[Polarion ALM] Transforming Requirements Analysis with AI

Over the past year, artificial intelligence has rapidly become a major focus and a prominent topic on our agenda. It is no longer just a buzzword. Natural language-based models are becoming disruptive tools that make AI accessible to everyone.

This has not only fundamentally changed the way we work, but also represents a turning point for software. It is therefore especially important to embrace this development and leverage the potential that AI brings to our daily work, particularly in areas such as requirements management where manual and repetitive tasks can slow down the process.

Let us take a closer look at how AI supports users in dealing with the challenges of requirements assessment and how it helps solve these issues in Polarion ALM.

Complex Requirements, Limited Time

Managing requirements is a critical and time-sensitive process. When suppliers receive new specifications from customers, they face pressure to respond quickly and effectively to stay competitive. However, this is easier said than done.

Engineers must manually read, review, and assess large volumes of stakeholder requirements and customer requests, even though the content may differ only slightly from previously reviewed specifications. The main challenges can be summarized as follows:

• High workload: Repetitive tasks such as manually reviewing and linking requirements demand significant effort and are very time-consuming.

  
  

• Time pressure: While the time to respond to customers is getting shorter, requirements are becoming more complex.

  
  

• Risk of human error: Humans are not machines, and tasks such as requirements analysis are especially prone to mistakes.

  
  

• Lack of experienced professionals: The expertise needed for evaluation is often held by specialists who are becoming increasingly scarce due to demographic shifts.

  
  

Given these challenges, it is clear that handling requirements without automation is no longer an option.

Knowledge-Driven Requirements Analysis with AI

What would take hours for a requirements engineer to complete can be done in seconds by artificial intelligence. There is a logical reason why new stakeholder and customer requirements are handled manually. They are often worded differently, making automation difficult. However, this is exactly where AI can demonstrate its full potential. By utilizing technologies such as large language models (LLMs), AI can read and understand large volumes of text, regardless of how it is phrased or which language it is written in. Without training or costly fine-tuning, it can quickly compare and analyze any type of stakeholder requirement or request at the semantic level.

In practice, this presents a highly valuable use case. A wealth of knowledge is stored in previous requirements projects. It is like a treasure trove of insights waiting to be unlocked with the help of AI. These insights can serve as the foundation for evaluating new stakeholder requirements and requests. By using AI to transfer data into Polarion ALM, users can automatically compare new requirements at the semantic level with previously assessed ones and receive a list of matched requirements along with all related information and references.

This gives users a shortcut to evaluate requirements and update their attributes and links with just a few clicks while maintaining full control over the analysis results.

Polarion ALM and AI – Powerful Collaboration

While Polarion ALM provides a comprehensive solution for managing the entire requirements lifecycle with complete traceability, AI can be seamlessly integrated as an extension.

This allows users to take advantage of AI capabilities within their familiar work environment while adhering to common workflows. In fact, incorporating AI-driven functions not only improves the quality of the requirements management process, but also enhances decision-making and collaboration among project stakeholders.

Evaluating stakeholder requirements with the help of AI is only the beginning. In the near future, AI may support many other steps throughout the lifecycle, such as requirements or software code generation and quality checks. This will require close collaboration between customers and partners to discover additional functionalities that will further advance modern AI-driven requirements management.

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If you would like more details or to learn how to enhance cybersecurity in your enterprise software development, the DevOps Tec. professional consulting team is ready to assist you. Feel free to contact us by email or phone.

How AI can revolutionize requirements evaluation

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