Qualicen Snapshot – Edition 1/2021 [in German]
Liebe Freunde und Freundinnen, liebe Partner*innen von Qualicen, es ist soweit: die zweite Ausgabe unseres Snapshots - die erste im Jahr 2021 - ist fertig. Wir sind im Dezember 2020 mit diesem neuen Format gestartet und möchten uns zuerst noch einmal ganz
Mature Enough? Using Maturity Models to Assess and Improve Software Projects
It is not rare that projects approach us not as a consultant firm but more like a medical first-aid station. In those cases, the patient represents a software or engineering project that is hanging on by a bare thread. So
Presenting to You: Qualicen Snapshot [in German]
Liebe Freund*innen und Partner*nnen von Qualicen, Langsam schaffen wir es nicht mehr uns mit allen Kontakten regelmäßig persönlich auszutauschen. Und manche/r scheut vielleicht die Hürde uns einfach mal auf die neuesten Entwicklungen anzusprechen. Und auch nicht jede/r hat die Zeit unseren Updates
Hey BERT, do you recognize any requirements?
Does scanning for your relevant requirements in massive documents sound familiar? In this blog post we are going to find them for you
Introducing Pipit – The User Story Validator for Jira
User stories and acceptance criterias are the backbone of agile development. Everyone knows badly written user stories provide little value. In extreme cases, they even do more harm than good. Therefore, many best practices and templates exist guiding us to
How-to Requirement Reviews
Reviews! Either the safety net you always wanted or the hammer that knocks your teeth out. In this blog post we will explain how to conduct a valuable, effective and pleasant requirement review.
Improving the German Corona Warn App Specification
Last tuesday, the german Corona Warn App officially launched, following intense and heated discussions about the data protection standards such an app should adhere to. In order to achieve transparency about the inner workings of the app, including which data
The Incredible Potential of Text Analytics – The Use Cases Explained.
New advances in text analytics make the tech news nearly every week, most prominently IBM Watson, but also more recently AI approaches such as ELMo or BERT. And now it made world news with the pandemic caused by the Covid-19 virus, with the white house requesting help via NLP.
Text Analytics and Natural Language Processing (NLP) deal with all types of automatic processing of texts and is often built on top of machine learning or artificial intelligence approaches. The idea of this article is not to explain how text analytics works, but instead to explain what is possible.
Release of Scout Version 4.6 (LTS)
If you ever had quality defects in your requirements-suite or test-suite, you know how time-consuming and expensive they can become. However, due to the sheer size of requirements-suites and test-suites, assessing the quality of the contained artifacts is almost impossible. So, is there no way out of this mess, or do you have to stick deep in this yogurt? There is help: The automated requirements and test analysis tool Scout by Qualicen comes now in a new and improved version!
A Gentle Introduction Of A Requirement Syntax
Requirement documentation is mainly done in either Natural Language (NL) or in formal models like UML or SysML. NL offers the lowest learning curve and the most flexibility, which for many companies means: "Everyone can start writing requirements without formal training".
In contrast, formal modelling languages require a considerable effort to learn and are very restrictive. But, the flexibility of NL comes with ambiguity and inconsistency. These are two major downsides that formal modeling languages aim to eliminate.
Our customers often ask: "Is there something in the middle, keeping the benefits of NL, but reducing the downsides?" our answer: "Yes, a requirement syntax".
But what has that children's puzzle to do with writing requirements?