Natural Language Requirements Processing: A 4D Vision

The future evolution of the application of natural language processing technologies in requirements engineering can be viewed from four dimensions: discipline, dynamism, domain knowledge, and datasets

Requirement mining for model-based product design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

Requirement Mining for Model-Based Product Design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

Requirement mining for model-based product design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

Requirement Mining for Model-Based Product Design

PLM software applications should enable engineers to develop and manage requirements throughout the product’s lifecycle. However, PLM activities of the beginning-of-life and end-of-life of a product mainly deal with a fastidious document-based approach. Indeed, requirements are scattered in many different prescriptive documents (reports, specifications, standards, regulations, etc.) that make the feeding of a requirements management tool laborious. Our contribution is two-fold. First, we propose a natural language processing (NLP) pipeline to extract requirements from prescriptive documents. Second, we show how machine learning techniques can be used to develop a text classifier that will automatically classify requirements into disciplines. Both contributions support companies willing to feed a requirements management tool from prescriptive documents. The NLP experiment shows an average precision of 0.86 and an average recall of 0.95, whereas the SVM requirements classifier outperforms that of naive Bayes with a 76% accuracy rate

Klassifikation von Anforderungen und Informationen zur Unterstützung von Qualitätssicherungsprozessen

Anforderungsdokumente werden im Anforderungsmanagement verwendet, um Eigenschaften und Verhalten von Systemen zu dokumentieren. In der Automobilindustrie werden diese Dokumente verwendet, um die von Zulieferern zu fertigenden Komponenten zu beschreiben und um für die Kommunikation zwischen Zulieferer und Konzern eine rechtliche Grundlage zu schaffen. Daher müssen diese Dokumente diversen Qualitätsstandards und Qualitätsrichtlinien entsprechen. In manuellen Reviews werden Anforderungsdokumente gegen diese Richtlinien geprüft. Eine Richtlinie besagt, dass in Anforderungsdokumenten eine klare Trennung zwischen rechtlich verbindlichen Anforderungen und sogenannten Zusatzinformationen (Abbildungen, Erläuterungen, Beispiele, Verweise, etc.) existieren muss. Dazu wird jedes Objekt entsprechend dem Inhalt mit einem Objekttyp annotiert. Die Überprüfung der Korrektheit des Objekttyps ist ein zeitaufwändiger und fehleranfälliger Prozess, da Anforderungsdokumente in der Regel mehrere tausend Objekte umfassen. In dieser Arbeit wird am Beispiel des Reviews des Objekttyps untersucht, ob und in welcher Art und Weise der Reviewprozess durch den Einsatz von maschinellem Lernen unterstützt werden kann. Dazu wird zuerst ein Klassifikator trainiert, der in der Lage ist, zwischen Anforderungen und Zusatzinformationen zu unterscheiden. Ein darauf basierendes Werkzeug ist in der Lage, Anwender bei der Überprüfung des Objekttyps durch Hinweise und Warnungen zu unterstützten. In empirischen Studien wird untersucht, ob Anwender durch den Einsatz des Werkzeugs das Review von Anforderungsdokumenten besser durchführen können. Die Ergebnisse zeigen, dass Anwender nicht nur mehr falsch klassifizierte Objekte finden, sondern auch durchschnittlich 60% der für das Review verwendeten Zeit einsparen können. Durch die Übertragung des Ansatzes auf ein weiteres Klassifikationsproblem wird zudem gezeigt, dass der Einsatz von Werkzeugen nicht nur auf den Anwendungsfall Objekttypklassifikation beschränkt ist, sondern potenziell auf viele weitere zu überprüfende Richtlinien übertragbar ist

ソフトウェア要求仕様書の品質モデル設計方法論と自動車ソフトウェアシステムへの応用に関する研究

南山大学201

(Semi-)Automatic Categorization of Natural Language Requirements

Context and motivation: Requirements of todays industry specifications need to be categorized for multiple reasons, including analysis of certain requirement types (like non-functional requirements) and identification of dependencies among requirements.This is a pre-requisite for effective communication and prioritization of requirements in industry-size specifications. Question/problem: Because of the size and complexity of these specifications, categorization tasks must be specifically supported in order to minimize manual efforts and to ensure a high classification accuracy. Approaches that make use of (supervised) automatic classification algorithms have to deal with the problem to provide enough training data with excellent quality. Principal ideas/results: In this paper, we discuss the requirements engineering team and their requirements management tool as a socio-technical system that allows consistent classification of requirements with a focus on organizational learning. We compare a manual, a semi-automatic, and a fully-automatic approach for the classification of requirements in this environment. We evaluate performance of these approaches by measuring effort and accuracy of automatic classification recommendations and combined performance of user and tool, and capturing the opinion of the expert-participants in a questionnaire. Our results show that a semi-automatic approach is most promising, as it offers the best ratio of quality and effort and the best learning performance. Contribution: Our contribution is the definition of a socio-technical system for requirements classification and its evaluation in an industrial setting at Mercedes-Benz with a team of ten practitioners