The role of implicit argumentation in nominal SRL

Gerber, Matthew S.; Chai, Joyce; Meyers, Adam

doi:10.3115/1620754.1620776

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…They are different in terms of models they adopt to capture roles. Semafor [45], [48] and Shalmaneser [46] are based on the FrameNet model, while the CogComp NLP pipeline (hereafter CNP [47]) uses the PropBank [49] and NomBank models [50], [51]. To the best of our knowledge, CNP is the only tool under active development, and is thus used in UMTG.…”

Section: Semantic Role Labelingmentioning

confidence: 99%

Automatic generation of system test cases from use case specifications

Wang

Pastore

Göknil

et al. 2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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In safety critical domains, system test cases are often derived from functional requirements in natural language (NL) and traceability between requirements and their corresponding test cases is usually mandatory. The definition of test cases is therefore time-consuming and error prone, especially so given the quickly rising complexity of embedded systems in many critical domains. Though considerable research has been devoted to automatic generation of system test cases from NL requirements, most of the proposed approaches require significant manual intervention or additional, complex behavioral modelling. This significantly hinders their applicability in practice.In this paper, we propose Use Case Modelling for System Tests Generation (UMTG), an approach that automatically generates executable system test cases from use case specifications and a domain model, the latter including a class diagram and constraints. Our rationale and motivation are that, in many environments, including that of our industry partner in the reported case study, both use case specifications and domain modelling are common and accepted practice, whereas behavioural modelling is considered a difficult and expensive exercise if it is to be complete and precise. In order to extract behavioral information from use cases and enable test automation, UMTG employs Natural Language Processing (NLP), a restricted form of use case specifications, and constraint solving.

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Section: Semantic Role Labelingmentioning

confidence: 99%

Automatic generation of system test cases from use case specifications

Wang

Pastore

Göknil

et al. 2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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show abstract

“…They are different in terms of models they adopt to capture roles. Semafor [52], [55] and Shalmaneser [53] are based on the FrameNet model, while the CogComp NLP pipeline (hereafter CNP [54]) uses the PropBank [56] and NomBank models [57], [58]. To the best of our knowledge, CNP is the only tool under active development, and is thus used in UMTG.…”

Section: Semantic Role Labelingmentioning

confidence: 99%

Automatic Generation of Acceptance Test Cases From Use Case Specifications: An NLP-Based Approach

Wang

Pastore

Göknil

et al. 2022

IIEEE Trans. Software Eng.

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Acceptance testing is a validation activity performed to ensure the conformance of software systems with respect to their functional requirements. In safety critical systems, it plays a crucial role since it is enforced by software standards, which mandate that each requirement be validated by such testing in a clearly traceable manner. Test engineers need to identify all the representative test execution scenarios from requirements, determine the runtime conditions that trigger these scenarios, and finally provide the input data that satisfy these conditions. Given that requirements specifications are typically large and often provided in natural language (e.g., use case specifications), the generation of acceptance test cases tends to be expensive and error-prone. In this paper, we present Use Case Modeling for System-level, Acceptance Tests Generation (UMTG), an approach that supports the generation of executable, system-level, acceptance test cases from requirements specifications in natural language, with the goal of reducing the manual effort required to generate test cases and ensuring requirements coverage. More specifically, UMTG automates the generation of acceptance test cases based on use case specifications and a domain model for the system under test, which are commonly produced in many development environments. Unlike existing approaches, it does not impose strong restrictions on the expressiveness of use case specifications. We rely on recent advances in natural language processing to automatically identify test scenarios and to generate formal constraints that capture conditions triggering the execution of the scenarios, thus enabling the generation of test data. In two industrial case studies, UMTG automatically and correctly translated 95% of the use case specification steps into formal constraints required for test data generation; furthermore, it generated test cases that exercise not only all the test scenarios manually implemented by experts, but also some critical scenarios not previously considered.

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“…In this respect, they are similar to the general class of argument-taking nominals as given in the NomBank (Meyers et al, 2004). Similarly, there is a small body of literature that addresses nominal semantic role labelling (Gerber et al, 2009) and nominal subcategorization frames (Preiss et al, 2007). That said, the distinguishing property of shell nouns is that one of their semantic arguments is the shell content, but the literature in computational linguistics does not provide any method that is able to identify the shell content.…”

Section: Related Workmentioning

confidence: 99%

Resolving Shell Nouns

Kolhatkar¹,

Hirst²

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Shell nouns, such as fact and problem, occur frequently in all kinds of texts. These nouns themselves are unspecific, and can only be interpreted together with the shell content. We propose a general approach to automatically identify shell content of shell nouns. Our approach exploits lexicosyntactic knowledge derived from the linguistics literature. We evaluate the approach on a variety of shell nouns with a variety of syntactic expectations, achieving accuracies in the range of 62% (baseline = 33%) to 83% (baseline = 74%) on crowd-annotated data.

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The role of implicit argumentation in nominal SRL

Cited by 14 publications

References 14 publications

Automatic generation of system test cases from use case specifications

Automatic generation of system test cases from use case specifications

Automatic Generation of Acceptance Test Cases From Use Case Specifications: An NLP-Based Approach

Resolving Shell Nouns

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