The Continuing Relevance of Manual Testing

 

Automation is most advantageous for regular and repeated testing, which may be very time-consuming and tiresome when done manually. However, automation cannot match a human’s intuitiveness, broad knowledge, and iterative assessment skills.

Additionally, some testing has to be done manually, and both the setup of automated testing need human involvement.

So today, we are going to talk about why manual testing remains a must for software development.

Importance of Manual Testing

Manual testing is of continuing relevance for software developments, some of which are enumerated below:

1. Usability and User Experience Testing

Usability/UX testing is the process by which testers determine if a website/software performs as anticipated by the designer when used by a real-world user. An observer watches invited user test participants while they complete a set of pre-designed activities.

The tester keeps track of how well they do and encounter any difficulties with the assigned activities. Additionally, they may collect valuable data, such as if the route to a destination has more steps than the user desires and whether navigational signposts are easily identified and successfully chosen.

However, technologies such as eye-tracking software that measures visual attention across a page may be beneficial. In contrast to the test experts who conduct the sessions, it is recommended that no one associated with the build does usability/UX testing. This is because the most valuable test data comes from users with no previous knowledge of the product, who are more likely to identify unanticipated problems and are not influenced by unconscious bias.

2. Issues Occur in the Most Inconspicuous Places

The success of manual testing may be measured by how a human being is involved in a project. Simultaneously, automation’s most significant contribution is a robotic adherence to specified sequences that would be tedious and time-consuming for testers. It is precisely this consistently non-thinking feature of automated testing, which makes it entirely inappropriate for use in ad hoc or exploratory testing.

Testers are urged to use their curiosity and initiative to pursue spontaneous lines of inquiry and investigate areas that are not routinely examined. When an issue does arise, human testers can rapidly alter their lines of inquiry.

This is not feasible in automated testing since the script would have to be paused to rewrite sections before testing could continue. Occasionally, common sense is needed, such as when something seems to be correct during the authoring process but requires modification throughout the coding process.

Humans have an innate sense of when something is wrong, highlighting another reason for the continuing necessity of manual testing.

3. Appreciating the Bigger Picture

Nothing beats a tester’s ability to synthesize a comprehensive overview of a product or project simply by drawing on their training, on-the-job expertise, and life experience. No automated system can duplicate this kind of manual testing, which does not need test plans and relies on a gut sense that something is wrong. Further, structured testing will be conducted to isolate the issue before notifying the developers, but the initial impetus comes from the tester’s unique and unquantifiable skills.

4. Automated Tests Might Include Errors and Gaps

Regardless of the benefits of automated testing, automated scripts can only test what they were designed to test. This means that there is always a chance that the person writing the script will overlook an important detail. Without human intervention at this stage, the likelihood is that the potential problem remains untested.

Because omission errors are common, scheduling a manual testing sweep as part of the setup procedure is critical before testing can be approved and then begun. Even then, inaccuracies in a script may not be discovered until testing is well underway.

When automated testing is running, it cannot deviate from the script, which means any errors or holes in the writing will eventually result in an inaccurate test report. Such circumstances necessitate exploratory testing performed exclusively by humans.

5. Device & Browser Testing

Cross-browser testing is critical to the overall testing process for bug identification. A cross-browser testing tool is used to repeatedly run the same script on a potentially infinite number of browser, device, and version combinations.

However, manual testing is the ideal choice for testing the more physical, visual, and tactile aspects since human testers possess the characteristics that enable them to take the initiative and explore utilizing their senses of sight and touch. Occasionally, a choice requiring compromise will have to be taken. Because a site may not appear flawless on every browser, a judgment decision about what is acceptable must be made.

Manual Testing Variations

In most cases, automated testing will not stray from the test script’s route, which may be good or bad depending on the circumstances. The way a person physically tests anything, even if they adhere to test cases, may differ slightly because of human impulses and the fact that they may have to process various inputs, and so on.

This human ability to be non-robotic may sometimes result in mistakes, but if any human-caused testing problems arise, manual testing is responsible for rectifying them.

1. Troubleshooting Connection Issues

As there is the availability of diverse options in terms of technology, manual testing becomes the best testing method.

All connectable devices make use of web browsers, which sometimes experience connection speed dips and connectivity problems. When dropouts occur, a site or application must already have a fallback mechanism to prevent the system from total failure. Due to the variety of fallback plans’ kinds and applications, manual testing is the only viable alternative.

2. End-User Support

End-user support goes beyond lab testing. It also includes the stage of product finishing and delivery. This stage in the market and is a closely connected and essential element of testing. Unfortunately, consumers may find stray bugs if their purchase fails somehow, detracting from their user experience. Because the client is satisfied

When is manual testing more cost-effective?

When it comes to low regression or low testing cases, manual testing can be highly cost effective. This is especially true for cases where some defect is visible to the user, or in cases where tests have to be run only one or twice.

Consider this- there is a learning module that contains several chapters. However, the logo of the application is different in some of the chapters. Manual testing can spot such issues easily, and hence, the issue can be resolved fast and leading to better ROI.

For even small bugs or issues like differences in name, id or class of a button- the script for automation testing has to undergo significant changes, which drives up costs. In case of manual testing, no change is required which helps keep costs under control.

Manual testing is also preferred by small companies who have limited IT staff and budget. It also does not require any programming knowledge- which is a must for automation testing. Last but not the least- you can go for random testing when doing it manually. The same cannot be done with automation testing.

And finally, when it comes to human experience of the product- pre-scripted automation tests may not be the best gauge. Observations from manual testing can be applied to other applications that aim to be user-friendly.

Conclusion

It is now clear that manual testing is how value judgments are made about technical errors. At the same time, automated testing can free up the tester from the repetitively boring aspects of software testing.

Like other businesses, if you too are looking for testing solutions, Mindfire Solutions can be your partner of choice. We have deep expertise in manual testing. We have a team of highly skilled and certified software professionals that has developed many custom solutions for our global clients over the years.

 

UPI (Unified Payments Interface) – The Game Changer

 

Phenomenal growth may be the exact term to describe the increased volume of digital payments in India in 2022. If you inspect the March data of the current year, it shows that this payment ecosystem grew by 216% compared to the same period in 2019. The Unified Payments Interface (UPI), an initiative of the National Payments Corporation of India (NPCI) has been a critical driver of cashless transactions since its launch in 2016. The idea behind introducing UPI was to enable fast, secure, and seamless digital payments with the Immediate Payment Service (IMPS) infrastructure.

The immense popularity of this system is evident from the fact that UPI transactions exceeded USD 110 billion in January 2022.

NPCI has launched several innovative payment systems for developing the digital payment space and tied up with foreign companies like Japan Credit Bureau, China Union Pay, Discover Financial Services, etc. The international coordination would provide support by payment acknowledgment through RuPay cards.

Starting with IMPS and NFS in 2010, there have been RuPay cards, NACH, Aadhaar Payments Bridge System (APBS), Aadhaar-enabled Payments System (AePS), NETC, BBPS, UPI, and the USSD based *99#.

What makes UPI the game changer in digital payments?

Convenience

For using debit or credit cards online, you need to enter several details related to the card holder’s name, card number, CVV, and expiry dates. For UPI, you just key in the UPI ID, login, and complete the payment after putting in the PIN.

If you are using net banking, you must first add the beneficiary to your bank account. You require the beneficiary’s name, bank account number, branch address, and IFSC code. The process takes time to get activated, and then you can make the payment. To make a UPI payment, you can transfer funds to a peer account even if they do not have UPI access.

Besides, there are advantages over other existing payment methods. For instance, UPI allows you to make payments directly from your bank account without any third-party wallet or a card. Thus, making it much more convenient and secure than using a debit or credit card for online payments.

Moreover, UPI transactions happen in real-time. There is no waiting period for the funds to be credited or debited from the user’s account. This feature makes UPI an ideal choice for digital payments.

Simple

UPI is also very simple to use. You just need a smartphone with an active internet connection. You can then download a UPI-enabled app from your bank’s website or the Google Play Store. After installing the app, simply link your bank account and start making payments.

Versatile

You can also use UPI to request money from others or to send money to them. All in all, UPI is a very convenient and secure way to make digital payments. It is fast and easy to use.

UPI is particularly beneficial as a non-cash payment instrument when it involves person-to-person (P2P) fund transfers and transactions related to small-value person-to-merchant (P2M) payments.

Interoperable

With interoperability technology between different payment systems and UPI, and more people using digital payments, the costs of transactions are expected to decrease further.

India’s most prominent technology firms like TCS, Infosys, Wipro, etc., and fintech companies in various fields like insurance, payment, agriculture, microloans, crowdfunding, and wealth management are some of the major factors behind the growth of UPI. The role of an open Application Programming Interface (API) is also worth mentioning. 

How does UPI’s rise affect the Indian economy?

All the above factors have pegged the volume of UPI transactions at approximately 9 times that of debit and credit card transactions in FY22. But this is just the initial phase. UPI is forecasted to grow manifold and comprise around 73% of the total volume of digital transactions by the financial year 2026.

UPI transactions beyond borders

The tie-up between NIPL (NPCI International Payments Limited) and others like Liquid Group – Singapore, Mashreq Bank – UAE, Lyra Network – France, and PayXpert – UK, are significant steps toward enabling QR-based UPI payments in countries across the globe. Nepal and Bhutan became the first countries to adopt UPI.

RBI’s latest move entails linking RuPay credit cards with UPI. The decision to implement such a change has come at the perfect hour since credit card transactions are rising. A Goldman Sachs report states that the volume of transactions was around $130 Bn in FY22. The forecast is that it may reach $285 Bn by FY26 while the UPI volume growth in August 2022 was more than Rs. 6500 Mn with 346 banks going live on the platform.

An important point is the MDR (merchant discount rate) on transactions by RuPay credit cards. Like other credit cards, the rate will likely be around 2%, as specified by the National Payments Corporation of India (NPCI).

What can be the impact of this MDR on BFSI?

Payment companies like PayU, Razorpay, Billdesk, etc., would be able to acquire more transactions as credit cards would be an additional payment instrument on the UPI platform.

The proposed MDR of 2% (1.5% for the issuing bank and 0.5% for the payment company) on credit cards for payments through the Unified Payments Interface (UPI), would generate a revenue stream for the payment companies.

Currently, the MDR on UPI merchant transactions is zero, which is a deterrent for the payment companies as there is no direct income available for them on UPI transactions. This MDR would help the payment companies to manage and maintain the infrastructure for UPI volume growth.

For smaller merchants, providing a subsidy for MDR will mean the payment companies would lose their revenue percentage by an amount equivalent to the subsidy amount. In that scenario, the Government can incentivize the payment companies to maintain their payment infrastructure (technology, etc.) and operating costs.

Conclusion

With the RBI’s decision to allow credit cards for payments through UPI, the central bank is enabling access to digital payments for those who do not have a debit card or a bank account. It would help to expand the reach of digital payments to a wide demographic besides bringing in higher revenue for the payment companies. If you are looking for a game changer in the digital payments space, UPI it is.

If you want to develop a mobile payment solution application that offers a top-notch user experience and data protection, team up with Mindfire Solutions.

Mindfire Solutions is a trusted software service provider that can assist you in developing robust and secure fintech products. With our two decades of experience, we have learned to leverage modern technologies to offer customized solutions for your business needs. To know more about us, visit Mindfire Solutions.

 

Utilizing Machine Learning In Banking To Prevent Fraud

 

 

Machine Learning (ML) is a vital tool for fraud detection in banks. It can spot potential fraud by examining patterns in transactions and comparing them with known fraudulent activity. It uses algorithms to identify these patterns, which are then used to predict whether or not a transaction is fraudulent. These algorithms are trained using historical data, so they can only identify patterns in existing data and cannot learn new ways as they occur. 

This means that companies must constantly update their machine learning models with further information for continuing to use machine learning in Banking to prevent fraud.

How Does Machine Learning Overcome The Traditional Security Techniques Used By Banks?

Machine learning pushes the boundaries of what can be done with security. A traditional security strategy is to make the system as difficult to access as possible, stopping the bad guys before they get in. Banks often use biometrics and key cards to access their accounts, which are more challenging to hack than a username/password combination. 

But machine learning in banking prevents fraud even when it’s not done by someone trying to access an account. It can also be used to flag suspicious behavior so that humans can investigate it and decide whether or not it’s worth taking action on.

Machine learning algorithms can analyze data from all sources—customer transactions, social media posts, etc.—and find patterns that indicate fraudulent activity or other risks. These algorithms are trained on examples of fraud so that they know what to look for when new transactions occur.

What Are The Benefits of Machine Learning In Fraud Detection?

Machine learning has been the buzzword in the tech industry for some time. From self-driving cars to automated customer engagement, machine learning is everywhere.

But what does it mean? Let’s look at some of the benefits of using machine learning in Banking to prevent fraud.

·         Speed

Machine learning can help improve the speed of fraud detection by reducing the time it takes to detect and flag suspicious activity. Machine learning algorithms can be trained to automatically flag transactions with a high risk of fraud. This can significantly improve your ability to identify fraudulent transactions quickly so you can act on them before they become too costly to remediate.

·         Efficiency 

Machine learning also improves efficiency by automating many manual tasks that waste time and effort. For example, machine learning in banking to prevent fraud can identify known bad actors who are likely to commit fraud in the future, so you can block their access to your business immediately without having to review every transaction they make manually. 

·         Scalability 

Machine learning allows you to scale up or down your fraud detection capabilities as needed. This is important because fraud patterns change over time as criminals adapt their approach or new types of fraud emerge. Machine learning algorithms are designed with built-in flexibility to adapt quickly when new threats emerge or old threats change tactics. 

·         Accuracy 

Finally, machine learning offers increased accuracy over traditional methods because it uses data from all available sources—including humans—to learn what normal behavior looks like and spot anomalies that indicate potential problems.

What Are Some Of The Ways Machine Learning Can Be Used To Detect And Block Fraud?

There are many different techniques to detect and block suspicious cases. Some of them include the following – 

·         Classification

Classification assigns a label to an observation based on a set of observed values used as predictors. The predictors are inserted into the algorithms, which use training data to learn what labels to give. These predictions can then be used for fraud detection. This is done by identifying fraudulent transactions or users by classifying them as fraudulent or not fraudulent.

·         Regression

Regression is a supervised learning method that predicts future outcomes based on historical data. The regression algorithms can be used in fraud detection to predict the likelihood that a transaction will be fraudulent based on historical data about previous transactions that were labeled as fraudulent or not fraudulent by humans.

·         Clustering And Anomaly Detection

Clustering and anomaly detection are unsupervised learning methods that can be used for fraud detection by identifying patterns within your data that suggest fraud may occur, such as many small withdrawals from an account or many large purchases made at one store over time.

·         Anomaly Detection

Machine learning algorithms search for patterns in existing data that are not typical of what you would expect. If a new transaction is entered into your system and doesn’t fit the pattern of existing transactions, it could be an anomaly.

·         Decision Trees

A decision tree is a tree-like diagram that shows all possible paths that can take place in a decision process. A decision tree algorithm takes in data and tests each piece of information against all possible outcomes to determine if they’re true or false. If any single piece of information leads to an inaccurate result, the entire transaction is flagged as fraudulent.

·         Neural Networks

Neural networks are used to detect fraud in several ways. They can be trained to recognize patterns that indicate fraudulent transactions, such as repeated requests for withdrawals from an ATM or many purchases at one store within a short period. 

Neural networks can also monitor customer behavior over time and flag suspicious activities like sudden changes in spending habits or changes in the type of purchases being made (from low-risk items like groceries to high-risk items like jewelry).

·         Natural Language Processing (NLP)

NLP refers to technologies that use machine learning algorithms to analyze text data and extract meaningful information. 

For example, NLP software might analyze customer statements and detect instances where someone has been using their bank account number on multiple credit card applications without having applied for those cards themselves. This could indicate that they have been victims of identity theft or another fraud scheme.

Summing It Up

If you’re looking to implement machine learning in banking to prevent fraud or other systems, Mindfire Solutions has got you covered. Our goal is to take the guesswork out of it and ensure you get the most out of your investment.

We have the experience and expertise to help you implement machine-learning algorithms for your security and other needs. Our team deeply understands this technology’s potential, and we can work with you to determine the best way to use it in your organization. Contact us today to see how we can help!

 

The critical role of QA in Medical Device Testing

 

Introduction:

Digitization has brought significant advancements to the healthcare industry in recent years. This has made it easier to connect people with the most up-to-date medical gadgets and software, resulting in better healthcare services. Medical devices are essential for patient care because they have a direct impact on human lives by delivering valuable data and observations.

Due to different advancements in healthcare, medical device software continues to get increasingly sophisticated. Certain critical compliance factors, such as regulatory delays and the adoption of international standards, have an impact on the software quality of medical devices.

Given the current demand for medical devices to serve the millions of patients caught up in the COVID fight, ensuring adequate quality and delivering products quickly has become a must. We need our MedTech producers to be adaptable and robust in the face of rising demand for medical equipment such as ventilators, oximeters, and other devices in such unusual times.

What is Medical Device Testing?

Medical device testing, for those unfamiliar, is the process of establishing that a gadget will be reliable and safe to use in the event of a medical emergency. This is one of the most important processes in the process of turning an inventive concept into a reliable and marketable product.

Multiple regulatory organizations oversee these devices, as well as the overall MedTech business, and they must meet a set of compliance and standardization before moving forward. Hence, this is where professional medical device testing services come into play. These massive software testing firms devise effective device testing methodologies and ensure that your devices are well integrated with your mobile apps, thoroughly tested, and fully functional prior to release. Furthermore, they must provide effective performance, accurate readings, and be safe to use.

Medical device testing guarantees that devices meet all quality-related aspects of the product, which necessitates severe Quality Assurance assistance to ensure that these devices are of the highest quality and reliability.

Types of Medical device testing

When it comes to a software-enabled medical equipment, there are several different forms of testing:

1. Lifecycle Verification &Validation:

Software verification provides objective proof that the design outputs of a specific phase of the software development life cycle meet all of the phase’s requirements. Furthermore, it checks the programme and its supporting documentation for consistency, completeness, and correctness as it is being produced. It also offers evidence for the judgement that the software has been validated.

2. GUI testing:

GUI testing ensures that the medical gadget responds to UI-based inputs correctly. Issues like UI complexity creates user confusion, usage delay, data entry mistakes, false inputs. These can be avoided with GUI testing for medical devices so that they do not become a reason for a life-threatening situation during real-world application.

3. Performance testing or Bench testing:

This type of testing rules out any mechanical or design problems in the medical device’s performance. Hence, medical equipment are subjected to performance bench testing to determine their endurance and capacity to work at the same level of efficiency under various loads.

4. Compliance testing:

Different medical authorities have established distinct federal and regulatory criteria to ensure that a medical gadget in circulation is safe and effective. Thus, compliance testing ensures that the medical device meets all of the quality and safety requirements by using the best design and manufacturing techniques available.

5. Interoperability testing:

Medical device interoperability is the capacity to share and use information between one or more devices, products, technologies, or systems in a safe, secure, and effective manner. Thus, interoperability testing assesses a medical device’s capacity to communicate data across systems and platforms. It thus, improves patient care, reduces errors and adverse events.

6. Reliability testing:

A medical device’s reliability is determined by its ability to work satisfactorily over a long timeframe and under stress. It basically checks the medical device’s dependability in any situation.

The Important Role of Quality Assurance in Medical Device Testing

The PDCA cycle, a four-step procedure, helps in control and continual improvement of processes and products. It has 4 basic steps: Plan, Do, Check and Act.

• PLAN: Make a change plan ahead of time. Analyze and forecast the outcomes.
• DO: carry out the strategy, one step at a time, in a controlled environment.
• CHECK: examine the outcomes.
• ACT: Take steps to assure process uniformity and improvement.

Streamlined Quality Outcomes With The Best QA Solutions

If you’re a medical device manufacturer, the most critical decision is to make sure your company follow the quality assurance and quality control rules. Thus, it leads to cost inefficiencies, if the final product fails to deliver the quality you expect. It’s critical to either have a full-fledged in-house software team or outsource QA to avoid any major fatalities.

The following are some benefits you may gain if you hire a QA consultant for a medical device testing service

• Reduced project budget
• Faster time-to-market
• Continuous support & maintenance
• Efficient domain expertise

Final Thoughts:

Medical devices are  important since they have a direct impact on a patient’s heath. End-to-end medical device software testing helps to ensure optimal device performance.

Any performance difficulties with these devices are deadly to the patient’s health. Businesses should use effective software quality assurance to assure completely functional medical device software.

Like other businesses, if you too are looking for medical device testing Mindfire Solutions can be your partner of choice. We have a team of highly skilled and certified software professionals, who have developed many custom solutions for our global clients over the years.