What is Easy Approach to Requirements Syntax?
The Easy Approach to Requirements Syntax (EARS) is a mechanism to gently constrain textual requirements. The EARS patterns provide structured guidance that enable authors to write high quality textual requirements. There is a set syntax (structure), with an underlying ruleset. A small number of keywords are used to denote the different clauses of an EARS requirement. The clauses are always in the same order, following temporal logic. The syntax and the keywords closely match common usage of English and are therefore intuitive.
Unstructured natural language (NL) remains the most common format for writing system requirements. However, this approach often leads to ambiguity and misinterpretation—especially when requirements authors lack formal training in best practices. As systems evolve, these unclear requirements cascade through development layers, increasing volatility, introducing risk, and driving up both timelines and costs.
The Easy Approach to Requirements Syntax (EARS) addresses these challenges by offering a streamlined, effective framework for writing precise, consistent requirements. EARS is particularly beneficial for global teams, including those for whom English is not a first language. Widely adopted by practitioners across industries, EARS stands out for its simplicity: it requires minimal training, no specialized tooling, and produces clear, readable requirements that reduce rework and accelerate delivery.
How Do I Analyze Requirements using Inflectra.ai?
From the requirement details page you can analyze the requirement to see how well it is written and organized, based off specific frameworks. This provides valuable insights into the quality of the text and how effectively it may communicate its meaning to others. Currently we only support EARS, but we plan on supporting additional frameworks as well as "plain English"
The analysis includes a score from 1 to 5. A score of 5 means the requirement is very well written and does not need to be improved, while a score of 1 means lots of work is needed. Along with a score, detailed notes and guidance are provided about how to improve the requirement and why, as well as what is in good shape already. The EARS framework typically recommends you follow the "When [optional trigger], while [optional pre-conditions], the [system name] shall [system response]" pattern. Its value lies in reducing ambiguity and errors in requirements specifications, leading to more efficient development and testing, and ultimately, higher quality systems, because its precise structure forces clarity and helps prevent misinterpretations.
For example, lets start with the following requirement:
We can now use the Inflectra.ai sidebar to analyze this requirement:
When we choose this option, we will see the following displayed:
You can see in this case, we have a well written requirement that meets the best practices of EARS. This is not surprising as we took the example from this great guide to EARS.
If we now try another example:
When we choose the option to Analyze the requirement, this time we receive the following feedback:
You can see that we received a partial score (3/5) and comments on what we did well and what we did badly.
As a final example:
I have written this next example in a standard user story format. Although user stories are a very common way to write requirements in an agile project, they don't really provide the level of precision that EARS is expecting. Unsurprisingly we get a very low score (1):
So in this case, we could either completely rewrite the user story to follow the EARS syntax, or more simply add a more detailed EARS formatted description and keep the name the same. This is possible because the Inflectra.ai analysis feature evaluates both the name and description of the requirement. In these examples we only had a name, but you could combine the two:
With this change we now get a much higher score (4/5):
What's Planned Next for This Feature?
We hope you are excited about this new Inflectra. ai feature as we are. The obvious question is, how can I use AI to help me improve the requirement based on the suggestions? Well don't worry, the next planned update to Inflectra.ai will be add a new AgenticAI feature that takes the recommendations from the current functionality and actually rewrites the requirement for you.
For those who want more control over the requirements change process, we will also be adding these additional productivity enhancements:
- The ability to take the recommendations and turn that into a formal recommendation document, stored in the Spira document management system, and linked to this requirement.
- The option to take the recommendations and create discrete project management tasks to review and implement.