A Lightweight Multilevel Markup Language for Connecting Software Requirements and Simulations

Pudlitz, Florian; Vogelsang, Andreas; Brokhausen, Florian

doi:10.1007/978-3-030-15538-4_11

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…The work of [11] describes a multilevel markup language to extract information from natural language requirements. An engineer can annotate text phrases to be observed in a simulation.…”

Section: B Multilevel Markup Languagementioning

confidence: 99%

“…Our definition differs from the one in [10] and [11]. While we also expect a specific value mapped to each name to be identified as a state, we do not incorporate this value into our state definition.…”

Section: A Definition Of Statesmentioning

confidence: 99%

“…Therefore, in [10], the state engine in a different requirements, without explicitly mentioned values, would not be identified as a state. The work in [11] does not consider values in their definition of level two (see II-B) as well. The decision whether a term is considered a state is entirely subjective to the engineer.…”

Section: A Definition Of Statesmentioning

confidence: 99%

“…In comparison, our definition is stricter, since we do require assignable values for a state to be identified as such. However, the approach in [11] can benefit from the one presented in this paper.…”

Section: A Definition Of Statesmentioning

confidence: 99%

“…In [11], a method is presented which, among other things, marks states in requirements. In a subsequent step, these states are connected to specific signals of the simulation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Extraction of System States from Natural Language Requirements

Pudlitz

Brokhausen

Vogelsang

2019

2019 IEEE 27th International Requirements Engineering Conference (RE)

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In recent years, simulations have proven to be an important means to verify the behavior of complex software systems. The different states of a system are monitored in the simulations and are compared against the requirements specification. So far, system states in natural language requirements cannot be automatically linked to signals from the simulation. However, the manual mapping between requirements and simulation is a time-consuming task. Named-entity Recognition is a sub-task from the field of automated information retrieval and is used to classify parts of natural language texts into categories. In this paper, we use a self-trained Named-entity Recognition model with Bidirectional LSTMs and CNNs to extract states from requirements specifications. We present an almost entirely automated approach and an iterative semi-automated approach to train our model. The automated and iterative approach are compared and discussed with respect to the usual manual extraction. We show that the manual extraction of states in 2,000 requirements takes nine hours. Our automated approach achieves an F1-score of 0.51 with 15 minutes of manual work and the iterative approach achieves an F1-score of 0.62 with 100 minutes of work.

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“…The work of [11] describes a multilevel markup language to extract information from natural language requirements. An engineer can annotate text phrases to be observed in a simulation.…”

Section: B Multilevel Markup Languagementioning

confidence: 99%

Section: A Definition Of Statesmentioning

confidence: 99%

Section: A Definition Of Statesmentioning

confidence: 99%

Section: A Definition Of Statesmentioning

confidence: 99%

“…In [11], a method is presented which, among other things, marks states in requirements. In a subsequent step, these states are connected to specific signals of the simulation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Extraction of System States from Natural Language Requirements

Pudlitz

Brokhausen

Vogelsang

2019

2019 IEEE 27th International Requirements Engineering Conference (RE)

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Specification of Passive Test Cases Using an Improved T-EARS Language

Flemström

Afzal

Enoiu

2022

Software Quality: The Next Big Thing in Software Engineering and Quality

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Simulation methods and tools for collaborative embedded systems: with focus on the automotive smart ecosystems

Cioroaica¹,

Pudlitz²,

Gerostathopoulos³

et al. 2019

SICS Softw.-Inensiv. Cyber-Phys. Syst.

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Embedded Systems are increasingly equipped with open interfaces that enable communication and collaboration with other embedded systems. Collaborative Embedded Systems (CES) can be seen as an emerging new class of systems which, although individually designed and developed, can form collaborations at runtime. When embedded systems collaborate with each other, functions developed independently need to be integrated for performing evaluation of the resulting system in order to discover unwanted side-effects. Traditionally, early-stage validation and verification (V&V) of systems composed of collaborative subsystems is performed by function integration at design time. Simulation is used at this stage to verify system's behaviour in a predefined set of test scenarios. In this paper we provide a survey of simulation methods and tools for the V&V of CES. In the context of one use case from the automotive domain (vehicle platooning) we present solutions (methods and tools) and challenges brought by evaluating vehicle collaboration using simulation.

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A Lightweight Multilevel Markup Language for Connecting Software Requirements and Simulations

Cited by 6 publications

References 9 publications

Extraction of System States from Natural Language Requirements

Extraction of System States from Natural Language Requirements

Specification of Passive Test Cases Using an Improved T-EARS Language

Simulation methods and tools for collaborative embedded systems: with focus on the automotive smart ecosystems

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