30,509 research outputs found
Boundary Objects and their Use in Agile Systems Engineering
Agile methods are increasingly introduced in automotive companies in the
attempt to become more efficient and flexible in the system development. The
adoption of agile practices influences communication between stakeholders, but
also makes companies rethink the management of artifacts and documentation like
requirements, safety compliance documents, and architecture models.
Practitioners aim to reduce irrelevant documentation, but face a lack of
guidance to determine what artifacts are needed and how they should be managed.
This paper presents artifacts, challenges, guidelines, and practices for the
continuous management of systems engineering artifacts in automotive based on a
theoretical and empirical understanding of the topic. In collaboration with 53
practitioners from six automotive companies, we conducted a design-science
study involving interviews, a questionnaire, focus groups, and practical data
analysis of a systems engineering tool. The guidelines suggest the distinction
between artifacts that are shared among different actors in a company (boundary
objects) and those that are used within a team (locally relevant artifacts). We
propose an analysis approach to identify boundary objects and three practices
to manage systems engineering artifacts in industry
An Exploratory Study of Forces and Frictions affecting Large-Scale Model-Driven Development
In this paper, we investigate model-driven engineering, reporting on an
exploratory case-study conducted at a large automotive company. The study
consisted of interviews with 20 engineers and managers working in different
roles. We found that, in the context of a large organization, contextual forces
dominate the cognitive issues of using model-driven technology. The four forces
we identified that are likely independent of the particular abstractions chosen
as the basis of software development are the need for diffing in software
product lines, the needs for problem-specific languages and types, the need for
live modeling in exploratory activities, and the need for point-to-point
traceability between artifacts. We also identified triggers of accidental
complexity, which we refer to as points of friction introduced by languages and
tools. Examples of the friction points identified are insufficient support for
model diffing, point-to-point traceability, and model changes at runtime.Comment: To appear in proceedings of MODELS 2012, LNCS Springe
Safety-Critical Systems and Agile Development: A Mapping Study
In the last decades, agile methods had a huge impact on how software is
developed. In many cases, this has led to significant benefits, such as quality
and speed of software deliveries to customers. However, safety-critical systems
have widely been dismissed from benefiting from agile methods. Products that
include safety critical aspects are therefore faced with a situation in which
the development of safety-critical parts can significantly limit the potential
speed-up through agile methods, for the full product, but also in the
non-safety critical parts. For such products, the ability to develop
safety-critical software in an agile way will generate a competitive advantage.
In order to enable future research in this important area, we present in this
paper a mapping of the current state of practice based on {a mixed method
approach}. Starting from a workshop with experts from six large Swedish product
development companies we develop a lens for our analysis. We then present a
systematic mapping study on safety-critical systems and agile development
through this lens in order to map potential benefits, challenges, and solution
candidates for guiding future research.Comment: Accepted at Euromicro Conf. on Software Engineering and Advanced
Applications 2018, Prague, Czech Republi
Predicting and Evaluating Software Model Growth in the Automotive Industry
The size of a software artifact influences the software quality and impacts
the development process. In industry, when software size exceeds certain
thresholds, memory errors accumulate and development tools might not be able to
cope anymore, resulting in a lengthy program start up times, failing builds, or
memory problems at unpredictable times. Thus, foreseeing critical growth in
software modules meets a high demand in industrial practice. Predicting the
time when the size grows to the level where maintenance is needed prevents
unexpected efforts and helps to spot problematic artifacts before they become
critical.
Although the amount of prediction approaches in literature is vast, it is
unclear how well they fit with prerequisites and expectations from practice. In
this paper, we perform an industrial case study at an automotive manufacturer
to explore applicability and usability of prediction approaches in practice. In
a first step, we collect the most relevant prediction approaches from
literature, including both, approaches using statistics and machine learning.
Furthermore, we elicit expectations towards predictions from practitioners
using a survey and stakeholder workshops. At the same time, we measure software
size of 48 software artifacts by mining four years of revision history,
resulting in 4,547 data points. In the last step, we assess the applicability
of state-of-the-art prediction approaches using the collected data by
systematically analyzing how well they fulfill the practitioners' expectations.
Our main contribution is a comparison of commonly used prediction approaches
in a real world industrial setting while considering stakeholder expectations.
We show that the approaches provide significantly different results regarding
prediction accuracy and that the statistical approaches fit our data best
Why and How Your Traceability Should Evolve: Insights from an Automotive Supplier
Traceability is a key enabler of various activities in automotive software
and systems engineering and required by several standards. However, most
existing traceability management approaches do not consider that traceability
is situated in constantly changing development contexts involving multiple
stakeholders. Together with an automotive supplier, we analyzed how technology,
business, and organizational factors raise the need for flexible traceability.
We present how traceability can be evolved in the development lifecycle, from
early elicitation of traceability needs to the implementation of mature
traceability strategies. Moreover, we shed light on how traceability can be
managed flexibly within an agile team and more formally when crossing team
borders and organizational borders. Based on these insights, we present
requirements for flexible tool solutions, supporting varying levels of data
quality, change propagation, versioning, and organizational traceability.Comment: 9 pages, 3 figures, accepted in IEEE Softwar
Absorptive capacity and relationship learning mechanisms as complementary drivers of green innovation performance
This paper aims to explore in depth how internal and external knowledge-based drivers actually affect the firms\u2019 green innovation performance. Subsequently, this study analyzes the relationships between absorptive capacity (internal knowledge-based driver), relationship learning (external knowledge-based driver) and green innovation performance.
This study relies on a sample of 112 firms belonging to the Spanish automotive components manufacturing sector (ACMS) and uses partial least squares path modeling to test the hypotheses proposed.
The empirical results show that both absorptive capacity and relationship learning exert a significant positive effect on the dependent variable and that relationship learning moderates the link between absorptive capacity and green innovation performance.
This paper presents some limitations with respect to the particular sector (i.e. the ACMS) and geographical context (Spain). For this reason, researchers must be thoughtful while generalizing these results to distinct scenarios.
Managers should devote more time and resources to reinforce their absorptive capacity as an important strategic tool to generate new knowledge and hence foster green innovation performance in manufacturing industries.
The paper shows the importance of encouraging decision-makers to cultivate and rely on relationship learning mechanisms with their main stakeholders and to acquire the necessary information and knowledge that might be valuable in the maturity of green innovations.
This study proposes that relationship learning plays a moderating role in the relationship between absorptive capacity and green innovation performance
Transferring Collective Knowledge: Collective and Fragmented Teaching and Learning in the Chinese Auto Industry
Collective knowledge, consisting of tacit group-embedded knowledge, is a key element of organizational capabilities. This study undertakes a multiple-case study of the transfer of collective knowledge, guided by a set of tentative constructs and propositions derived from organizational learning theory. By focusing on the group-embeddedness dimension of collective knowledge, we direct our attention to the source and recipient communities. We identify two sets of strategic choices concerning the transfer of collective knowledge: collective vs. fragmented teaching, and collective vs. fragmented learning. The empirical context of this study is international R&D capability transfer in the Chinese auto industry. From the case evidence, we find the expected benefits of collective teaching and collective learning, and also discover additional benefits of these two strategies, including the creation of a bridge network communication infrastructure. The study disclosed other conditions underlying the choice of strategies of transferring collective knowledge, including transfer effort and the level of group-embeddedness of the knowledge to be taught or re-embedded. The paper provides a group-level perspective in understanding organizational capabilities, as well as a set of refined constructs and propositions concerning strategic choices of transferring collective knowledge. The study also provides a rich description of the best practices and lessons learned in transferring organizational capabilities.http://deepblue.lib.umich.edu/bitstream/2027.42/39804/3/wp420.pd
Keeping Continuous Deliveries Safe
Allowing swift release cycles, Continuous Delivery has become popular in
application software development and is starting to be applied in
safety-critical domains such as the automotive industry. These domains require
thorough analysis regarding safety constraints, which can be achieved by formal
verification and the execution of safety tests resulting from a safety analysis
on the product. With continuous delivery in place, such tests need to be
executed with every build to ensure the latest software still fulfills all
safety requirements. Even more though, the safety analysis has to be updated
with every change to ensure the safety test suite is still up-to-date. We thus
propose that a safety analysis should be treated no differently from other
deliverables such as source-code and dependencies, formulate guidelines on how
to achieve this and advert areas where future research is needed.Comment: 4 pages, 3 figure
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