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Role of Artificial Intelligence (AI) in software quality assurance framework
Role of Artificial Intelligence (AI) in software quality assurance framework

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AI has become a prominent technology in today’s world. It helps perform the cognitive functions such as continuous learning using Large Language Modelling (LLM), interacting, reasoning, perceiving and even creativity. These are usually associated with human mind. Well-defined and process-focused systems are instrumental in ensuring that software in the rapidly growing field of AI delivers high quality and reliability. Any software quality assurance framework is a collection of experiences, learnings and methods. Another important aspect of any quality assurance framework is the availability of accurate and relevant data from all development and support activities.

AI transforming software quality assurance

With the increasing maturity and prevalence of AI and machine learning in software applications, the importance of quality assurance practices becomes more apparent. In the realm of AI, software quality assurance necessitates specific considerations and adherence to leading practices to help ensure the accuracy and reliability of system performance and predictions.

The incorporation of AI into the software quality assurance framework presents exciting possibilities, as it has the potential to be impactful across several key activities.

 

  • Enhances methods: AI can generate leading practices that teams can use regularly as part of project delivery process. The accuracy of the solution provided by AI increases with the precision of the data input into the system. These leadingpractices can be fed-back into the organizational methods repository so that it remains current and relevant.
  • Converting lag to lead indicators:  In project delivery, most metrics are lag indicators, meaning they provide information after an event has already occurred. However, AI has the potential to predict issues earlier and provide insights into future events, such as anticipated costs, bug detection trends, team motivation levels and more. By integrating multiple indicators, AI can derive a risk factor for the project delivery process, potentially enabling better planning and decision-making for more successful outcomes. Hence, converting lag indicators to lead indicators.
  • Governance strengthening: AI systems have the capability to enhance leadership governance of project delivery by offering more precise and accurate status updates, metrics and risk assessments. Through automated AI structures, project monitoring can be strengthened, as these systems can detect projects that may be in trouble and provide early warning mechanisms.
  • From transactions to impact: AI systems can alleviate quality analysts from monotonous transactional tasks such as process audits and metric reviews, which often lead to reduced attention to detail and potential errors in decision-making. By automating these activities, AI allows quality experts to focus on higher-value tasks and collaborate with project teams to continuously improve processes, enhancing the perceived impact of quality teams on project delivery.
  • Effective on-the-job training: AI systems can enhance on-the-job training by actively alerting team members when they are about to make an error, leading to more accurate training and reducing the chances of introducing errors during project delivery.
  • Empowering best practice evangelization: In today’s world, quality teams often stumble upon leading practices during their interactions with software development teams, with little analysis of their impact. AI systems have the capability to analyze the impact of these leading practices and encourage their adoption by software development teams, thus validating their effectiveness and making them better.

In conclusion, the potential for leveraging AI in software quality assurance frameworks is significant. However, the readiness of our quality teams to embrace and explore this technology remains the crucial question. It is imperative for quality teams to be prepared and open-minded to fully tap into the benefits that AI can bring to software quality assurance processes.

 

Author: Srinivasa Nagaraja, Client Technology Quality and CMMI leader, EY Global Delivery Services LLP.

Disclaimer: This Publication contains information in summary form and is therefore intended for general guidance only. It is not intended to be a substitute for detailed research or the exercise of professional judgment. Member firms of the global EY organization cannot accept responsibility for loss to any person relying on this article.


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