180,952 research outputs found
Boundary Value Exploration for Software Analysis
For software to be reliable and resilient, it is widely accepted that tests
must be created and maintained alongside the software itself. One safeguard
from vulnerabilities and failures in code is to ensure correct behavior on the
boundaries between sub-domains of the input space. So-called boundary value
analysis (BVA) and boundary value testing (BVT) techniques aim to exercise
those boundaries and increase test effectiveness. However, the concepts of BVA
and BVT themselves are not clearly defined and it is not clear how to identify
relevant sub-domains, and thus the boundaries delineating them, given a
specification. This has limited adoption and hindered automation. We clarify
BVA and BVT and introduce Boundary Value Exploration (BVE) to describe
techniques that support them by helping to detect and identify boundary inputs.
Additionally, we propose two concrete BVE techniques based on
information-theoretic distance functions: (i) an algorithm for boundary
detection and (ii) the usage of software visualization to explore the behavior
of the software under test and identify its boundary behavior. As an initial
evaluation, we apply these techniques on a much used and well-tested date
handling library. Our results reveal questionable behavior at boundaries
highlighted by our techniques. In conclusion, we argue that the boundary value
exploration that our techniques enable is a step towards automated boundary
value analysis and testing which can foster their wider use and improve test
effectiveness and efficiency
Information technology as boundary object for transformational learning
Collaborative work is considered as a way to improve productivity and value generation in
construction. However, recent research demonstrates that socio-cognitive factors related to fragmentation of specialized knowledge may hinder team performance. New methods based on theories of practice are emerging in Computer Supported Collaborative Work and organisational learning to break these knowledge boundaries,
facilitating knowledge sharing and the generation of new knowledge through transformational learning. According to these theories, objects used in professional practice play a key role in mediating interactions. Rules and methods related to these practices are also embedded in these objects. Therefore changing collaborative
patterns demand reconfiguring objects that are at the boundary between specialized practices, namely boundary objects. This research is unique in presenting an IT strategy in which technology is used as a boundary object to facilitate transformational learning in collaborative design work
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Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework
Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA
The Topology ToolKit
This system paper presents the Topology ToolKit (TTK), a software platform
designed for topological data analysis in scientific visualization. TTK
provides a unified, generic, efficient, and robust implementation of key
algorithms for the topological analysis of scalar data, including: critical
points, integral lines, persistence diagrams, persistence curves, merge trees,
contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots,
Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due
to a tight integration with ParaView. It is also easily accessible to
developers through a variety of bindings (Python, VTK/C++) for fast prototyping
or through direct, dependence-free, C++, to ease integration into pre-existing
complex systems. While developing TTK, we faced several algorithmic and
software engineering challenges, which we document in this paper. In
particular, we present an algorithm for the construction of a discrete gradient
that complies to the critical points extracted in the piecewise-linear setting.
This algorithm guarantees a combinatorial consistency across the topological
abstractions supported by TTK, and importantly, a unified implementation of
topological data simplification for multi-scale exploration and analysis. We
also present a cached triangulation data structure, that supports time
efficient and generic traversals, which self-adjusts its memory usage on demand
for input simplicial meshes and which implicitly emulates a triangulation for
regular grids with no memory overhead. Finally, we describe an original
software architecture, which guarantees memory efficient and direct accesses to
TTK features, while still allowing for researchers powerful and easy bindings
and extensions. TTK is open source (BSD license) and its code, online
documentation and video tutorials are available on TTK's website
The liminality of trajectory shifts in institutional entrepreneurship
In this paper, we develop a process model of trajectory shifts in institutional entrepreneurship. We focus on the liminal periods experienced by institutional entrepreneurs when they, unlike the rest of the organization, recognize limits in the present and seek to shift a familiar past into an unfamiliar and uncertain future. Such periods involve a situation where the new possible future, not yet fully formed, exists side-by-side with established innovation trajectories. Trajectory shifts are moments of truth for institutional entrepreneurs, but little is known about the underlying mechanisms of how entrepreneurs reflectively deal with liminality to conceive and bring forth new innovation trajectories. Our in-depth case study research at CarCorp traces three such mechanisms (reflective dissension, imaginative projection, and eliminatory exploration) and builds the basis for understanding the liminality of trajectory shifts. The paper offers theoretical implications for the institutional entrepreneurship literature
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