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Requirements Semiant Skill

Check Requirements Quality with a Single Click

A key use case in product development is the authoring and reviewing of requirements. The quality of the requirements matters: Incorrect or incomplete requirements can generate issues down the line can be expensive, create delays and even lead to product recalls.

For this reason, some requirements management tools include AI-based requirements analysis. But there are two problems with this: First, those checkers only work with exactly one requirements tool – the one that they are built in. And second, they are not customizable, which means that they cannot take organizational authoring rules into account.

Semiant now provides a requirements quality assistant that takes both issues into account

We partnered up with Qualicen, a leader in AI-based requirements analysis with natural language processing (NLP). We integrated Holmes, their requirements analysis engine, as a new skill into Semiant. This means that you can perform requirements quality analysis with a single click anywhere: In your favorite requirements management tool or when analyzing your competitor’s datasheet.

Would you like to see the Holmes quality check in action? Then contact us for a demonstration

Using the quality check is quite intuitive. You can see Holmes in the following screenshot (assuming that the Holmes has been enabled):

Holmes Requirements Quality Check is Now a Semiant Skill

Upon activating Semiant via the browser extension icon, you will see the familiar sidepane. But now it features tabs: You can see in the screenshot that the glossary manager is still available on a different tab.

The sidepane shows statistics on the requirements quality of the active specification. In the screenshot, you see a specification in Jama Connect.

You can dive into specific problems, as Semiant highlights issues directly in the text. The screenshot shows the word “many”, identified as a weak word. Hovering over the word will produce a tooltip with additional information on the problem and guidance on how to fix it.

Speed up Reviews, Unburden Your Team and Improve Requirements Quality

Semiant and Holmes together are easy to use and provide immediate value. Therefore, your team will start using it right away. This leads to higher quality requirements, wich in turn leads to faster and better reviews – and less rework.

Even better, Holmes works everywhere (as long as it’s in a web browser). You are therefore not limited to a quality check in Jama: You can check quality in Confluence, Polarion, gitHub or on a Wiki.

Holmes is highly customizable. Therefore, we can tailor it to your organization’s needs.

What’s that smell?

Like software code, requirements can become “smelly” over time. This means that over time, they get outdated, inconsistent, badly structures, etc. Holmes consists of modular smell checkers. As part of the tailoring, we would enable and configure those smell checkers that matter to you and your organization.

List of Smell checkers for analyzing requirements quality
List of Homes smell checkers for analyzing requirements quality

In addition, we can customize Holmes for your organization’s authoring rules. Consistency of texts makes it easier to read them and to spot problems early, thereby saving time and reducing the need for rework.

Can Holmes help your team?

If you think that Semiant, together with the Holmes requirements analysis could add value to your team, then let’s have a conversation.

Image Source: Qualicen

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Partnerships Requirements

Semiant partnering with Qualicen

The quality of product development is of strategic importance for many companies because problems can lead to delays, costly recalls or even personal injury. Increasing complexity and stricter compliance requirements are exacerbating the situation. Traditional approaches are reaching their limits and jeopardizing economic success. What to do?

The use of AI solutions in product development is already showing promising results. And one or the other reader has certainly followed my activities in the field of AI. I have been working on a virtual quality assistant under the name Semiant for about a year. I have been following the activities of the Qualicen company for a while. Now we have decided to continue on this path together.

With Semiant and Michael Jastram, we are gaining a strong partner who understands our customers and will play a key role in shaping the vision of Holmes in order to create the greatest possible benefit for our users

Dr. Sebastian Eder, General Manager Qualicen GmbH

Under the name Holmes , Qualicen is working on a platform that uses artificial intelligence to relieve teams in product development. Holmes also uses Natural Language Processing (NLP) to process human-written content. What both solutions have in common is that they can be used to automate many important but monotonous and error-prone tasks for which employees are often overqualified. For product development, this means more efficient work, fewer risks and a lot of time saved.

Customer Benefit

For Semiant customers and those of Qualicen, not much will change at first. Together, however, we can act faster with the enlarged team. In the medium term, we want to develop a product with Holmes that can be used immediately without much adjustment and delivers measurable results. Which use cases we’ll tackle first isn’t clear yet, but we have a lot of ideas.

Image Source: Unsplash

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Requirements

How to Turn a Requirement From Black Box to Glass Box

Requirements Engineering is a key discipline in product development. They are an enabler for communication. Customers and Suppliers use requirements to capture a common understanding on what needs to be done.

The space flight industry was pioneering this in the 1960s, when they started to practice systems engineering. This worked exceptionally well and put people on the moon, even though engineers were working only on paper.

From Documents to Requirements Items

With the advent of computers, engineers started to look for ways to boost efficiency. And they found something else: Word and Excel, the most popular tools for requirements engineering. Whether these are really better than paper is another question…

Unbelievable: Word and Excel are still the most popular tools for capturing requirements

For those who can afford it, more powerful tools are around. Telelogic pioneered requirements engineering tools in the 1990s with DOORS (acquired by IBM ever since). DOORS is still around, other popular requirements engineering tools include Polarion (Siemens), Jama Connect (Jama Software), Codebeamer (Intland) and Integrity (PTC).

All these tools work in a similar fashion: They allow the definition of “Item Types”: These are entities with properties. A “User Need” item type, for instance, may have properties for “Name”, “Description” and “Priority”, in addition to a system-generated unique ID.

Example of a simple traceability (Source: Jama Connect)

Items can be connected through traceability. This supports activities like coverage or impact analysis and supports change management.

A Requirement is a Black Box

But all these tools have one thing in common: The actual content almost always resides in a rich text field. In DOORS, this is the “Object Text”, in Jama it is “Description”. Users can write anything they want into these fields.

Engineers like this, because they are not constraints in what they write. They can even include pictures. But the flip side is that engineers have to look at the actual content a lot. The tools help engineers to decide when to look at the content, but not why. To the tool, the requirement is a black box.

A simple example: For a web system, the stakeholder specifies a timeout of 10 minutes. A test engineer reads the requirement and writes a test case. For instance, the tester shall wait for a little less than 10 minutes and make sure the user is still logged in.

If the stakeholder changes the timeout to 20 minutes, then the test is making a potential problem: The test will still pass, but it would not test the revised requirement! A typical requirements engineering tool would mark the test as suspect, but a human has to look at it.

Is MBSE the Answer?

Model Based Systems Engineering (MBSE) uses a formal modeling notation to capture requirements. Depending on the notation, MBSE may in fact solve this.

Notations like SysML create transparency, but are so difficult to use that many stakeholders will reject them and few stakeholders will actually master them

There is just one catch: The notations used are difficult. The most popular notation is SysML, consisting of dozens of diagram types and hundreds of element types.

A few SysML diagrams (Source: Wikimedia)

The Solution: Let AI Read Your Requirements

Fortunately, there is another options. Natural Language Processing (NLP) is now so powerful that systems can process the requirements text and do useful things with it. NLP is a subfield of artificial intelligence (AI) concerned with the interactions between computers and human language.

To pick up the example above where the timeout had changed. An NLP assistant like Semiant would recognize that two items connected by a trace (requirement and test) share a parameter (10 minutes). After the change of the requirement, Semiant could alert the engineer to the mismatch (10 minutes vs. 20 minutes) and even offer to change either parameter to the value chosen by the engineer.

MBSE Without Anybody Noticing

Semient can do this, because it builds a systems model behind the scenes. In other words, Semiant leverages the power of MBSE, without the stakeholders having to learn MBSE.

This combines the best of both worlds: Stakeholder can continue to articulate their requirements the way they are used to: human language. But behind the scenes, Semiant builds and maintains a model that takes advantage of 20 years of MBSE. Power users can take advantage of the model directly, but most stakeholders will simply benefit from the analysis of the model, which Semiant will articulate in natural language.

And this is how Semiant makes black box requirements transparent.

Photo by Esther Jiao on Unsplash