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!
Have you ever read a text and suddenly felt like you had a déjà vu? Maybe this happened because you came across a sentence that was very similar to one that you already read before. We call this semantic duplicates. Semantic duplicates can happen because we think one specific instruction is so important that we simply have to repeat it. But often semantic duplicates arise from simply copy-pasting text. First, semantic duplicates can lead to inconsistency within the requirements. In detail, if there are two similar sentences that explain the same requirement, the same requirement can be interpreted in two different ways. Second, if the sentences are not just similar, but rather a copy of each other, it makes the copy simply superfluous. However, semantic duplicates are redundant, which is why we decided to tackle this problem.
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.
Falls Ihr/Sie nächste Woche in München seid, kann man fast gar nicht über die REConf 2018 laufen ohne uns zu begegnen:
Keynote: "Vom Design Thinking zum Requirements Engineering: Vom Warum und Wieso zum Was und Wie"Prof. Dr. h.c. Manfred Broy wird am Dienstag um 9 Uhr die Eröffnungs-Keynote zum Thema Design Thinking und RE halten: Requirements Engineering ist vielleicht der wichtigste Teil der Software-Evolution. Falls es uns nicht gelingt, die Funktionalität, die der Endnutzer benötigt, korrekt zu spezifizieren, und falls es uns nicht gelingt, die geforderte Qualität korrekt zu identifizieren, besteht die Gefahr, dass ein System entwickelt wird, das nur teilweise oder vielleicht sogar völlig nutzlos ist. Im Prinzip gibt es zwei wichtige Schritte im Prozess des Requirements Engineerings. Die größte Herausforderung ist, die benötigte Funktionalität zu finden. Das ist eine schwierige Aufgabe und Techniken wie Design Thinking können hier helfen. Design Thinking ist ganz darauf ausgerichtet, Lösungen für Probleme zu finden und diese durch die Konstruktion eines Prototyps konkret zu machen. Dies ist ein kreativer Prozess, um Ideen zu entwickeln, wie die richtige Funktionalität eines Softwaresystems ausschauen könnte. Jedoch, wenn ein Prototyp vorliegt, ist man noch weit entfernt davon, einen guten Satz von Anforderungen zu besitzen. Deshalb ist es notwendig, eine Brücke zu finden von den Resultaten des Design Thinking-Prozesses zum Requirements Engineering, um alle Details einer Anforderungsspezifikation auszuarbeiten. Dieser Prozess ist beeinflusst von dem gewählten Entwicklungsmodell, wie etwa agiles oder konventionelles dokumentationsorientiertes Vorgehen. Design Thinking und Requirements Engineering ergänzen sich perfekt, um die kreative Identifikation der Funktionalität und der detaillierten Beschreibung der Funktionen, aber auch der Qualität von softwareintensiven Systemen sicher zu stellen.
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.
Several roles are concerned with requirements quality. Of course, there is the requirements author, writing the requirements. But there is also the reviewer, who proof-reads and validates the requirements. And finally, there is the QA-Engineer, responsible for the overall quality of all artifacts created during the engineering process. Each of these roles needs a different view on requirements and different tools in order to do their work efficiently and achieve a high requirements quality. In this article I am going to show you how the Qualicen products specifically support authors, reviewers and QA-engineers in their work to keep requirements quality high.
I've worked quite some time on understanding and detecting quality defects in requirements documents and requirements quality in general. All the time, I was very dissatisfied with the current state in both research and practice on this topic. I think, the problem behind this is that there is no guidance: In times of rapid change and delivery, where every project looks different, we still have no good rule of what a good requirements document is. A few years ago, we came up with such a rule, and tried it in various applications. And - so far - it seems to work! We've collected this experience and are now ready to tell you about it, because we really believe this should change how you view requirements engineering, and this should change what you consider good requirements documents.
For high requirements quality we need quality assurance. In a previous post, I explained why automatic methods cannot replace manual methods. Instead I suggested to combine both worlds. And the ugly truth is, in both system testing and requirements engineering, we need both manual and automatic quality assurance to control requirements quality and test quality. Now you wonder, how? I got you covered. In this brief post, I want to point out how you can combine the two worlds and how you benefit from the combination.