Test Scenario Generation from Natural Language Requirements Descriptions based on Petri-Nets

Sarmiento, Edgar; Leite, J.C.S.P.; Almentero, Eduardo; Alzamora, Guina Sotomayor

doi:10.1016/j.entcs.2016.12.008

Cited by 27 publications

(28 citation statements)

References 6 publications

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“…The generated test cases cannot be executed automatically because they do not include test data. Sarmiento et al [12] generate test scenarios without test data from a restricted form of NL requirements specifications.…”

Section: Related Workmentioning

confidence: 99%

“…There are techniques [8] [9] [10] that generate test models from NL requirements, but the generated models need to be manually edited to enable test automation, thus creating scalability issues. In approaches generating test cases directly from NL requirements [11] [12] [13] [14], test cases are not executable and often require significant manual intervention to provide test input data (e.g., they need additional formal specifications [14]). A few approaches can generate executable test cases including test input data directly from NL requirements specifications [15] [16], but they require that requirements specifications be written according to a controlled natural language (CNL).…”

Section: Introductionmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Therefore, the authors use already pre-processed specifications in such a way dealing with texts in the natural language. More interesting are two approaches that use NLP for requirements transformation to test scenarios [32] and behavioural models [33], [35]. The same as the previous authors, Sarmiento et.al.…”

Section: Related Workmentioning

confidence: 97%

Extracting TFM Core Elements From Use Case Scenarios by Processing Structure and Text in Natural Language

Nazaruka

Osis

Gribermane

2019

Applied Computer Systems

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Extracting core elements of Topological Functioning Model (TFM) from use case scenarios requires processing of both structure and natural language constructs in use case step descriptions. The processing steps are discussed in the present paper. Analysis of natural language constructs is based on outcomes provided by Stanford CoreNLP. Stanford CoreNLP is the Natural Language Processing pipeline that allows analysing text at paragraph, sentence and word levels. The proposed technique allows extracting actions, objects, results, preconditions, post-conditions and executors of the functional features, as well as cause-effect relations between them. However, accuracy of it is dependent on the used language constructs and accuracy of specification of event flows. The analysis of the results allows concluding that even use case specifications require the use of rigor, or even uniform, structure of paths and sentences as well as awareness of the possible parsing errors.

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“…In the automotive engineering literature, architectures have been proposed to tackle the verification and validation of Autonomous systems. First, Sarmiento & al., 33 propose an automated method for generating scenarios. The method starts by the use of RNL (Restricted-form of Natural Language) for the description of the scenarios, and then deduces some Petri-Net models that are used as input to generate the scenarios.…”

Section: Simulation Architecture For Safety Validationmentioning

confidence: 99%

Safety Demonstration of Autonomous Vehicles: A Review and Future Research Questions

Koné¹,

Bonjour²,

Levrat³

et al. 2019

Complex Systems Design &Amp; Management

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The safety demonstration and validation of Autonomous vehicles (AVs) remains a challenging activity. In this paper, we firstly review what those challenges are and how they affect the safety validation of the AV. Then, we particularly focus on the simulation-based validation process, which seems to be inevitable among the recommended safety validation approaches. We show what is actually done and required in terms of scenarios generation, their assessment taking into account uncertainty and the simulation architecture to test and validate them. Finally, we end our review by summarizing key research questions that need to be addressed to help with this safety validation issue. 1 Introduction. An automated vehicle (AV) is a vehicle, which is able, according to the conditions of its operating environment and the level of automation, to move with or without human intervention. The Society of Automotive Engineers 1 (SAE) identifies six levels of automation: No Automation (Level 0), Driver Assistance (Level 1), Partial Automation (Level 2), Conditional Automation (Level 3), High Automation (Level 4), and Full Automation (Level 5). For its operation, an automated vehicle collects information about its environment, processes them, plans its trajectory and decides on actions to be performed. To implement this, manufacturers use specific technologies such as sensors and localization systems, communication systems and intelligent control systems. These embedded technologies are sometimes new, difficult to specify and have functional performance limitations regarding environmental conditions. This affects standard safety validation procedures, which face new challenges and are

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Test Scenario Generation from Natural Language Requirements Descriptions based on Petri-Nets

Cited by 27 publications

References 6 publications

Automatic generation of system test cases from use case specifications

Automatic generation of system test cases from use case specifications

Extracting TFM Core Elements From Use Case Scenarios by Processing Structure and Text in Natural Language

Safety Demonstration of Autonomous Vehicles: A Review and Future Research Questions

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