Today’s software and systems engineers face unprecedented challenges amidst rising complexity in product development. Following the recent launch of Requirements Quality Assistant (RQA) from IBM Engineering, systems engineers and C-level executives from across multiple industries now share insights about how they weave artificial intelligence (AI) into their engineering processes.
75 percent of development projects fail due to poor requirements. AI can help.
IBM helps its customers embrace the necessary and inevitable digital transformation with the deployment of RQA within their Requirements Management solution. RQA uses Watson AI to help engineers improve the quality of their requirements, in real time. This capability allows engineers to find detailed answers to highly specific questions, even across globally distributed teams. By applying AI and using tools like Watson Natural Language Understanding to leverage machine and deep learning, companies that are losing senior engineers to retirement can effectively translate expertise and industry knowledge to more junior engineers.
Requirements Quality Assistant helps customers achieve noticeable efficiencies:
- Tens of thousands of documents can be analyzed to train Watson, a feat that would take many years for a human to achieve
- 30+ years of practical engineer experience at the fingertips of all employees with the pre-trained, built-in Watson capability
- Reduction of 75 percent in time spent by employees who are searching for expert knowledge
Watch the video to learn more about Requirements Quality Assistant
Why Requirements Quality Assistant? Why now?
Traditional methods of writing and analyzing requirements leaves teams exposed to error and costly re-work. It’s no longer optional to rely on document-based tools which will never provide a real-time single source of truth. Peer reviews, checklists and rules engines don’t always cut it, especially working across global teams. Leveraging AI to improve requirements quality helps companies improve their time to market and increase market share by releasing new features before the competition.
RQA is pre-trained to detect key quality indicators that are consistent with the INCOSE Guidelines for Writing Good Requirements. The solution uses AI to help engineers improve completeness, consistency and accuracy of their requirements. Teams can remove ambiguity and reliably articulate objectives to stakeholders.
When we inject intelligence into the requirements writing phase of your projects, software and systems engineers can:
Reduce errors: The requirements analysis phase takes up only 2 percent of total design time. But poor requirements account for more than half of all engineering errors.
Reduce costs: The cost of correcting errors increases exponentially as a project progresses. Decrease product development costs and delays by catching errors early and reducing rework.
Strengthen requirements: Isolate requirement issues before they are sent for manual human review. Receive suggestions for improvement based upon a score provided by Watson.
Defects in a launched product cost up to 200X more to correct than defects found during requirements.*
IBM Engineering recently introduced AI into our Requirements Management solution, a first step in broadening capabilities across the rest of the Engineering Lifecycle Management portfolio. Modeling, test and workflow management solutions are the next to be infused with AI. RQA is currently available for users of IBM Engineering Requirements Management DOORS Next, and will soon be available for customers using DOORS on premise.
This offering comes as IBM continues to modernize and integrate ELM tools to provide customers with a complete, end-to-end lifecycle approach to systems engineering. In a recent report from Ovum, IBM was named a leader in Engineering Lifecycle Management, with high marks for its Requirements Management solution. Learn more about IBM’s commitment to helping its customers embrace digital transformation in engineering.
*IEEE Transactions on Software Engineering