Requirements Engineering for Embedded Systems: An Investigation of Industry Needs

Sikora, Ernst; Tenbergen, Bastian; Pohl, Klaus

doi:10.1007/978-3-642-19858-8_16

Cited by 19 publications

(16 citation statements)

References 11 publications

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“…The success of the analysis of requirements depends heavily on models. Formalization of requirements through models enables quality control at a level that is impossible to reach with requirements articulated in natural language (Sikora, Bastian, & Pohl, 2011). With the growing importance of compliance between business strategy and ICT realizations as well as the emergence and evolution of Model Driven Engineering (MDE), conceptual modeling gains more relevance.…”

Section: Introductionmentioning

confidence: 99%

Process mining analysis of conceptual modeling behavior of novices – empirical study using JMermaid modeling and experimental logging environment

Sedrakyan

Snoeck

Weerdt

2014

Computers in Human Behavior

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a b s t r a c tPrevious studies on learning challenges in the field of modeling focus on cognitive perspectives, such as model understanding, modeling language knowledge and perceptual properties of graphical notation by novice business analysts as major sources affecting model quality. In the educational context outcome feedback is usually applied to improve learning achievements. However, not many research publications have been written observing the characteristics of a modeling process itself that can be associated with better/worse learning outcomes, nor have any empirically validated results been reported on the observations of modeling activities in the educational context. This paper attempts to cover this gap for conceptual modeling. We analyze modeling behavior (conceptual modeling event data of 20 cases, 10.000 events in total) using experimental logging functionality of the JMermaid modeling tool and process mining techniques. The outcomes of the work include modeling patterns that are indicative for worse/better learning performance. The results contribute to (1) improving teaching guidance for conceptual modeling targeted at process-oriented feedback, (2) providing recommendations on the type of data that can be useful in observing a modeling behavior from the perspective of learning outcomes. In addition, the study provides first insights for learning analytics research in the domain of conceptual modeling.

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Section: Introductionmentioning

confidence: 99%

Process mining analysis of conceptual modeling behavior of novices – empirical study using JMermaid modeling and experimental logging environment

Sedrakyan

Snoeck

Weerdt

2014

Computers in Human Behavior

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“…The survey conducted in 12 software companies by Hall et al [21] identifies communication as a key problem in requirements engineering but argues that most requirements problems are organizational rather than technical. Sikora et al [48] conducted a qualitative study with 7 large companies from five branches of the embedded systems domain: they found that models are regarded as a supportive means for analysis, but are not used in specification documents. Since the authors' focus is model-based research, they recommend providing industrial practitioners with a methodological guidance to foster the use of models, such as reference models and rules.…”

Section: B!field Studies In Re Practices -Tool Perspectivementioning

confidence: 99%

Usability Insights for Requirements Engineering Tools: A User Study with Practitioners in Aeronautics

Gaspard-Boulinc¹,

Conversy²

2017

2017 IEEE 25th International Requirements Engineering Conference (RE)

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Requirements Engineering plays a crucial role in coordinating the different stakeholders needed for safe aeronautics systems engineering. We conducted a qualitative study, using interviews and mockups, with fifteen industrial practitioners from four aeronautics companies, to investigate what tasks are actually performed by requirements engineers and how current tools support these tasks. We found that RE-specific tools constrain engineers to a rigid workflow, which is conflicting with the adaptive exploration of the problem. Engineers often start by using general-purpose tools to foster exploration and collaborative work with suppliers, at the expense of traceability. When engineers shift to requirements refinement and verification, they must use RE-specific tools to grant traceability. Then, the lack of tool usability yields significant time loss and dissatisfaction. Based on scenarios of observed RE practices and walkthrough, we formulate usability insights for RE-specific tools to conciliate flexibility and traceability throughout the RE process.

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“…Several studies have reported on the state of practice in RE in the embedded domain, e.g. [10,12,25,50]. According to Broy [12], one of the big challenges in the automotive industry is RE.…”

Section: Introductionmentioning

confidence: 99%

“…Despite these statements, Braun et al [10] state that requirements are still elicited and specified in an ad hoc manner and typically in natural language. Sikora et al [50] report from a case study with industry practitioners 123 that models could be used to tackle several RE challenges in the embedded domain, e.g. the increasing complexity.…”

Section: Introductionmentioning

confidence: 99%

Use, potential, and showstoppers of models in automotive requirements engineering

2018

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Several studies report that the use of model-centric methods in the automotive domain is widespread and offers several benefits. However, existing work indicates that few modelling frameworks explicitly include requirements engineering (RE), and that natural language descriptions are still the status quo in RE. Therefore, we aim to increase the understanding of current and potential future use of models in RE, with respect to the automotive domain. In this paper, we report our findings from a multiple-case study with two automotive companies, collecting interview data from 14 practitioners. Our results show that models are used for a variety of different purposes during RE in the automotive domain, e.g. to improve communication and to handle complexity. However, these models are often used in an unsystematic fashion and restricted to few experts. A more widespread use of models is prevented by various challenges, most of which align with existing work on model use in a general sense. Furthermore, our results indicate that there are many potential benefits associated with future use of models during RE. Interestingly, existing research does not align well with several of the proposed use cases, e.g. restricting the use of models to informal notations for communication purposes. Based on our findings, we recommend a stronger focus on informal modelling and on using models for multidisciplinary environments. Additionally, we see the need for future work in the area of model use, i.e. information extraction from models by non-expert modellers.

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Requirements Engineering for Embedded Systems: An Investigation of Industry Needs

Cited by 19 publications

References 11 publications

Process mining analysis of conceptual modeling behavior of novices – empirical study using JMermaid modeling and experimental logging environment

Process mining analysis of conceptual modeling behavior of novices – empirical study using JMermaid modeling and experimental logging environment

Usability Insights for Requirements Engineering Tools: A User Study with Practitioners in Aeronautics

Use, potential, and showstoppers of models in automotive requirements engineering

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