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How Can Ai Help To Enhance The Software Quality In 5 Different Ways?
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The digital transformation initiatives of enterprises have become comprehensive and complex in their scope and challenges. These initiatives entail the development and delivery of myriad software applications, which in turn leverage a host of digital elements. These elements include API’s, legacy systems, platforms, frameworks, cloud, cutting edge devices, operating systems, browsers, and networks. The complexities and challenges brought about by the interaction of applications and digital elements (as mentioned earlier) have witnessed a paradigm shift in the Quality Assurance process. To ensure deliverables such as quality and cost savings within small turnarounds, the leveraging of machine based intelligence has become important. Artificial intelligence, apart from improving the quality and scope of test automation, helps in analyzing huge chunks of data to draw meaningful inferences. AI application testing is poised to do away with traditional manual testing processes and help stakeholders to implement the Agile-DevOps model in a better way.
The artificial intelligence testing services assess and analyze software applications under specific parameters/conditions to identify the risks and overall threshold before their eventual delivery. AI testing paves the way for developers, testers and members of the operations unit to be convinced of the quality of software as well as understand the specific software attributes that would appeal to the end customers. AI testing helps the QA teams to impart quick and precise feedback to the development teams. This is of utmost importance given the speed of software launches and the mindboggling array of digital elements the software needs to have an interface with. The QA requirements of today have shifted to smart testing from the traditional waterfall model of testing. Furthermore, the pace of new software releases notwithstanding, the frequent updates too are needed to be factored in by the QA team. This is where leveraging AI application testing becomes essential.
Let us delve into five reasons as to why artificial intelligence has become crucial to enhance software quality.
Why use artificial intelligence app testing?
#1. Better accuracy: A proper QA exercise includes the validation of each and every feature and functionality of the software. This is not always possible in the traditional waterfall model of manual testing given the factor of human fatigue in testing repetitive and complex processes. Even the much vaunted test automation might come a cropper if the QA team is not able to create proper test scripts for specific processes. However, AI application testing can analyze the testing requirements and create suitable automated test scripts. Also, the AI testing tools can generate meaningful data for the QA team to take relevant inferences about enhancing the quality of software. As testers are freed from carrying out mundane tests, they can utilize the time to create better test automation suites.
#2. Simulated testing: Given the preponderance of test variables interfacing with myriad digital elements, it is virtually impossible to validate them by any QA department. However, AI app testing services can simulate any number of virtual users (even running into thousands) who can interact with the software application. In doing so, these set of virtual users can validate the features and functionalities of the software application, especially when the latter interfaces with sundry digital elements. The virtualization process checks each and every test variable and identifies the glitches lying therein. Thus, the quality of software gets enhanced significantly in tune with the business requisites leading to customer satisfaction.
#3. Easy testing process: The foremost challenge for any QA tester is to write automated test scripts that examine a vast amount of data. This can delay the testing process thereby impacting the overall turnaround time – a critical lapse that can have an adverse impact on business. Remember, customers are generally attracted to new products, and if the same turn out to be innovative, qualitatively superior, and feature rich, the business delivering such products can reap rich dividends. However, such lapses can be easily mitigated or eliminated by leveraging artificial intelligence app testing. The powerful algorithms of AI can create sophisticated test scripts that can analyze and sift through copious amount of data. Moreover, AI can analyze past test records and evaluate the entire SDLC to suggest the areas of testing and predict glitches. This can significantly increase QA efficiency and save time.
#4. Helps developers: The development teams are dependent on the QA teams to identify glitches that may have been incorporated into the software. This has a bearing on the overall turnaround time. What if, the development teams conduct some sort of initial screening of the codes before handing the latter to the QA teams? This can help the development teams to remove the gaps or bottlenecks that have crept into the codes. This becomes possible when an AI testing framework is considered as a part of DevOps. The development teams can leverage the AI testing tools to conduct an initial screening exercise. Consequent to this approach, the efficiency of the QA process improves and crucial time/cost is saved, which otherwise would have gone into rework. AI can detect the source of bugs in the software, which can help the development teams to carry out suitable modifications in the codes thereby pre-empting the entry of bugs.
#5. Improves the test coverage area: Traditional QA leaves a lot to be desired when it comes to the scope of testing. AI, on the other hand, can look into areas such as memory, database, internal file contents, and program states to test if the software is delivering outcomes that are consistent with the expected ones.
The AI app testing services are increasingly leveraged by enterprises to turn their QA processes precise, elaborate, quick, and cost effective. These services analyze and offer test outcomes that improve quality, streamline processes, quicken the pace of delivery, and achieve ROI.
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