#software testing methodology

The digital world is increasingly getting cluttered with scores of companies slugging it out to remain relevant and competitive. Software is the glue that binds the whole IT infrastructure in a company and across the global online landscape. Moreover, the success of any business in the online sphere depends on the quality and performance of its products and their eventual adoption by the end customers. It has been observed that businesses, in order to reach the market quickly, often give software testing a miss.

The predominant thinking among companies looking at cost optimization and maximizing profits is that QA is an adjunct that can be done without. They feel all software applications are supposed to work seamlessly and there is no need to spend time, effort, and money into ensuring so. However, in today’s world of competitive populism and achieving brand equity, this type of thought process is a recipe for disaster. Remember, building a brand brick by brick can wither away in no time should a software application delivers a bad user experience. This can eventually impact the brand value of a business as well.

Today, when cybercrime has raised its ugly head leading businesses and individuals to grapple with data theft and loss of reputation, software testing should be pursued with all vigor. It will ultimately ensure the software delivered to the end customer remains glitch-free and delivers the best user experience. In a competitive business environment where every business wants to grab the elusive customer pie, the quality of software application will ultimately decide who wins the race. The writing on the wall for businesses is - perform or perish, where perform entails conducting software testing across the SDLC.

What is software application testing?

It involves executing a series of steps to identify glitches that are inherent in the software. The software testing methodology may differ based on specific business objectives - manual or automated. However, it is geared towards checking the performance, security, compatibility, usability, reliability, and integration of the software application.

In the Software Development Life Cycle, each unit of the software code needs to be tested to deliver the expected outcomes. If not, the same is sent back to the development team for rectification. In the Agile driven software testing methodology, the testing goes on alongside development in an integrated module called the sprint. Post rectification, the QA team again conducts the tests until the unit performs to its optimum. The various software testing approach comprises unit testing, functional testing, security testing, regression testing, stress, and load testing, usability testing, system testing, integration testing, and acceptance testing.

What are the software testing challenges?

There are challenges galore when it comes to testing software with multiple features/functionalities and having connections to other software systems. Some of the challenges are -

·         There is no single foolproof method to test the entirety of a software application given the plethora of device platforms available. The QA team needs to employ various test combinations and scenarios depending on the target demography and business goals.

·         Setting up a uniform testing mechanism in an organization having various processes, teams, and branches that are spread across locations and time zones can be a challenge.

·         Lack of proper communication between the development and QA teams can hinder the testing efforts. For example, changes in the app features should be communicated to the QA team to ensure timely bug identification and removal.

·         An effective software testing approach should ensure the application works across devices, operating platforms, browsers, and networks. This can prove to be challenging for companies following manual testing or using an inappropriate tool for automated testing.

What are the benefits of software testing?

Businesses need to include QA process in their SDLC to derive a slew of benefits.

·         A rigorous testing of software applications will eliminate glitches and enhance the quality of software. Such software will stand a greater chance of finding acceptance by the customers. This will help the business to consolidate its brand equity in the market.

·         QA testing ensures the software’s adherence to industry quality standards and helps to avoid the risks or pitfalls of non-compliance.

·         Any modern software testing leads to business optimization with outcomes such as better-quality products, satisfied customers, enhanced brand image, reduced cost of operation or customer service, and increased ROI.

Conclusion

In a highly competitive business environment where perform or perish is the mantra, businesses need to adopt QA testing in their workflows to remain profitable.

The unprecedented run of technology and digitization has brought enormous benefits for everyone. Software development has become more sophisticated and is largely guided by market dynamics. With digitization ushering in a new age of consumerism and offering outcomes such as ease of access, speed and agility, convenience, and scalability, the process of software development has changed. The traditional SDLC has given way to Agile and DevOps with software application testing becoming an integral part of the whole workflow.

As new technological platforms and trends have taken centre stage and businesses adopting digitization in a big way, QA courtesy software testing services has undergone a change as well. In the test-driven SDLC environment, development and QA have become mutually complementary rather than exclusive. Thus, QA experts belonging to any software application testing firm should adopt new developments in the field of testing. Let us find out what is new in the software testing services market.

New trends in implementing software testing methodology

With quality, cost, and competition becoming the guiding parameters for businesses in setting up their objectives, they need to adopt the latest software testing approach.

·         Test automation sans scripts: As changing customer preferences and competition demands quick release of quality software applications, businesses have included test automation in their testing arsenal. Automation helps to do away with mundane testing efforts and accelerates the process. However, one of the biggest challenges in implementing test automation is writing test scripts. This requires considerable expertise and experience on the part of testers, for any missing parameter would render vulnerabilities or glitches to remain undetected.

This is where scriptless test automation can address the challenge and enhance the scalability of testing - functional, regression, performance, etc. In scriptless test automation, QA experts get on with automating test cases without worrying about coding. This way, a lot of time that is likely to be spent on understanding and writing the code, can be saved.

·         Big data testing: With digital transformation picking up speed, a humongous quantum of data gets generated across business verticals. Since such data can have hidden vulnerabilities and glitches with wider security ramifications, these need to be tested rigorously. At the outset of big data testing, the quality of data is ascertained on the basis of factors such as integrity, redundancy, completeness, consistency, conformity, and accuracy. In this software testing approach, QA experts ensure the success of testing, especially performance and functional, using commodity cluster and accompanying components.

·         Artificial Intelligence: The advancements in Artificial Intelligence and Machine Learning (AM and ML) have been utilized by software testing services for quite some time now. These help in optimizing the testing scenario by generating better test data, cases, scripts, and reports. Smart analytics provided by AI and ML can help QA specialists to identify faults and the areas for testing. AI and ML can help in prioritizing such test cases and predict risks associated with a software release. In the ultimate analysis, taking the help of AI and ML can ensure greater accuracy in software application testing.

·         IoT testing: With digital transformation becoming a reality, the presence of connected devices communicating with each other over the internet has increased. Since the performance of these IoT components has a bearing on the overall functioning of a smart system, they need to be tested. The slew of tests associated with IoT testing checks and validates the usability, compatibility, scalability, performance, data integrity, and security of IoT devices. For example, the performance of a driverless car can only be successful and optimal should the various sensors embedded in the car function to their optimal level. Any glitch thereon can lead to unforeseen outcomes such as the car meeting with an accident.

·         Penetration Testing as a Service (PTaaS): Digital transformation is accompanied by security challenges as well. Of late, there have been cases of businesses and individuals being hit by malware, ransomware, trojans, and other attacks. This is where penetration testing can help by identifying the vulnerabilities in a cloud-based or in-house system. The PTaaS methodology helps in validating the entire technology stack used in an organization.

Conclusion

The software application methodology should incorporate the latest trends in testing to address the growing challenges of compatibility, security, and scalability. These trends would help businesses to deliver foolproof software applications and enhanced user experience.

Check out this e-Book showcases insights and trends observed in the software testing space based leading analyst reports, empirical findings and observations

Information technology is transforming the landscape of industry and business in a big way. It has given convenience, engagement and connectivity a new meaning with technologies like IoT, artificial intelligence and machine learning, blockchain, and augmented reality among others. As the world moves towards automation and its attendant benefits, the software testing trends in 2019 will be interesting to watch. The benefits include an increase in quality, speed, productivity, efficiency, collaboration as well as reduction in glitches, wastage, and cost. Moreover, the advent of Agile and DevOps has caused disruptions in software application testing. Let us have a look at the software testing trends of 2019 that businesses can integrate into their arsenal to remain competitive.

The Testing Trends of 2019

#1. Better DevOps and Agile collaboration: If Agile is about changing the dynamics of QA by integrating the development and testing teams within a single sprint, DevOps goes a step further by creating a quality culture in the entire organization. DevOps came about as Agile fell short in taking the quality paradigm further in the organization. With customer experience becoming the sole determinator in competition, organizations cannot ignore the last mile connectivity for customers. DevOps is a software testing approach that ensures a seamless coordination between the silo-based departments of an organization to achieve the overall objective. A better collaboration between DevOps and Agile would replace the specific test groups with nodal groups executing a dynamic quality analysis. DevOps and Agile would help deliver quicker and superior test outcomes for businesses at fewer costs.

#2. Use of AI and machine learning in software application testing: Artificial intelligence is going places especially in the IT industry. It helps to transform workflows and processes by creating unique and optimized test cases. By mining historical data, AI can identify the source of glitches in billion lines of codes and predict customer preferences for products and services. AI and machine learning can find out the high-risk areas of software and prioritize regression testing in real time. For example, the AI-based software testing methodology is best placed for use in the banking sector. It can identify fraudulent transactions by analyzing the pattern of historical data and help the banks to uphold both the customer interest and brand proposition.

#3. QA to quality engineering: If traditional QA is all about testing a product post development and integration, then quality engineering is much more comprehensive. As software becomes more complex with numerous digital touchpoints, achieving the ultimate customer experience is only possible when quality becomes a pan organizational responsibility rather than that of a specific department. The forthcoming software testing trends will see QA giving way to quality engineering. This entails managing, developing, operating, and maintaining the IT architecture with high standards of quality.

#4. Performance engineering: Prevention is better than cure, so goes the saying. This applies to software testing strategy as well wherein the focus is not merely on identifying glitches in software but finding ways and means to pre-empt them. If performance testing is about testing the performance of software on metrics like throughput, scalability, response time, resource usage, capacity, stress, volume etc., performance engineering is all encompassing. The year 2019 will see a shift towards adopting performance engineering wherein the focus will be on analyzing the workings of various elements in a software. In an IT ecosystem where achieving customer satisfaction is imperative, performance engineering helps stakeholders across departments to deliver items with the highest priority. It focuses on the design, development, and delivery of quality software products and services.

#5. Cloud-based testing tools: Enterprises are leveraging cloud-based platforms like AWS in a big way, thanks to the latter’s attributes like scalability, flexibility, security, and cost effectiveness. The software testing trends in 2019 will see the utilization of cloud-based testing tools such as Selenium, Robotium etc. The range of testing shall include cross browser testing, performance testing, usability testing, regression testing etc.

#6. Use of open source tools: Rising competition has forced businesses to cut input costs without compromising the quality of deliverables. With test automation tools adding to the input cost, the shift is more towards using open source tools such as Selenium, JMeter, Grinder etc. The quality of open source tools is no way less than the premium ones for they are backed by an extensive community of developers.

#7. Big data testing: The latest software applications with access to multiple touchpoints generate large volumes of data. Since testing such big data with traditional computing techniques is not possible, the software testing approach needs to have a relook. The year 2019 will see the testing of big data in a major way to tackle the software testing challenges. The testing is imperative to ensure the integrity, completeness, validity, and compatibility of big data across the SDLC and beyond.

#8. Robotic process automation (RPA): Increasing the efficiency of legacy systems has become a challenge for businesses. However, by implementing robotic process automation, the efficiency of such systems can be enhanced to a large degree. The automation technology helps enterprises to perform critical and repetitive jobs with speed and perfection.

#9. IoT testing: Embedded software in devices has become commonplace, especially in consumer durables. The quality of software will ultimately determine the seamless running of a device’s features and functionalities. The year 2019 will see more emphasis being given to the testing of such embedded software in this world of Internet of Things (IoT) especially in terms of usability, compatibility, reliability, scalability, data integrity, performance, and security.

Conclusion

With the quality of software determining the customer experience, software testing will move into frontiers like IoT, performance engineering etc. The year 2019 will see more emphasis to be given to the adoption of new technologies and testing to be executed along the DevSecOps way.

The complexity of software applications has grown manifold thanks to an increase in their delivery touchpoints, device platforms, functionalities, features, networks, and embedded technologies. Businesses more than ever before have their tasks cut out – develop quality applications and accelerate their delivery to the market. Furthermore, developments such as Agile and DevOps with their outcomes focused on Continuous Integration and Delivery, the software testing strategy needs a transformation of sorts. This has become important for businesses to stay relevant and exceed customer expectations.

Let’s find out why the software testing strategy of your business should undergo a change in 2019.

# 1. The growing footprint of IoT: You must have seen increased automation in consumer durables like television, air conditioner, geyser etc. For example, the display of your television set can adjust its intensity based on the ambient light. Also, the geyser switches off on its own should the temperature of water reaches a certain threshold. Such experiences have become possible due to the presence of embedded software in these devices.

Now, unless the quality of such embedded software remains top notch, the functioning of these devices will leave a lot to be desired. Hence, the software testing methodology should not only test the application(s) independently but also as an integral part of the device. As IoT spreads its footprint all across, software application testing should be comprehensive ensuring each and every functionality and APIs meet their desired objectives. It calls for adopting a software testing approach with a view to validate the software’s usability, compatibility, scalability, reliability, security, performance, and data integrity.

#2. The advent of AI and machine learning technology: The year 2019 will see AI and machine learning expand their outreach into newer domains. Moreover, software application testing too would involve the use of AI to meet the challenges of growing complexity, repetitive tasks, and accelerating the time to market etc. These will augment the capabilities of human testers manifold and release them from conducting mundane, repetitive or highly critical tasks. This will lead to the optimization of resources and the meeting of stringent turnarounds.

#3. Big data testing: A number of software applications with a multitude of APIs generate a humongous amount of data. For example, software buying tickets for airlines or railways among others. Here, such volume of data needs to be validated on criteria such as performance and functional testing. In addition to the software, the data should also be validated on parameters such as accuracy, completeness, security, consistency, integrity, and validity.

#4. Shift towards performance engineering: With the quality of software applications being determined by the number of their digital touchpoints and APIs, traditional performance testing needs to give way to performance engineering. In other words, the software testing approach will no longer test the performance of software on metrics such as load, test, spike, scalability, endurance, and volume alone. Performance engineering would entail analyzing how various elements of a system collaborate in a seamless way to deliver quality outcomes. The elements would include the entire sweep of business and software – security, usability, performance, hardware, operations, ROI, and customer experience.

#5. Shift towards quality engineering: The traditional quality assurance methodology does not fit into the emerging scheme of things anymore. As Agile and DevOps become the de facto industry benchmarks, the shift is more towards ensuring integration and testing throughout the SDLC and beyond. This calls for engaging quality engineering to foolproof a system against inherent glitches in the entire development and operations processes by adopting test automation.

#6. Challenges of test automation: The growing complexity of software with demands such as delivering faster testing cycles, identifying glitches early and ensuring a greater test coverage means adopting test automation in a big way. This helps the team to focus on tasks such as preparing automated test cases, simulating variables and analysing test reports. Since testing would be both for the functional and non-functional aspects, test automation becomes the best approach.

#7. Security of applications: Even though software technology is making our daily tasks convenient, easy, fast and glitch free, the spectre of security breach looms large. With security becoming the single most challenge facing testers and developers in 2019 and the foreseeable future, the DevOps testing methodology should include security in the form of DevSecOps.

Conclusion

Testing software has become the sine qua non for businesses to deliver glitch free applications and ensure better test coverage and a faster time to market. The year 2019 will see businesses adopt new testing methodologies to stay ahead in the competition and achieve great customer experience.

Software development has become the sine qua non for enterprises looking to embrace digitization to increase efficiency, reduce waste, and achieve ROI. At the same time, reaching out to customers with quality software in the quickest possible time is what drives the whole digital ecosystem. However, maintaining the quality of software is not always an easy thing to do, for there is a conflicting business imperative of keeping the cost within limits as well. Add to this the rapidly changing technology and development models and you are caught between the devil and the deep sea – executing rigorous testing and keeping the cost within limits.

If previously it was about following the traditional waterfall model of manual testing to identify and eliminate bugs in the software, today the situation is different. The difference has come about on account of an increased threat of malware, changing customer preferences, and an intense competition to be the first one in the market. The three imperatives can only be met if enterprises ensure the security, quality, and speedy delivery of software. Needless to say, the traditional waterfall model of software testing does not fit the bill to address the above mentioned imperatives. The reasons include the model’s slowness in executing manual testing, poor test coverage, and a little scope for repetitive testing.

The Agile DevOps model of Testing early and frequently

The inadequacy of the waterfall model stemmed from the fact that software testing post development and integration increased the complexity of eliminating glitches. This is due to the fact that developers, in order to fix glitches, had to unbundle the codes to reach the unit level. This led to the adoption of the Agile model of software development where the software testing approach is shift left. In other words, the testing of software is done early on during the development cycle as the two processes (development and testing) are executed simultaneously in what is called a development sprint. The software testing approach forms part of the Agile and DevOps methodologies in which Continuous Integration and Delivery are the key outcomes.

In today’s scenario of software development, Continuous Integration pre-empts many integration glitches. It ensures the SDLC delivers a well-knit software that meets the business objectives. In simple terms, the Agile model of software testing methodology incurs fewer expenses to fix glitches vis-à-vis the waterfall model.

Why Agile?

The software development process, especially the mission critical one, has become complex. This is due to the fact that software needs precision to be able to function effectively across a slew of digital elements. These include devices, platforms, frameworks, operating systems, browsers, and networks. To meet the deadlines of software development, testing, integration, and delivery within the specified time and cost, different teams work collaboratively.

The future of software testing – is test early, test frequently still relevant?

The advent of new technologies has whetted the appetite of customers. The customers of today look for software that has amazing features and functionalities, and at the same time, are quick and precise in delivering outcomes as well. Moreover, as the functioning of software depends on its interfaces (read APIs) with the immediate digital environment, the resulting complexity can only be addressed by the Agile-DevOps’ testing early and testing frequently software testing methodology.

Also, since the end customer experience has become the be-all and end-all objective for enterprises to stay competitive, the cornerstones of DevOps viz., Continuous Integration and Delivery have become the rigmarole to be followed in any SDLC. To ensure tomorrow’s software straddling cutting edge technological paradigms like Artificial Intelligence and Machine Learning, Internet of Things, Blockchain, Predictive Analysis, Big Data and others delivers the business objectives, the following processes should be embraced.

Test Automation: The best way to address the software testing challenges is by embracing test automation of a select number of processes. In fact, Artificial Intelligence can be used to write automated test scripts and predict glitches based on analysing the past outcomes.

Security Testing: The security threats from malware have become arguably the biggest challenge facing the global digital ecosystem. To address the challenge, every software should have security as an integral part of its testing in the DevOps mould.

Regression Testing: The slew of modern software testing validating the performance, functionality, usability, and security of software should have regression testing at the unit level. This would enable the software to be compatible with the many versions of the elements of digital environment.

Conclusion

Test early and frequently is not passé but rather underpins the whole software application testing architecture in the SDLC. Its relevance is only going to increase as more enterprises take the digital transformation initiative to stay competitive.

Software underpins the success or failure of any digital transformation initiative. In a world increasingly driven by computers and automation, software stays at the heart of a system executing things that were unthinkable in the past. It has helped create a human machine interface thereby reducing the human effort to a large extent. The customers of today have become the biggest beneficiaries of machines (read computers, tablets and smartphones) that are run by software wherein a host of activities gets done. These include paying utility bills, shopping from eCommerce stores, watching movies, booking hotels, cabs, trains, and airlines among others. However, executing these activities in a seamless manner can only happen when the software behind them works optimally. If not, the customer experience can eventually go for a toss. Thus, software testing and quality assurance have become important cogs in the wheels of software development and delivery.

What is quality assurance?

Quality assurance is a series of activities that check if the software, and processes based on that, deliver outcomes that conform to the business objectives. It checks and validates the requirements and standards for a software in the entire SDLC. Quality assurance thus identifies the bugs or glitches present in a software before its eventual deployment for customer use.

With growing competition, providing the best customer experience has become the primary objective of businesses to achieve ROI. And a glitch prone software application would be a detriment to realize that objective. Thus, software quality assurance testing has taken the centre stage in any organization’s scheme of things. The software application testing activity reveals inconsistencies, glitches, and redundancies in a newly developed software. It ensures the same are flagged off and removed (by the developers) to provide a better customer experience and longevity.

Where does software quality assurance services come in SDLC?

Traditionally, QA used to come after the development of software and before its eventual deployment. Since this approach was found inadequate to meet challenges like accelerating the time to market, testing the software across devices, platforms, devices, and networks, and offering a robust customer experience, it was discarded in favour of Agile-DevOps method of software development, testing and delivery. Thus, in the Agile-DevOps methodology, QA is part of the development sprint instead of operating in a silo.

Reasons to apply software application testing

#1. Enhance the quality of software: A bug filled software can impact the customer experience by not functioning to its optimum or as per expectations. It may lead to latency, downtime or render the features ineffective. As a software is supposed to reduce human effort and offer specific services, the presence of glitches can put paid to everything. And in the competitive business environment where customers have a range of choices, a glitch prone software can spell trouble for the business. For the customers will not have second thoughts in discarding the software and moving to the competitors.

#2. Prevent security issues: The software application testing exercise, especially security testing, can identify (and later eliminate with the help of developers) malware, trojans and viruses present in the software. This prevents hackers from siphoning off sensitive business and customer information or even money. For example, eCommerce or banking apps make use of network banking, credit and debit cards to make payments. However, if these apps are affected by malware, it can result in customers losing money.

#3. Performance: Quality Assurance tests the performance of software by conducting load test, stress test etc. These tests ensure the software runs to its optimum when the workload increases. For example, eCommerce stores experience a rush of customers during holidays or Sundays. So, if the online stores are not tested beforehand for their performance, they can suffer from latency, downtime or even server crashes. Needless to say, such outcomes can hit the customer experience and bottom lines really hard.

#4. Saves time and money: It is better to identify and fix glitches early on in the SDLC rather than going through an elaborate exercise later. As the saying goes, ‘a stitch in time saves nine,’ carrying out software testing in the initial phase of software development can save time and money.

Conclusion

The quality of software is arguably the most important attribute to garner customer approval. It is important for any business engaged in the development of software to validate the latter’s quality before delivery. By making a software quality compliant, the company can boost its brand, market, and ROI.

The increased emphasis on the quality of software and accelerated time to market has made software development and testing a complex affair. Add to this the complexities arising out of API interaction and leveraging of legacy systems and you end up mulling about a new and innovative way to manage the whole canvas of software testing. Moreover, studies have shown that the latest software testing approach involving a greater testing of mobile and hybrid applications has shrunk the actual time for development.

The breakneck speed of the launching of new products or services has reduced the scope for keeping the development team updated of the impending software issues. This in turn has made the developers and testers understand and appreciate the need for a smarter and efficient software testing approach. Since artificial intelligence and machine learning have become the new technology juggernauts, it is about time the software testing companies also adopt these to bring efficiency, scale, and speed to their testing efforts.

Before extolling the virtues of artificial intelligence and machine learning, let us first understand why the traditional software testing methodology has come a cropper in meeting these challenges.

Changing user preferences: The traditional software testing approach is more about identifying and fixing the mismatch in a test case when an input does not behave as per expectations. In such a case, the tester should typically run a checklist to mimic the steps of the users. Such an approach used to suffice when the amount of data was relatively manageable. However, with the development of new applications having multiple interfaces with devices, platforms, networks, and frameworks, the amount of data generated has become humongous. Consequently, the testing of such big data with the traditional method of QA has become difficult and calls for a software testing approach involving new technologies such as artificial intelligence, machine learning, and predictive analysis.

Shortcomings of manual testing: Humans are not capable of showing constant attention to the job at hand. They are prone to making errors and overlooking the bugs in the system. Besides, any repetitive nature of testing would leave the human element exhausted and error prone. This is where test automation with machine fed intelligence can produce accurate test results. So, no matter how big the data is or how frequent or repetitive the tests are, AI and predictive analysis can be of help.

Greater automation: The manual way of testing has its glaring restrictions. For example, humans cannot work round the clock and have a limited attention span. On the other hand, machine aided testing can be done on a consistent basis irrespective of the time period. This approach suits the requirements of Agile and DevOps especially related to continuous testing, integration, and delivery. Thus, not only the testing gets accurate and time saving, it enhances the user experience and ROI as well.

Let us find out how software testing with AI can be made possible

Predict customer needs with machine learning & predictive analytics: The changing market dynamics and increased competition have necessitated the need to predict what the customers want. So, instead of the customers reaching you, it would be better if you reach the customers beforehand with their choicest products/services. This would mean predicting their shopping needs by analyzing past consumer behaviour. The behaviour would bring to light the shopping patterns and give you an insight into the kind of products or features the customers would seek.

Streamline the testing process: Frequent product releases and updates can put a lot of strain on the testing team and processes. The machine induced testing a la AI can register changed controls and updates much more efficiently than humans. Importantly, the vast range of dependencies with hundreds of input combinations can be validated accurately and consistently with AI.

Types of tests and data driven decisions: While planning for QA, testers are often at their wit’s end about finalizing the test processes. This is where AI with its user behaviour analysis can be of help. In fact, AI can ascribe a risk preference while analyzing the user behaviour. It can monitor, evaluate, and categorize the same, and determine the kind of tests to be conducted. Testers, on their part, can automate redundant test cases and take data driven decisions. When time is a critical factor in testing, it is better to focus on crucial test cases and give them a greater coverage by leveraging risk-based automation.

Saving time and cost: When the help of AI is taken in terms of creating, modifying, analyzing, and executing test cases, testers can do away with manually updating them. They can rather identify controls and defects much more effectively. Also, when the source code undergoes modification due to repeated software testing, validating them each time manually is time consuming and costly. Whereas an AI based test automation can be executed repeatedly at a faster rate leading to cost savings.

Use of AI based tools: AI or machine learning based test automation tools such as Functionize, Test.ai, Testim.io, Appvance and others can create, execute, and maintain various types of testing thereby increasing the stability of test suites.

Conclusion

Artificial Intelligence and machine learning have found their way into the SDLC leading to a better authoring and executing of test cases. Notwithstanding the initial investment in implementing an AI based test automation system, the results in terms of better products that suit the customer needs, can save costs and improve your brand equity.