144,784 research outputs found
What influences the speed of prototyping? An empirical investigation of twenty software startups
It is essential for startups to quickly experiment business ideas by building
tangible prototypes and collecting user feedback on them. As prototyping is an
inevitable part of learning for early stage software startups, how fast
startups can learn depends on how fast they can prototype. Despite of the
importance, there is a lack of research about prototyping in software startups.
In this study, we aimed at understanding what are factors influencing different
types of prototyping activities. We conducted a multiple case study on twenty
European software startups. The results are two folds, firstly we propose a
prototype-centric learning model in early stage software startups. Secondly, we
identify factors occur as barriers but also facilitators for prototyping in
early stage software startups. The factors are grouped into (1) artifacts, (2)
team competence, (3) collaboration, (4) customer and (5) process dimensions. To
speed up a startups progress at the early stage, it is important to incorporate
the learning objective into a well-defined collaborative approach of
prototypingComment: This is the author's version of the work. Copyright owner's version
can be accessed at doi.org/10.1007/978-3-319-57633-6_2, XP2017, Cologne,
German
Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review
The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field
We Don't Need Another Hero? The Impact of "Heroes" on Software Development
A software project has "Hero Developers" when 80% of contributions are
delivered by 20% of the developers. Are such heroes a good idea? Are too many
heroes bad for software quality? Is it better to have more/less heroes for
different kinds of projects? To answer these questions, we studied 661 open
source projects from Public open source software (OSS) Github and 171 projects
from an Enterprise Github.
We find that hero projects are very common. In fact, as projects grow in
size, nearly all project become hero projects. These findings motivated us to
look more closely at the effects of heroes on software development. Analysis
shows that the frequency to close issues and bugs are not significantly
affected by the presence of project type (Public or Enterprise). Similarly, the
time needed to resolve an issue/bug/enhancement is not affected by heroes or
project type. This is a surprising result since, before looking at the data, we
expected that increasing heroes on a project will slow down howfast that
project reacts to change. However, we do find a statistically significant
association between heroes, project types, and enhancement resolution rates.
Heroes do not affect enhancement resolution rates in Public projects. However,
in Enterprise projects, the more heroes increase the rate at which project
complete enhancements.
In summary, our empirical results call for a revision of a long-held truism
in software engineering. Software heroes are far more common and valuable than
suggested by the literature, particularly for medium to large Enterprise
developments. Organizations should reflect on better ways to find and retain
more of these heroesComment: 8 pages + 1 references, Accepted to International conference on
Software Engineering - Software Engineering in Practice, 201
Technical Debt Prioritization: State of the Art. A Systematic Literature Review
Background. Software companies need to manage and refactor Technical Debt
issues. Therefore, it is necessary to understand if and when refactoring
Technical Debt should be prioritized with respect to developing features or
fixing bugs. Objective. The goal of this study is to investigate the existing
body of knowledge in software engineering to understand what Technical Debt
prioritization approaches have been proposed in research and industry. Method.
We conducted a Systematic Literature Review among 384 unique papers published
until 2018, following a consolidated methodology applied in Software
Engineering. We included 38 primary studies. Results. Different approaches have
been proposed for Technical Debt prioritization, all having different goals and
optimizing on different criteria. The proposed measures capture only a small
part of the plethora of factors used to prioritize Technical Debt qualitatively
in practice. We report an impact map of such factors. However, there is a lack
of empirical and validated set of tools. Conclusion. We observed that technical
Debt prioritization research is preliminary and there is no consensus on what
are the important factors and how to measure them. Consequently, we cannot
consider current research conclusive and in this paper, we outline different
directions for necessary future investigations
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