Why are intelligent test systems the need of the hour?

Shitendra Bhattacharya, Country Head & Director (Sales) - India & ASEAN for Emerson's Test & Measurement Group | Wednesday, 28 August 2024, 04:31 IST

In an exclusive interview with CIO TechOutlook, Shitendra Bhattacharya, Country Head & Director- India & ASEAN for Emerson's Test & Measurement Group stresses on the urgent need of intelligent testing systems and the role they play in enhancing cybersecurity while emphasizing the need of an engineer’s intervention in an AI demanding world. He is also a pre-seed and angel investor/mentor and consultant in start-ups that are disrupting markets.

In today's fast-paced software development environment, how do intelligent test systems enable companies to maintain high-quality standards while adhering to rapid development timelines?

Intelligent systems generally leverage artificial intelligence (AI) and advanced data analytics in a bid to automate the testing process. The main motive here is detecting defects at an early level. This is also done to get insights on data in real time. Proper execution in early stages helps in reducing time and effort that is required for testing, thus maintaining quality without compromising on speed of said tests.

Automation of routine tasks and optimization of the various workflows will enable faster development cycles. This action further cements the upheld quality, which will not collapse with the advent of tight deadlines because the more significant challenge lies in the time that the organizations take to market.

How do intelligent test systems differ from traditional testing methods, and why are they considered essential in today's rapidly changing technological landscape?

Traditional testing methods are incredibly reliant on manual processes and predefined scripts, which are tremendously time-consuming. Another drawback that can be seen here is its lack of adaptability to changes.

Intelligent testing, on the other hand, will employ AI to adapt to all the changing conditions the operator is operating in; it’ll also foresee potential problems and simultaneously provide a reasonable amount of insights into the system's performance. This testing method is crucial because it can handle the rate at which modern technology is progressing. The traditional method falls short in this regard. On a fundamental level, intelligent testing is more flexible than traditional testing.

Cybersecurity threats are constantly evolving, how do intelligent test systems enhance the detection of security vulnerabilities compared to traditional testing approaches?

Intelligent test systems enhance cybersecurity by continuously analyzing the vast amounts of data which allows it to identify irregular patterns and anomalies that are indicative of security vulnerabilities. When a system is AI driven, it can adapt to the new and emerging trends rapidly compared to the traditional counterparts which is obstructed by manual updates and adjustments.

The intelligent test systems are also proficient in attack simulation. Deploying a digital twin to simulate attacks, which in turn allows a proactive security testing approach by enabling quicker identification. Subsequently, the remedy can be executed promptly.

How do intelligent test systems manage the challenges posed by the growing IoT ecosystem in terms of data handling and interaction testing?

The IoT ecosystem generally involves massive amounts of data and complex interaction with numerous devices in said ecosystem. Here, intelligent testing can manage these challenges by implementing AI to analyze the different pools of data streams in real time. This action will ensure accurate and efficient data processing.

The operator can also simulate interactions between different devices via the digital twins concept, which will further enable thorough testing of how diverse components work together in an entire IoT farm or a connected set of devices. This property will yield a secure deployment for the devices.

How do intelligent test systems enable more autonomous testing processes, and what are the implications for the future of software testing?

Intelligent test systems will enable more autonomous testing by automating all the low-value tasks and capitalizing on AI for decision-making and recommendations during the testing process. This is the primary objective. Here, it is imperative that the need for human intervention is non-existent and that the operator focus on complex issues or domain challenges.

The implications for the future will include a shift towards being a more device-centric ecosystem, building a device-centric workflow where the testing system requires minimum human intervention, thus leading to a far more efficient testing process. The autonomy will drive further advancements in the software quality, which subsequently will lead to excess availability of time for marketing of new products.

It is imperative we understand that intelligent testing isn’t a total adoption of AI and ML. This means that one must incorporate the intelligence of the system in tandem with human intelligence. Setting up an intelligent system requires adherence to specific protocols, which involves the union of all the different elements. It is not a 100% AI but an AI-enhanced system. This means AI is essentially providing the engineers with information on which they will need to base their decisions. At the end of the day, intelligent systems are intended to mitigate risks and give the industry more time to market quality products.