The complexity of software applications grows by the day with the incorporation of AI and ML technologies. The AI testing of such applications can help them meet business objectives and quality considerations.
The progression of technology has manifested itself in the form of developing a range of software applications. These applications have transformed our lives in more ways than one, be it in the realms of eCommerce, health and fitness, entertainment, manufacture, communication, travel, education, food and beverage etc. With the growing appetite of customers to operate futuristic apps, app developers have gone in for leveraging technologies like Artificial Intelligence (AI) and Machine Learning (ML). If earlier, AI was confined to research laboratories or big corporations, today, it has found usage in data collection, processing, analysis, and computation.
The leapfrogging of AI and ML technologies into everyday lives is due to the proliferation of applications across industry verticals. In fact, the incorporation of AI and ML into applications has helped improve the efficiency of processes. The potential of AI in processing large datasets and predicting a pattern has been leveraged by industries to study customer behaviour and predict the changing market trends. It can be reasonably argued that the sweep of AI and MI will be much more comprehensive in future.
Today AI and ML are used in sophisticated input models to detect glitches or deviant behavior in processes. These are more probabilistic in nature as opposed to the deterministic nature of the traditional rule based IT systems. Since the input models are critical in the running of AI-based applications, they should be tested for their quality and robustness. Just to cite an example, modern jet planes or automobiles use a million lines of codes to develop mission critical intelligent systems. If AI testing of codes is not executed to check for glitches or vulnerabilities, the performance of the final product (read jet planes or cars) can leave a lot to be desired. Even though AI testing of applications has become the prerequisite to enhance their performance, it comes with a few challenges as well.
Challenges in artificial intelligence app testing
- The sensor-based test datasets could be huge creating challenges of storage and analytics.
- The AI-based systems generate vast amounts of data in real time during unanticipated events. These are often difficult to collate and analyse.
- The test scenarios should identify and eliminate cases of built-in human biases.
- The artificial intelligence testing services amplify the presence of glitches. This turns the fixing of glitches into a difficult exercise.
Why should ML and AI-based applications be tested?
Enhanced customer experience: The be all and end all objective of developing, testing and deploying applications is to enhance the customer experience during the entire lifecycle of the former. This is in view of the intense competition wherein customers, if not happy with the app, can discard the same in favour of the competing one. Artificial intelligence testing services can validate an application in terms of its functionality, performance, usability, and security. Remember, a satisfied customer not only keeps the revenue stream flowing but brings in potential customers as well.
Conformity to regulations: The rising graph of cybercrime has led to the setting up of a slew of regulations and protocols. These should be fully adhered to in order to minimize, if not eliminate, the exposure of applications to malware, viruses, trojans etc. The regulations such as PCI, IEC, and HIPAA among others should be fully complied with. The AI app testing services ensure the conformity of AI-based applications to the above mentioned regulations or specifications. This way, the test services pre-empts an organization from falling foul of the regulatory authorities inviting censure, penalties and litigation.
Improve the quality: The AI app testing services verify and validate the software application as per its stated business objectives. By identifying and eliminating glitches, the organization can reduce costs, which would otherwise go into providing customer support and service. The improved quality of application generates customer goodwill thus helping the organization to achieve ROI and profits.
Keeping with the times: Technology evolves from time to time thereby allowing the change of customer preferences as well. The AI testing framework validates the quality of an application as per the technological advancements of the day.
Testing the core algorithm: The AI and ML-based systems have a complex algorithm at their core. It is the algorithm that processes data and generates insights for the stakeholders. The AI app testing services ensure the learnability of an application to its optimum. For example, an AI-based application can learn about customer behavior in an eCommerce store and present customers with the merchandise that they are likely to buy.
Artificial Intelligence and Machine Learning are becoming the choicest technologies of the day given their role in improving efficiencies and performances. Testing applications that are based on these technologies has become critical in achieving quality, customer delight and ROI.
Komal Lopez works with Cigniti Technologies and is instrumental in helping enterprises take better decisions related to Quality Assurance products, tools and services by leveraging research and content. She specializes in writing about technology trends, testing trends and has been in the Marketing and Communications industry for over a decade.
Twitter Username: https://twitter.com/savkomal