Liebe Freunde und Freundinnen, liebe Partner*innen von Qualicen, das Jahr 2021 neigt sich langsam dem Ende. Hier kommt nun noch rechtzeitig vor den Feiertagen unser neuer Snapshot. Mit diesem Format sind wir im Dezember 2020 gestartet und wollen Sie zweimal im Jahr
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
We are proud to announce the latest release of our automated quality analysis tool: Scout 5.4-1. In this release we incorporated many new features, and improved existing ones. In this blogpost we provide a brief overview of the most
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.
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!
How we investigated whether our Qualicen Scout is a useful tool for companies in the domains of software and systems engineering.
Why we wanted to answer this questionAs science showed, the quality of the requirements documentation influences the subsequent activities of the software engineering process. Detecting errors late in a software engineering process leads to very expensive changes of parts of every pre-executed activity. Accordingly, we at Qualicen help our customers to assure the quality of requirements specifications before they are used in other activities.
When we look at requirements documents that are new to us, we often need some help on terms and abbreviations. Creating a glossary to explain these imporant domain terms and abbreviations is a fine idea. It helps new team members to get going, improves the readability of a requirements specification and helps to avoid misunderstandings. The main problem with glossaries is that we create them once and update them only rarely. In consequence, the majority of glossaries are not particulary useful. In this article, Qualicen consultant Maximilian Junker shows how you can get more out of your glossary and keep it always up-to-date.