127 research outputs found
Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies
In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations
Risk management with enhanced tracing of requirements rationale in highly distributed projects
A recent survey with project managers of highly distributed projects at Siemens Program and Systems Engineering (PSE) brought up as main challenges: more severe communication hurdles compared to collocated teams and higher effort to communicate requirements in the team. In this paper, we address requirements tracing options to facilitate risk management with requirements clarification, collaboration, and knowledge management. We propose concepts for enhanced requirements tracing that include the rationale for requirements, related decisions, their history; and stakeholder value propositions. We sketch a cost-benefit model that helps the project manager to understand what tracing approach is worthwhile to address requirements risk in a project. The outcome lays the basis for planning empirical studies at PSE
Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review
Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE
A conceptual framework for semantic case-based safety analysis
Hazard and Operability (HAZOP) Analysis and Failure Mode and Effect Analysis (FMEA) are among the most widely used safety analysis procedures in the development of safety-critical and embedded systems. These analyses are generally perceived as complex and time-consuming, hindering an effective reuse of previous results or experiences. In this paper we present a conceptual semantic case-based framework for safety analysis, which facilitates the reuse of previous HAZOP and FMEA experiences in order to reduce the time and effort associated with these analyses. We present the core technologies of the conceptual framework and evaluated a prototype of the framework, KROSA, in an experiment with domain experts at ABB Norway. Initial results confirm the viability of the conceptual framework for industrial applicatio
Collaborative Exchange of Systematic Literature Review Results: The Case of Empirical Software Engineering
ABSTRACT Complementary to managing bibliographic information as done by digital libraries, the management of concrete research objects (e.g., experimental workflows, design patterns) is a pre-requisite to foster collaboration and re-use of research results. In this paper we describe the case of the Empirical Software Engineering domain, where researchers use systematic literature reviews (SLRs) to conduct and report on literature studies. Given their structured nature, the outputs of such SLR processes are a special and complex type of research object. Since performing SLRs is a time consuming process, it is highly desirable to enable sharing and reuse of the complex knowledge structures produced through SLRs. This would enable, for example, conducting new studies that build on the findings of previous studies. To support collaborative features necessary for multiple research groups to share and re-use each other's work, we hereby propose a solution approach that is inspired by software engineering best-practices and is implemented using Semantic Web technologies
Status Quo and Problems of Requirements Engineering for Machine Learning: Results from an International Survey
Systems that use Machine Learning (ML) have become commonplace for companies
that want to improve their products and processes. Literature suggests that
Requirements Engineering (RE) can help address many problems when engineering
ML-enabled systems. However, the state of empirical evidence on how RE is
applied in practice in the context of ML-enabled systems is mainly dominated by
isolated case studies with limited generalizability. We conducted an
international survey to gather practitioner insights into the status quo and
problems of RE in ML-enabled systems. We gathered 188 complete responses from
25 countries. We conducted quantitative statistical analyses on contemporary
practices using bootstrapping with confidence intervals and qualitative
analyses on the reported problems involving open and axial coding procedures.
We found significant differences in RE practices within ML projects. For
instance, (i) RE-related activities are mostly conducted by project leaders and
data scientists, (ii) the prevalent requirements documentation format concerns
interactive Notebooks, (iii) the main focus of non-functional requirements
includes data quality, model reliability, and model explainability, and (iv)
main challenges include managing customer expectations and aligning
requirements with data. The qualitative analyses revealed that practitioners
face problems related to lack of business domain understanding, unclear goals
and requirements, low customer engagement, and communication issues. These
results help to provide a better understanding of the adopted practices and of
which problems exist in practical environments. We put forward the need to
adapt further and disseminate RE-related practices for engineering ML-enabled
systems.Comment: Accepted for Publication at PROFES 202
Reviewing technical approaches for sharing and preservation of experimental data
Empirical Software Engineering (ESE) replication
researchers need to store and manipulate experimental data
for several purposes, in particular analysis and reporting.
Current research needs call for sharing and preservation of
experimental data as well. In a previous work, we analyzed
Replication Data Management (RDM) needs. A novel concept,
called Experimental Ecosystem, was proposed to solve
current deficiencies in RDMapproaches. The empirical ecosystem
provides replication researchers with a common framework
that integrates transparently local heterogeneous data
sources. A typical situation where the Empirical Ecosystem
is applicable, is when several members of a research group, or
several research groups collaborating together, need to share
and access each other experimental results. However, to be
able to apply the Empirical Ecosystem concept and deliver
all promised benefits, it is necessary to analyze the software
architectures and tools that can properly support it
Replication Data Management,needs and solutions: an initial evaluation of conceptual approaches for integrating heterogeneous replication study data
Replication Data Management (RDM) aims at enabling the use of data collections from several iterations of an experiment. However, there are several major challenges to RDM from integrating data models and data from empirical study infrastructures that were not designed to cooperate, e.g., data model variation of local data sources. [Objective] In this paper we analyze RDM needs and evaluate conceptual RDM approaches to support replication researchers. [Method] We adapted the ATAM evaluation process to (a) analyze RDM use cases and needs of empirical replication study research groups and (b) compare three conceptual approaches to address these RDM needs: central data repositories with a fixed data model, heterogeneous local repositories, and an empirical ecosystem. [Results] While the central and local approaches have major issues that are hard to resolve in practice, the empirical ecosystem allows bridging current gaps in RDM from heterogeneous data sources. [Conclusions] The empirical ecosystem approach should be explored in diverse empirical environments
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