#ai application testing

The process of digitization and the advent of the internet have brought about a revolution of sorts touching all walks of life. However, manufacturing and operations have significantly gained by incorporating the digital trends in terms of augmenting production, streamlining the value chain, enhancing efficiency, removing or cutting down on redundancies, improving the quality of deliverables, and driving innovations to prepare the organization for future challenges among others. The digital revolution has been successful in addressing the global business challenges - rising cost of energy, logistics, and distribution etc. Moreover, new digital technologies such as the Internet of Things (IoT), Artificial Intelligence and Machine Learning (AI & ML,) 3D manufacturing, big data and analytics, and robotics have significantly altered the value proposition and capabilities of the global business and industry.

Internet of Things or IoT is essentially a proliferation of digitally connected devices that streamline the flow of information from one node to another. This interrelation eliminates the need for a human’s or a machine’s intervention to effectuate data transmission. If IoT devices are the practical enabler of digital transformation, Artificial Intelligence is the key driving force. It is next to impossible to manually make sense out of the voluminous data being produced by these IoT devices. This is why, there is a need to amalgamate the power of automation and cognition of humans. Artificial Intelligence is a product of such amalgamation. Mimicking human-level intelligence, AI is effectively taking over mundane tasks, resulting in higher productivity and better quality.

In the competitive business landscape of today, the enterprises that can thrive and be competitive are the ones that have gone for digital transformation. The process includes revamping the processes, departments, resources, models, and activities within enterprises by leveraging the latest digital technologies and the possibilities and opportunities they offer. The digital transformation services involve the breaking of silos among departments and streamlining communications across the value chain, upgrading the digital assurance and testing processes, and strengthening the customer feedback mechanism.

Factors driving digital transformation initiatives

Changing customer  expectations and market dynamics.

The need to deliver quality product faster and within the turnaround time.

The need to enhance the  operational efficiency to develop and deliver products/services that meet customer expectations.

Digital transformation has become the need of the hour for entities operating across domains such as healthcare, banking, insurance, and manufacturing, among others. It drives automation of processes, innovation, connects the value chain with various device platforms, leverages cloud, and adopts the latest build and test paradigms like Agile, DevOps and DevSecOps. Besides, it includes deriving business intelligence by applying predictive, descriptive, and prescriptive analytics on historical datasets.

Why AI driven digital transformation?

The enterprises of today need to go beyond merely improving their process efficiency through automation, albeit that is important as well. The futuristic goals would include developing and delivering products/services as per the needs and expectations of customers. In doing so, customer data could be culled from various digital touchpoints including the social media to be analyzed threadbare to obtain actionable outcomes. AI can be leveraged by business stakeholders to make informed decisions on products or services that are aligned to customer choices and market trends. AI driven digital transformation can be a catalyst to generate actionable business intelligence besides offering personalized customer experiences. Let us discuss a few scenarios where AI led digital transformation services are changing the norms of user engagement.

Banking: The banking sector is saddled with Non-Performing Assets of mind-boggling proportions. This has been caused by the non-payment of dues by individuals and businesses. An AI led banking system architecture can collate financial data about customers’ or business’ account details, defaults, buying preferences, and sundry activities, to get the true picture of their financial status. This insight can act as a bulwark for the banks disbursing a loan or mortgage. Furthermore, AI and ML can help banks to make better decisions using real time data about staff’s productivity, efficiency, and find out the outliers or exceptions related to the accounts. Also, today, when incidences of cybercrime or/and internal frauds are becoming common occurrences across the BFSI domain, an AI driven banking system can red flag accounts or processes that show signs of being compromised.

Risk and Compliance: Modern enterprises often go for takeovers or Mergers and Acquisitions (M&A) to stay competitive. However, these can be fraught with risks as insolvent businesses can manipulate accounts to show themselves as thriving. In such a scenario, the company going for M&A or takeover can leverage AI and ML to garner actionable intelligence to find out whether the proposed ‘union’ is conducive. The AI system can gather information and understand from unstructured documents, bank accounts, insurance details, and loan statements. It can help enterprises to mitigate the risk of non-compliance.

Chatbots: AI/ML led chatbots have become a rage with enterprises deploying them at digital touchpoints to address sundry customer queries. These have improved customer interaction and help to meet their expectations. Since AI driven chatbots can operate 24 x 7, customers can get their queries resolved at any point in time. Enterprises too can save precious man hours and expenses on deploying the workforce beyond office hours and on holidays/off days. The best example of an AI led system can be the Facebook algorithm where one gets to view posts that are aligned to their preferences.

IoT as an enabler for transformation

If AI can act as an enabler of digital transformation, why should the Internet of Things (IoT) be left behind. The IoT envisages the interconnection of all digital devices or smart systems over a network (internet or intranet) for communication without any human input. Also, devices owned by customers generate copious amount of data that can offer rich insights into their lifestyles, thought processes, impulses, and preferences. IoT transformation can help digital marketers/businesses to understand customer preferences across geographies and demography. It can help them tailor their campaigns effectively and as per the customer expectations. Thus, IoT technology can help enterprises or marketers to generate leads and drive conversions, in the process, creating new or augmenting existing revenue streams. IoT can act as a strategic or transformational asset for enterprises and help them create value for the business as well as end customers.

Conclusion

AI and IoT can transform businesses and create value from analyzing data that are generated through digital devices. These technologies can help to improve the digital assurance and testing models thereby delivering customer delight. Incorporating AI and IoT in the value chain of enterprises can enhance the quality of deliverables and mitigate the attendant risks.

This article is originally published at softwaretestingmaterial.com, AI and IoT are the practical enablers of Digital Transformation.

The spectrum of software testing has broadened considerably. This is due to the fact that the software of today has interfaces with other software, systems, networks, and platforms through APIs. The systems mentioned herein are the various devices (mobiles, tablets, laptops etc) or legacy machines of various make, configuration and resolution. The complexity arising out of such an arrangement renders the testing of software a tricky affair. Importantly, the use of test automation under the Agile and DevOps framework has helped matters to some extent with an increase in the test coverage area, better identification of glitches, accelerating the time to market, and realizing the ROI.

Quality assurance can pre-empt the failure of software, be it in terms of performance, functionality, security, or usability. It has become the pre-requisite for digital transformation for it helps the software application to not only meet the business objectives but deliver a rich customer experience as well. The latter has become an important outcome should businesses want to stay in the competition. However, to ensure greater quality compliance, enterprises are increasingly looking at Artificial Intelligence and machine learning as enablers.

The objective of leveraging AI is to write and execute automated test scripts through QA and move away from the process of manual testing. The latter needs to be done away with given its glaring inadequacies. In fact, AI can help execute complex and iterative testing processes with ease and relieve the testers of their workload. The testers, in turn, can work on other pressing issues concerning software development. The various ways in which AI can add value to the quality assurance efforts are as follows:

Helps in test automation: The competitive market dynamics requires a software product to be delivered to the users before others. To achieve this objective, businesses are releasing software without subjecting it to a proper software quality assurance process. This approach, unfortunately, can break the trust of users should the software contain glitches.

Besides, many IT companies do not want to stretch their budget to accommodate QA software testing given their misplaced business priorities. Since most of the software quality assurance processes involve repetitive testing of inputs and their dependencies, they often leave a lot to be desired especially when used manually. This is where artificial intelligence can help by automating the testing processes and thereby enhancing the accuracy of software.

Facilitates testing: The powerful analytical tools of AI can help in the writing of automated test scripts and derive suitable outcomes. The AI test algorithm can examine a large number of files and sort the relevant areas for testing. This way it can save time, cost and improve the accuracy of the codes. Moreover, AI can predict glitches in the codes based on their thorough analysis. It can help the development and testing teams to focus on areas where the chances of glitches from getting into the system are more. This approach can prevent cost escalation, which otherwise would have arisen due to rework.  

Evolve a bug proof system: Software testers are often at their wits’ end to identify bugs and the point at which they entered the codes. This is mainly due to the lack of due diligence and painstaking QA software testing processes they ought to follow. The AI algorithm can accurately predict and identify the source of bugs. Its incisive and comprehensive analysis can help developers to carry out modifications in the software development process to prevent the entry of glitches. Moreover, since the AI algorithm can run virtually non-stop, it can identify and eliminate bugs, which manual testing is simply not capable of pulling through.

Helps to improve continuous testing: AI can easily create various sets of test data and perform software quality assurance testing at various stages of software development. Thus, each and every code gets into the testing rigmarole (not always the case in manual testing,) which in turn helps to identify hidden glitches. Since AI can perform end to end QA and integration quickly and consistently, it results in enhancing the quality of the application in terms of functionality, reliability, security, and performance.

Conclusion

AI can augment the efforts of QA testers on a scale that is simply not possible to achieve otherwise. AI bots should be utilized as test enablers by the testing teams to reduce their workload and enhance the quality of the software development process.