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 to implement Test Automation in an Agile set-up?

The digital landscape has witnessed frequent changes in the way software products are developed, devices introduced, technologies used, costs optimized, processes streamlined, and customer expectations are fulfilled. One of the changes is related to the way a software product is developed. As opposed to the traditional method of SDLC where development and testing take place one after the other, Agile has gone a step further. In an Agile testing strategy, development and testing are done concurrently in short sprints.

                 

The advent of Agile methodology in the development of software has brought about significant changes. The changes are primarily about improving the quality of software and hastening its delivery schedule. The thrust of Agile is towards improving as well as shortening the sprint iteration leading to better outcomes. In the Agile testing environment, the imperative of testing a product feature or functionality post its development in the quickest possible way has underlined the importance of test automation.

Test automation services bring the following benefits

• Glitches or bugs can significantly reduce the quality of software by impairing its functionality, usability, and security. If the bugs are detected at a later stage, the cost and time of removing them can be significantly high. However, Agile testing can identify and eliminate glitches in the initial phases of the SDLC. This apart from improving the quality and timeliness of software delivery, can enhance the ROI as well.

• Easy and quick adaption to changes put forward by the client. Since the changes are done in quick iterations but in an incremental way, they can be easily incorporated. In the changing market dynamics, such a development process is the most suited one as it squarely addresses the client and market requirement(s) as and when needed.

• Enhances the test coverage area, which otherwise gets severely curtailed during manual testing. Moreover, automated tests can be conducted without manual intervention at any time of the day (or night.)

• Optimizes the testing of resources by engaging a small number of test automation experts. This is not possible in manual testing for conducting a large number of tests needs a greater number of testers.

• Easy to develop the beta version and receive customer feedback. Based on the feedback, the software can be suitably customized to meet the client or market requirement.

How to effectively implement test automation in an Agile set up?

Selecting the right automation framework: Not all tests are to be automated, for writing scripts for them can be a time consuming exercise. The test automation experts should earmark specific tests for automation and select an appropriate framework. The criteria for choosing a framework should be based on its scalability, clear understanding and easy adaptability. There can be a number of automation frameworks as given below:

Data driven:  This framework is used when specific functionalities of a software application are tested for different sets of data. In this, the data are stored in external databases such as Excel or XML files.

Keyword driven: This framework allows the testing of data based on certain keywords. The keywords present in the database perform specific functions on the application, which are tested for their effectiveness.

Modular based: Here, the test code is broken into independent modules that are connected logically to each other. The framework considers each module as a separate test case, which upon completion, ensures the entire software functions according to the stated objectives.

Hybrid: As the name suggests, this framework leverages the benefits of one or more frameworks as mentioned above.

Setting the sprint target: The target for each and every sprint should be identified and fixed. The target should not be influenced by the level of complexity of the effort involved. However, notwithstanding the difficulties one can face while working on the sprint, the end result leads to a better testing outcome.

 

Team feedback: Timely feedback from the team can help test experts to identify features or modules that can be automated successfully. It is a good practice to involve each and every member of the testing team in deciding the features to automate. The expert should discuss the approach taken for automation with other team members. Continuous feedback can help members to suggest new features and approaches to be adopted for automation.

Conclusion

The above mentioned points can pave the way for an effective implementation of automation in the testing of software. These can help the team to achieve test objectives that are made part of an Agile set up.

This Article is originally published at Medium.com, Effectively implementing Test Automation in the Agile set-up.

How The Next-Gen Testing Needs Are Defined By Digital Assurance

The digital transformation initiatives have placed the customer at their core. This customer centricity has led to strategies to find out what drives a customer to select and use specific products. As customers become aware of new age technologies, they demand products that incorporate aspects of such technologies but at reasonable prices. These technologies can have interfaces to the social media, mobility, analytics, cloud, Internet of Things, big data, AI and machine learning, and virtual reality among others.

In an increasingly interconnected world, the news of a trendy product spreads fast. In their quest to get hold of such a product, customers do not think twice about abandoning the legacy products. Such shifting preferences (read loyalties) of customers have put enormous strain on businesses as they struggle to keep pace with trending technologies and a dynamic demand curve.

This is where digital assurance incorporating predictive analysis comes to the help of enterprises. The predictive analysis uses databases reflecting past and present customer behaviour patterns. It does so to derive future trends and other business critical information by leveraging statistical algorithm and machine learning. Such an analysis helps enterprises to predict failures and take pro business decisions. The new age analytics helps businesses to foretell the market need and deliver quality products ahead of others.

Digital assurance is all about ensuring the quality of a product right from its design stage and extended to its entire usage lifecycle. It is primarily aimed at providing a seamless customer experience. To explain it further, if a company builds a powerful mobile phone, digital assurance ensures its quality right across the usage lifestyle. This means digital QA checks the product during the development phase and beyond to validate the latter’s performance, navigability, security, and usability. In doing so, it uses test automation as the technique to usher in a faster time to market besides covering a larger test area.

Before understanding how digital quality assurance can define the next generation testing needs, it is better to understand the latter. The next gen testing needs go beyond the traditional functional and regression testing. It rides the Agile and DevOps paradigms to infuse quality right into the design and development stages of the SDLC and beyond. Digital quality assurance studies the market, takes customer feedback and develops products that are high on user experience.

If the next gen testing needs are about speed, quality, compatibility, security, and scalability, digital assurance solutions can address them squarely. Let us find out how.

Speed: By employing Agile and DevOps methodologies, digital QA identifies and eliminates glitches early in the SDLC. This prevents costly and time consuming rework leading to the speedier delivery of products. Speed can offer the early mover advantage in a competitive market. It can be the differentiator between a market leader and an also ran one.

Quality: An enhanced customer experience can only be obtained if the product functions seamlessly across devices, platforms, networks, browsers, and operating systems. It is the chief enabler for business transformation and ensures customers stick to the product. A quality product always stands tall amidst competition. The leading brands in any domain are a testimony to this axiom.

Compatibility: Digital testing ensures the seamless functioning of a product across a complex application landscape. It provides a high customer experience as the product functions on different platforms and devices without any hitch. The test automation framework used in digital testing is the chief enabler for compatibility. This is because a test automation framework provides the right tools, APIs, and language compatibility to write robust reusable scripts.

Security: In the IoT defined landscape, the lack of security of an application can play havoc. Not only can it lead to the breach of sensitive customer and business information, but also lead to disastrous consequences for all stakeholders. The need for 100% trustworthy applications with multiple connectivities can be met by leveraging digital assurance solutions. Needless to say, when customers are assured of the security of an application, the trust level grows manifold.

Scalable: A product should provide more or less similar customer experience in real time irrespective of the user load. The product, be it backed by cloud or legacy systems, should meet user traffic at all times.

Conclusion

The Agile and DevOps led quality assurance goes beyond the traditional regressive testing. It has the right wherewithal to address the next gen testing needs predicated on cutting edge technologies, business models, and customer demand.

This Article is originally published at Medium.com, How is Digital Assurance Defining the Next-Gen Testing Needs?

Diya works for Cigniti Technologies, which is the world’s first Independent Software Testing Services Company to be appraised at CMMI-SVC v1.3, Maturity Level 5, and is also ISO 9001:2015 & ISO 27001:2013 certified.

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?