How businesses can achieve ultimate success with a QA culture

Competition forces businesses or technicians to come up with things (read innovations) that they would not do otherwise. In the rat race where businesses are delivering products or services at the drop of a hat, not everything is lapped up by the end customers. At the end of the day, it is quality that plays an all-important role in making an enterprise successful. This is due to the fact that customers of the day are choosy, smart, knowledgeable, and won’t settle for anything less.

In fact, they choose products that meet the highest standards of security, usability, functionality, and performance, among other parameters. However, there can be issues galore when it comes to ensuring the quality of a product or service. To begin with, each product should work seamlessly across devices, operating systems, browsers, frameworks, and networks. This is easier said than done as ensuring that would mean subjecting the product to a rigorous quality assurance exercise.

Why software quality assurance?

Today, good customer experience has become a differentiator for the success of a product or service in the market. This can only come about when end-customers evaluate and accept that product or service based on various quality parameters. However, meeting such quality parameters in the SDLC consistently would require the product to be tested across devices, operating environments, and networks.

In the event of such parameters not meeting the desired standards, the consequences can be immense, both for the customers and businesses. The presence of bugs in a finished product can mar the quality of service. For example, it can allow vulnerabilities to creep in and let hackers steal sensitive personal or business information. Today, when software applications carry sensitive financial and personal information, the presence of glitches can render them vulnerable.

Since the traditional waterfall model has proved to be ineffective in measuring up to the quality standards of today’s products, methodologies like Agile and DevOps have come into play. If earlier, QA testing services used to follow development and integration, today these have become concurrent with them. The focus is on executing QA software testing alongside development to save cost and time. To make DevOps successful, businesses need to develop a QA culture where everyone is an equal stakeholder.

How to enable a robust QA culture?

Building a robust culture of quality assurance in the organization is not easy as it requires establishing a seamless coordination between silo-based departments and processes. The best way to go about the same is discussed below.

Engaging everyone in the process: For start-ups and small businesses, meeting the quality standards with their products need greater involvement of all the stakeholders. In fact, everyone involved with software development (and testing) viz., developers, managers, business analysts, and testers should be a part of the QA process. Except for developers, people can evaluate the functionality of a product and give feedback. The same can then be worked upon by developers to fix glitches, thereby offering a positive experience to the users. This shall make the QA process more efficient and help deliver quality products & services.

Follow Agile methodology: The process shall lead to better communication and collaboration between departments. Also, test management can implement better test automation tools to identify and fix bugs quickly and effectively. The use of QA automation tools can eliminate running of repetitive test cases. Thus, the QA team can focus its time in executing exploratory testing.

How a QA culture can help?

An all-encompassing QA culture can help enterprises to achieve success. They can do so in the following ways.

Better customer experience: A glitch-free application is a result of executing quality assurance and testing thoroughly. The final validation of features and functionalities against expected outcomes allows the application to work seamlessly across environments and customers to enjoy the best experience.

Faster time to market: When quality assurance and testing takes place alongside development following the Agile and DevOps methodologies, glitches are identified and remedied fast in the SDLC. As the workflow gets streamlined, the delivery of the product becomes faster.

Better security: The rising incidences of cybercrime have brought into sharp focus the importance of strengthening the security features of software applications. This can only happen when total quality culture pervades across the organization with every stakeholder being aware of upholding the security protocols, regulations, and standards. This can help to reduce security vulnerabilities and prevent applications from being hacked.

Conclusion

The changing market dynamics and the advent of new technologies have brought the aspect of ‘quality’ into sharp focus. It has not remained the preserve of a single department but a shared responsibility of all concerned. If only a total quality culture prevails in an organization, achieving success can only be a matter of time.

Why does Software Quality Assurance is essential in healthcare?

The healthcare industry has evolved over the years to include the latest technologies for accurate diagnosis, prognosis, treatment, and rehabilitation. In modern hospitals, highly sophisticated machines are used with the latest embedded software to diagnose and treat patients. It is important that the software should be quality assured to ensure the results generated by the sophisticated equipment are 100% accurate. For should there be any inaccuracies in the readings, the entire treatment protocol can go haywire and the patients’ life itself could be in danger. This makes QA software testing such a critical component in the manufacturing and maintenance of medical apps and devices.

With increased digitization, the healthcare industry is increasingly using sensor-embedded mobile devices to check the health stats of individuals remotely. While this is a great initiative, a lot depends on how the mobile app or any other accessory works to give the most accurate results about the health stats of an individual. To make this happen, rigorous healthcare application testing is necessary. Moreover, as we move more towards an Internet-based health delivery system, there is a greater need to conduct healthcare QA testing and healthcare app security testing to ensure complete data security as well as accuracy of the readings.

Also, certain factors are needed to be considered when you are looking to offer a hundred percent software qualityassurance for medical devices. Some of these are as follows.

1). Interoperability of various health devices: To get highly accurate diagnostic results, doctors and paramedics depend on a number of sophisticated devices such as MRI, CT Scan, EEG, etc. These should possess greater interoperability to be able to provide a comprehensive healthcare report of individual patients. Interoperability thorough healthcaresoftware testing ensures multiple devices using different platforms to easily access each other’s data and fulfill the required security compliance related to data privacy. 

2). How good the application is: To deliver quality outcomes for the healthcare app, its attributes like functionality, performance, security, and usability, among others, ought to be tested meticulously. Comprehensive testing of an app is a highly specialized job that requires a huge amount of domain knowledge and experience. This is where the role of a reputed software quality assurance company with years of experience in testing software and applications comes into play.  

3). Security of the healthcare application: When businesses strategize about developing healthcare related apps for people on-the-go, the issue of cybersecurity in healthcare comes into focus. As very sensitive personal data related to patients are shared through the online platform, it becomes imperative to make the entire ecosystem hack-proof. To do this in a systematic way, penetration testing becomes essential. It ensures hidden glitches and vulnerabilities are identified and the app is made compliant with safety protocols like HIPAA. 

4). Making a sense out of compiled data: A huge amount of data gets generated through various sensors, diagnostic tools, as well as, healthcare applications. To make some sense out of this mountain of data, these should be compiled and presented to the healthcare experts in an easy-to-understand way. This allows doctors to make a quick and accurate assessment of the patient’s health condition and decide the line of treatment. Here, big data analysis can play a critical role. It analyses the data generated through various diagnostic tools to deliver the right inference for doctors to make correct diagnosis.

5). Cutting down on complexities: In any modern hospital, the entire healthcare system is highly centralized where the data related to patients can be accessed by authorized persons for various medical as well as administrative needs. These may include nurses looking to deliver the right medicinal dosage to the patient, the pharmacist looking for the right generic combination of medicine available in his or her stock, or the administrative staff preparing the bill. Here, high quality software assurance can provide a seamless interaction between the stakeholders and the healthcare system to offer a holistic experience.

Conclusion

The above-mentioned reasons highlight the criticality of comprehensive software assurance of medical devices for delivering high-quality healthcare services to the patients. Remember, any flaw in the software system can generate erroneous output, which may lead the healthcare expect to conduct wrong diagnosis and treatment. Thus, it is imperative that there should be no loose threads in the strategy for testing embedded software in diagnostic tools and healthcare applications to ensure their accuracy and seamless functioning.

Source: https://it.toolbox.com/blogs/hemanthkumaryamjala/the-life-critical-need-to-employ-software-quality-assurance-in-healthcare-111219

Why AI is so important in optimizing Software Quality Assurance

Digital Transformation is making a huge business impact. This is mainly driven by the advent of new technologies, development/adoption of new products or business models, and growing customer expectations. The increasingly competitive nature of online business has led enterprises to aim at garnering more customers to realize the ROI.

As the growing competition has resulted in new product launches, the need to validate their quality through software quality assurance has become important. Faced with such constraints including that of time and cost, enterprises look to leverage Artificial Intelligence and machine learning to understand the needs of customers based on their past behavioural patterns.

Moreover, the spread of Internet of Things (IoT) and its growing scope have increased the need for testing the IoT processes, embedded devices and the software within them. For in the absence of any software quality assurance vis-a-vis IoT, the devices would falter in their functioning leading to unpredictable outcomes. However, the use of traditional methods for testing embedded devices can be time consuming leading to a bad user experience. This is where AI and machine learning can help in predicting the user traffic, delivering real time insights and bring about a drastic improvement in user experience.

As enterprises deal with big data, they look to leverage the same to deliver productive outcomes. The use of data mining and predictive analytics a la AI can help businesses to optimize their processes, predict customer behaviour and create better products. One of the foremost challenges of Agile and DevOps based test automation is identifying processes for software qualityassurance testing. This is due to the fact that not all processes can be automated owing to process complexity, lack of time and resources to write the script, and cost. AI and machine learning can help in identifying processes to be automated based on repetitive patterns of data. The use of AI and machine learning can help improve the test efficiencies and enable better decision making.

AI can optimize software quality assurance in the following ways:-

Analysing the defects: One of the main objectives of Agile and DevOps based testing is identifying and eliminating glitches early in the design and development phase. AI can help in identifying the most critical glitches as opposed to the lesser ones. Thus, the QA software testing team can prioritize regression testing leading to quicker turnaround times.

Analysing Customer behavioural patterns: The testers can leverage the use of monitors or embedded sensors to generate behavioural patterns. These patterns throw light on the demographics, geographies, and devices of users. The same can be used by testers to build better test suites for achieving a greater test efficiency.

Use of social media: Effective test suits can be built by analyzing suitable data patterns from social media usage of customers. This is in terms of identifying the demographic trends.

Enhancing QA testing: AI can help in optimizing test cases, prioritizing testing, and reducing the task of analyzing complex data patterns. Moreover, by mining the test management data from dashboard, the productivity of testers can be ascertained. The productivity is related to the creation and execution of test cases. The knowledge can be used to bring efficiency in the testing process leading to faster test outcomes. 

Non functional analysis: AI can help in generating performance reports, identifying security vulnerabilities and SLA misses based on the operational dashboard. The information can be used to plug security vulnerabilities, reduce performance issues and meet SLA targets.

Better feedback: QA testers can get a better and quicker feedback through AI. The analytics element in AI can run test cases quickly by mimicking the input test scenarios. These scenarios can be very tedious given their repetitive and complex nature. AI can generate a suitable output from such scenarios based on past data patterns.

Predictive analytics: Test automation leads to the generation of vast amounts of data (read big data.) The big data can form the basis of identifying and forecasting the quality of processes and products. As a consequence of identifying the test outcomes or inherent glitches in the system, AI can guide the tester by prescribing a better course of action.

Conclusion

As the demand for quality products with quick turnaround times increases, AI and machine learning can be of help with their data based analytics. AI helps in optimizing QA software testing a great deal and leads to the development of better products.  

This Article is originally published at Medium.com, How can AI optimize Software Quality Assurance?