Improving manual reviews in function-centered engineering of embedded systems using a dedicated review model

Daun, Marian; Weyer, Thorsten; Pohl, Klaus

doi:10.1007/s10270-019-00723-2

Cited by 11 publications

(13 citation statements)

References 53 publications

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“…Our work is intended to primarily foster the manual validation of the functional adequacy of a system during early stages of development. This work complements our previous work on using diagrammatic representation to validate stakeholder intentions [57][58][59], safety properties [24,60,61], and context assumptions [62].…”

Section: Modeling During Validation and Verificationmentioning

confidence: 54%

“…Under consideration of the threats to validity and the fact that some statistical tests did not yield a level of significance but that findings well align with previous experiments, we are confident to claim that the purposeful use of multi-level MSC is beneficial compared to the use of only either type-level MSC or instance-level MSC. As we have already shown that MSC in general are good language to support manual validation tasks [57,59], this indicates that the use of multilevel MSC can considerably support validation.…”

Section: Deductions and Inferencesmentioning

confidence: 64%

“…Our work has consistently shown that dedicated review models, specific to the purpose of validation, are more effective in uncovering defects in system requirements. For example, we compared ITU MSCs with other modeling languages as review artifact [57][58][59] showing that MSCs are a good review model. In addition, we have shown that MSC-based review models support both less experienced and more experienced reviewers [63] that instancelevel MSC are a significantly better review artifact for collaborative CPS than type-level MSC [64].…”

Section: Modeling During Validation and Verificationmentioning

confidence: 99%

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Multi-Level Message Sequence Charts to Validate the Collaborative Automotive Cyber-Physical Systems

Daun¹,

Tenbergen²,

Brings³

et al. 2021

JASEN

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Autonomous driving and e-mobility are swiftly becoming not only the work of science fiction or popular science, but a reality.A key focus of manufacturers and suppliers in the automotive domain is of course to specify systems that implement this reality. Often, scenarios at type-level are used throughout the development process to specify system behavior and interaction within the car, as scenario models are comparatively easy to understand and can easily be subjected to manual validation. However, autonomous driving and e-mobility require interaction not just of systems within the same car, but collaboration between multiple cars as well as between cars and miscellaneous road infrastructure (e.g., smart road signs). The car becomes a Cyber-Physical System that dynamically forms collaborating networks at runtime with other Cyber-Physical System to create functionality that goes beyond the scope of the individual vehicle (e.g., resolve a traffic jam). Consequently, a plethora of possible compositions of such a network exist and must be specified and validated completely to assure their adequate and safe execution at runtime. Doing this at type-level with scenario models becomes prohibitively tedious, error prone, and likely results in unrealistic development cost. To combat this issue, we investigate the use of multi-level Message Sequence Charts to allow for specifying interaction scenarios between collaborative Cyber-Physical System in a network of collaborating automotive Cyber-Physical System. To assist the developer in systematically defining multi-level Message Sequence Charts, we propose two processes. The resulting diagrams use a mixture of type and instance-level abstractions within one conceptual diagram. This allows reducing the required effort to manually validate the adequacy of scenarios to a manageable amount because information within the scenarios can be validated in batches. At the same time, instance-level defects become more obvious. Evaluation results from a controlled experiment show that multi-level Message Sequence Charts contribute to effectiveness and efficiency of manual validation for collaborative automotive Cyber-Physical System.

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Section: Modeling During Validation and Verificationmentioning

confidence: 54%

Section: Deductions and Inferencesmentioning

confidence: 64%

Section: Modeling During Validation and Verificationmentioning

confidence: 99%

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Multi-Level Message Sequence Charts to Validate the Collaborative Automotive Cyber-Physical Systems

Daun¹,

Tenbergen²,

Brings³

et al. 2021

JASEN

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“…Model-based continuous function-centered engineering processes are already established in engineering practice [Daun et al 2019a]. To support function-centered development, this chapter proposes a function metamodel.…”

Section: Methodological Approachmentioning

confidence: 99%

Function Modeling for Collaborative Embedded Systems

Hayward

Daun

Petrovska

et al. 2020

Model-Based Engineering of Collaborative Embedded Systems

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The evolution from traditional embedded systems to dynamically interacting, collaborative embedded systems increases the complexity and the number of requirements involved in the model-based development process. In this chapter, we present the new aspects that need to be considered when modeling functions for collaborative embedded systems and collaborative system groups, such as the relationship between functions of a single system and functions resulting from the interplay of multiple systems. These different aspects are represented by a formal, domain-independent metamodel. To aid understanding, we also apply the metamodel to two different use cases.

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“…In Daun et al (2019b) we reported an experiment to investigate whether reviews of an automatically generated and consolidated dedicated review model are beneficial compared to the review of the behavioral requirements and the functional design of CPS. Furthermore, it investigates whether the experience and skill level of reviewers have an influence on The review of the review model is more effective, more efficient, more user confidence increasing and is subjectively perceived as more supportive than the review of behavioral requirements and functional design.…”

Section: Experiments 4: Comparison Review Model Vs Original Specificationsmentioning

confidence: 99%

Reliability of self-rated experience and confidence as predictors for students’ performance in software engineering

et al. 2021

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Students’ experience is used in empirical software engineering research as well as in software engineering education to group students in either homogeneous or heterogeneous groups. To do so, students are commonly asked to self-rate their experience, as self-rated experience has been shown to be a good predictor for performance in programming tasks. Another experience-related measurement is participants’ confidence (i.e., how confident is the person that their given answer is correct). Hence, self-rated experience and confidence are used as selector or control variables throughout empirical software engineering research and software engineering education. In this paper, we analyze data from several student experiments conducted in the past years to investigate whether self-rated experience and confidence are also good predictors for students’ performance in model comprehension tasks. Our results show that while students can somewhat assess the correctness of a particular answer to one concrete question regarding a conceptual model (i.e., their confidence), their overall self-rated experience does not correlate with their actual performance. Hence, the use of the commonly used measurement of self-rated experience as a selector or control variable must be considered unreliable for model comprehension tasks.

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Improving manual reviews in function-centered engineering of embedded systems using a dedicated review model

Cited by 11 publications

References 53 publications

Multi-Level Message Sequence Charts to Validate the Collaborative Automotive Cyber-Physical Systems

Multi-Level Message Sequence Charts to Validate the Collaborative Automotive Cyber-Physical Systems

Function Modeling for Collaborative Embedded Systems

Reliability of self-rated experience and confidence as predictors for students’ performance in software engineering

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