Défis pour la variabilité et la traçabilité des exigences en ingénierie système

National audienceMajor industrial projects are facing an important size of their requirements documents, often based on an implicit normative or legislative context. Managed through a document-centric approach, they are facing two challenges: variability and traceability of their requirements at both design time and runtime. In the paper, we identify vectors of variability and propose model-driven engineering as a solution to tame this normative context and address variability and traceability concerns in an industrial context with safety concerns.Les grands projets industriels font face à une volumétrie importante des exigences, souvent contraintes par un cadre réglementaire ou législatif important mais implicite. Ces projets sont menés via des approches centrées documents et connaissent une variabilité importante de leurs exigences tant à la conception que pendant l'exploitation. Après avoir identifié un certain nombre de facteurs de variabilité, nous nous positionnons pour une approche dirigée par les modèles pour expliciter ce contexte réglementaire et adresser la variabilité et la traçabilité des exigences dans un contexte industriel et sûreté de fonctionnement

Toward Multilevel Textual Requirements Traceability Using Model-Driven Engineering and Information Retrieval

International audienceIn complex industrial projects, textual information remains the main vector of information at the project level. Consequently, requirements are scattered throughout multiple documents expressing different levels of requirements and different kinds of requirements. Formalizing this information and tracing different relationships among documents and organizing this environment present a challenging question. Domain-specific modeling and traceability modeling are Model-Driven Engineering (MDE) techniques that could address various aspects of requirements formalization. Text-based high level requirements can be formalized as document concepts can be gathered and represented. Still, relationships cannot always be determined using sole MDE approaches and, as a consequence, relationships and traceability issue remains. Information retrieval (IR) approaches have already proved to work in an efficient way on large text corpora for requirements traceability analysis but do only consider similarity aspects of flatten documents, losing their organization and hierarchy. This paper aims to introduce how a combined use of both MDE and IR can lead to improved requirements organization and traceability while handling textual ambiguous requirements documents.Dans les projets industriels complexes, le texte reste le principal vecteur d'information au niveau du projet. Les exigences sur le futur système sont ainsi disséminées à travers de nombreux documents qui expriment différents niveaux d'exigences et différentes sortes d'exigences. Formaliser cette information et tracer les relations entre ces documents et organiser un un tel environnement représente un défi important. La modélisation d'un domaine ou de la traçabilité dans ce domaine sont des contributions de l'ingénierie dirigée par les modèles (IDM) pour formaliser de tels ensembles. Des exigences textuelles de haut niveau peuvent être formalisées tandis que les documents qui les contiennent peuvent être importés et analysés. Cependant, les relations de traçabilité, elles, ne peuvent pas forcément être acquises à travers ces seules techniques provenant de l'IDM. D'un autre côté, les techniques de recherche d'information (RI) ont déjà démontré leur efficacité sur de larges collections de textes pour la traçabilité des exigences mais ne considèrent que des versions "planes" des documents, perdant toute la hiérarchie et l'organisation de ceux ci. Cet article vise à introduire l'utilisation combiné de l'IDM et de la RI afin d'améliorer les aspects structurels et traçabilité de ces collections de documents d'exigences

Defining and Retrieving Themes in Nuclear Regulations

International audienceSafety systems in nuclear industry must conform to an increasing set of regulatory requirements. These requirements are scattered throughout multiple documents expressing different levels of requirements or different kinds of requirements. Consequently, when licensees want to extract the set of regulations related to a specific concern, they lack explicit traces between all regulation documents and mostly get lost while attempting to compare two different regulatory corpora. This paper presents the regulatory landscape in the context of digital Instrumentation and Command systems in nuclear power plants. To cope with this complexity, we define and discuss challenges toward an approach based on information retrieval techniques to first narrow the regulatory research space into themes and then assist the recovery of these traceability links

An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts

Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4% and recall scores of 94.9% and 92.4%

Model-Based Simulation of Legal Policies: Framework, Tool Support, and Validation

Simulation of legal policies is an important decision-support tool in domains such as taxation. The primary goal of legal policy simulation is predicting how changes in the law affect measures of interest, e.g., revenue. Legal policy simulation is currently implemented using a combination of spreadsheets and software code. Such a direct implementation poses a validation challenge. In particular, legal experts often lack the necessary software background to review complex spreadsheets and code. Consequently, these experts currently have no reliable means to check the correctness of simulations against the requirements envisaged by the law. A further challenge is that representative data for simulation may be unavailable, thus necessitating a data generator. A hard-coded generator is difficult to build and validate. We develop a framework for legal policy simulation that is aimed at addressing the challenges above. The framework uses models for specifying both legal policies and the probabilistic characteristics of the underlying population. We devise an automated algorithm for simulation data generation. We evaluate our framework through a case study on Luxembourg’s Tax Law

On Product Comparison Matrices and Variability Models from a Product Comparison/Configuration Perspective

National audienceComparators and configurators have now become common in our daily activities and are usually based on Product Comparison Matrices (PCMs) to present and compare features. Based on a previous analysis of 300+ PCMs from Wikipedia, we identify the limits of existing comparators, configurators and PCMs. Variability Models (VMs) have been extensively used through the last 20 years to provide a synthetic and formal way to represent a product line. As a consequence, using VMs instead of PCMs could tackle these limits and improve comparison and configuration activities. In this paper, we present 5 research questions that focus on using VMs to represent PCMs and their applications for comparators and configurators.Les comparateurs et configurateurs de produits sont devenus des objets du quotidien et sont souvent représentés sous la forme de tableaux. L'analyse de 300+ tableaux de comparaison issus de Wikipedia a montré les limites de ceux-ci, en plus de celles des comparateurs et configurateurs. Les modèles de variabilité (MV) proposent une vue formelle et synthétique d'une ligne de produits. La formalisation de MVs à partir de matrices de comparaison permettrait d'aller au delà de ces limites et de proposer des outils de comparaison et de configuration plus avancés. Dans cet article, nous proposons 5 questions de recherche autour de l'utilisation de MVs pour la formalisation de matrices de comparaison et leur utilisation dans le cadre de comparateurs et configurateurs

Automated Extraction of Semantic Legal Metadata Using Natural Language Processing

[Context] Semantic legal metadata provides information that helps with understanding and interpreting the meaning of legal provisions. Such metadata is important for the systematic analysis of legal requirements. [Objectives] Our work is motivated by two observations: (1) The existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis. (2) Automated support for the extraction of semantic legal metadata is scarce, and further does not exploit the full potential of natural language processing (NLP). Our objective is to take steps toward addressing these limitations. [Methods] We review and reconcile the semantic legal metadata types proposed in RE. Subsequently, we conduct a qualitative study aimed at investigating how the identified metadata types can be extracted automatically. [Results and Conclusions] We propose (1) a harmonized conceptual model for the semantic metadata types pertinent to legal requirements analysis, and (2) automated extraction rules for these metadata types based on NLP. We evaluate the extraction rules through a case study. Our results indicate that the rules generate metadata annotations with high accuracy

An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts

Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97,2% and 82,4%, and recall scores of 94,9% and 92,4%

A Query System for Extracting Requirements-related Information from Legal Texts

Searching legal texts for relevant information is a complex and expensive activity. The search solutions offered by present-day legal portals are targeted primarily at legal professionals. These solutions are not adequate for requirements analysts whose objective is to extract domain knowledge including stakeholders, rights and duties, and business processes that are relevant to legal requirements. Semantic Web technologies now enable smart search capabilities and can be exploited to help requirements analysts in elaborating legal requirements. In our previous work, we developed an automated framework for extracting semantic metadata from legal texts. In this paper, we investigate the use of our metadata extraction framework as an enabler for smart legal search with a focus on requirements engineering activities. We report on our industrial experience helping the Government of Luxembourg provide an advanced search facility over Luxembourg’s Income Tax Law. The experience shows that semantic legal metadata can be successfully exploited for answering requirements engineering-related legal queries. Our results also suggest that our conceptualization of semantic legal metadata can be further improved with new information elements and relations