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?

How Can a Business Strategy Be Ruined Through the Lack of Strategy?

The combined onslaught of disruptive technologies, changing user preferences, a greater competition and security considerations have forced businesses to reimagine and reinvent themselves. To achieve the same, companies are increasingly into innovation and development of products that reach the market faster. The aim is to pre-empt competition and enjoy the early mover advantages. The approach forms a part of the digital transformation initiatives that businesses have adopted with a greater frequency.

The process of digital transformation ought to be smooth without disrupting the existing ecosystem. For otherwise, the customer service gets impacted leading to dissatisfaction and the company risks losing its brand value. Bringing about digital transformation in an organization is a difficult proposition. This is mainly to do with the presence of legacy systems (read unwieldy, non-scalable, complex and costly), and human resources that need to be upskilled for new technology platforms and paradigms.

Furthermore, the growing expectations of customers about 24 x 7 connectivity and device agnostic products and services have necessitated the adoption of new technologies and business models. This has brought about challenges for enterprises as many dimensions of technology are not in their control. These dimensions include network connectivity, availability of an adequate bandwidth, and the security of cloud platforms among others. These challenges coupled with the business imperatives of reaching the market faster have given rise to the need for quality assurance. In fact, there is a growing realization among business stakeholders that quality assurance and testing cannot be ignored any longer. This is due to the fact that software quality assurance testing plays a greater role in achieving business outcomes.

The greater emphasis on software quality assurance testing has led to adoption of innovative practices, tools and platforms. The industry has seen a paradigm shift in testing approaches – from the early manual based waterfall model to the latest Agile, TCoE, and DevOps models with emphasis on test automation. Importantly, notwithstanding the role of QA software testing in improving the product quality and meeting business outcomes, business strategy is still about beating the margins. Let us find out the consequences of neglecting qualityassurance and testing in terms of ruining a business strategy.

Consequences of not following Quality Assurance

Losing competitiveness: One of the objectives in framing a business strategy is to remain competitive. And to achieve the same, the products and services of a company should either be on par or better than its competitors. This is where the role of software quality assurance becomes critical. It validates the quality of a product on parameters like performance, functionality, load carrying capacity, usability and security. However, in the absence of testing, the inherent glitches can let the product falter on any of the above mentioned parameters. As a result, the user experience can go for a toss leading customers to opt for competing products and the company losing its competitive edge.

Not reaching the market in time: The changing market dynamics and evolving technologies mean businesses need to remain ever vigilant. They are required to bring out innovative products and reach customers before their competitors. However, without using the right test automation tools, the timeframe of testing applications gets extended. This can delay activities like the delivery of innovative solutions, adding functionalities/updates or conducting regression testing.

Security ramifications: Neglecting the quality testing of software applications can leave glitches to go unnoticed and unmitigated. The same can be exploited by hackers with the use of Trojans, viruses, spyware and malware to achieve the following:

• Steal sensitive personal and business information

• Disrupt the systems and networks

The above activities can lead a business to lose customer’s trust, attract penalties and censure, or lawsuits from customers and regulatory agencies. These have the potential to ruin a business completely.

Impacting the product quality: Customers are increasingly using software applications to carry out a plethora of activities. These can range from paying for utility services and booking of train/plane/movie tickets to buying from eCommerce stores among others. Moreover, in the age of the Internet of Things (IoT,) embedded technologies are used extensively in medicine and other mission critical processes. The lack of quality of such software can lead to the malfunctioning of sensitive equipments. This can have perilous consequences such as loss of life and property.

Conclusion

The unpredictable business environment shaped by increased competition and the advent of new technologies has meant that companies have to deliver quality products within tight turnaround times. This can be ensured by following rigorous testing of software and streamlining the processes. Neglecting the same can have perilous consequences as mentioned above.

This Article is originally published at Medium.com, How can neglecting Quality Assurance and Testing ruin a business strategy?