computing. Actively promoting awareness of career paths and opportunities in this field and facilitating connections with potential employers can empower students to thrive in the dynamic and evolving landscape of cloud computing and blockchain technologies.
By taking these steps, educational institutions can effectively prepare the young generation to fill the skills gap and thrive in the rapidly evolving world of cloud computing and blockchain.
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