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[Context] In legal requirements elicitation, requirements analysts need to extract obligations from legal texts. However, legal texts often express obligations only indirectly, for example, by attributing a right to the counterpart. This phenomenon has already been described in the Requirements Engineering (RE) literature [1]. [Objectives] We investigate the use of requirements templates for the systematic elicitation of legal requirements. Our work is motivated by two observations: (1) The existing literature does not provide a harmonized view on the requirements templates that are useful for legal RE; (2) Despite the promising recent advancements in natural language processing (NLP), automated support for legal RE through the suggestion of requirements templates has not been achieved yet. Our objective is to take steps toward addressing these limitations. [Methods] We review and reconcile the legal requirement templates proposed in RE. Subsequently, we conduct a qualitative study to define NLP rules for template recommendation. [Results and Conclusions] Our contributions consist of (a) a harmonized list of requirements templates pertinent to legal RE, and (b) rules for the automatic recommendation of such templates. We evaluate our rules through a case study on 400 statements from two legal domains. The results indicate a recall and precision of 82,3% and 79,8%, respectively. We show that introducing some limited interaction with the analyst considerably improves accuracy. Specifically, our human-feedback strategy increases recall by 12% and precision by 10,8%, thus yielding an overall recall of 94,3% and overall precision of 90,6%.

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.

[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.

Using models for expressing legal requirements is now commonplace in Requirements Engineering. Models of legal requirements, on the one hand, facilitate communication between software engineers and legal experts, and on the other hand, provide a basis for systematic and automated analysis. The most prevalent application of legal requirements models is for checking the compliance of software systems with laws and regulations. In this experience paper, we explore a complementary application of legal requirements models, namely simulation. We observe that, in domains such as taxation, the same models that underlie legal compliance analysis bring important added value by enabling simulation. Concretely, this paper reports on the model-based simulation of selected legal requirements (policies) derived from Luxembourg’s Income Tax Law. The simulation scenario considered in the paper is aimed at analyzing the impact of a current tax law reform proposal in Luxembourg. We describe our approach for simulation along with empirical results demonstrating the feasibility and accuracy of the approach. We further present lessons learned from the experience.

With an increase in regulations, it is challenging for organizations to identify relevant regulations and ensure that their business processes comply with legal provisions. Multiple regulations cover the same domain and can interact with, complement or contradict each other. To overcome these challenges, a systematic approach is required. This paper proposes a thorough approach integrating the Eunomos knowledge and document management system with Legal-URN framework, a Requirements Engineering based framework for business process compliance).

Ensuring compliance of software systems with government regulations, policies, and laws is a complex problem. Generally speaking, solutions to the problem first identify rights and obligations defined in the law and then treat these as requirements for the system under design. This work examines the challenge of developing tool support for extracting such requirements from legal documents. To address this challenge, we have developed a tool called GaiusT. The tool is founded on a framework for textual semantic annotation. It semiautomatically generates elements of requirements models, including actors, rights, and obligations. We present the complexities of annotating prescriptive text, the architecture of GaiusT, and the process by which annotation is accomplished. We also present experimental results from two case studies to illustrate the application of the tool and its effectiveness relative to manual efforts. The first case study is based on the US Health Insurance Portability and Accountability Act, while the second analyzes the Italian accessibility law for information technology instruments.