Data-driven and tool-supported elicitation of quality requirements in agile companies

Software Quality Journal - Quality requirements (QRs) are a key artifact needed to ensure the quality and success of a software system. Despite their importance, QRs rarely get the same degree of...     

Efficient Computation of Smoothed Exponential Maps

Many applications in geometry processing require the computation of local parameterizations on a surface mesh at interactive rates. A popular approach is to compute local exponential maps, i.e. parameterizations that preserve distance and angle to the origin of the map. We extend the computation of geodesic distance by heat diffusion to also determine angular information for the geodesic curves. This approach has two important benefits compared to fast approximate as well as exact forward tracing of the distance function: First, it allows generating smoother maps, avoiding discontinuities. Second, exploiting the factorization of the global Laplace–Beltrami operator of the mesh and using recent localized solution techniques, the computation is more efficient even compared to fast approximate solutions based on Dijkstra's algorithm.     

Does syntax highlighting help programming novices?

Program comprehension is an important skill for programmers – extending and debugging existing source code is part of the daily routine. Syntax highlighting is one of the most common tools used to support developers in understanding algorithms. However, most research in this area originates from a time when programmers used a completely different tool chain. We examined the influence of syntax highlighting on novices’ ability to comprehend source code. Additional analyses cover the influence of task type and programming experience on the code comprehension ability itself and its relation to syntax highlighting. We conducted a controlled experiment with 390 undergraduate students in an introductory Java programming course. We measured the correctness with which they solved small coding tasks. Each test subject received some tasks with syntax highlighting and some without. The data provided no evidence that syntax highlighting improves novices’ ability to comprehend source code. There are very few similar experiments and it is unclear as of yet which factors impact the effectiveness of syntax highlighting. One major limitation may be the types of tasks chosen for this experiment. The results suggest that syntax highlighting squanders a feedback channel from the IDE to the programmer that can be used more effectively.     

Software engineering practices and Simulink: bridging the gap

International Journal on Software Tools for Technology Transfer - Although widely used in embedded systems design, Matlab/Simulink is not considered a state-of-the-art design environment by the...     

Tree Branch Level of Detail Models for Forest Navigation

We present a level of detail (LOD) method designed for tree branches. It can be combined with methods for processing tree foliage to facilitate navigation through large virtual forests. Starting from a skeletal representation of a tree, we fit polygon meshes of various densities to the skeleton while the mesh density is adjusted according to the required visual fidelity. For distant models, these branch meshes are gradually replaced with semi‐transparent lines until the tree recedes to a few lines. Construction of these complete LOD models is guided by error metrics to ensure smooth transitions between adjacent LOD models. We then present an instancing technique for discrete LOD branch models, consisting of polygon meshes plus semi‐transparent lines. Line models with different transparencies are instanced on the GPU by merging multiple tree samples into a single model. Our technique reduces the number of draw calls in GPU and increases rendering performance. Our experiments demonstrate that large‐scale forest scenes can be rendered with excellent detail and shadows in real time.     

A user parameter-free approach for mining robust sequential classification rules

Recent years have witnessed extensive studies of graph classification due to the rapid increase in applications involving structural data and complex relationships. To support graph classification, all existing methods require that training graphs should be relevant (or belong) to the target class, but cannot integrate graphs irrelevant to the class of interest into the learning process. In this paper, we study a new universum graph classification framework which leverages additional “non-example” graphs to help improve the graph classification accuracy. We argue that although universum graphs do not belong to the target class, they may contain meaningful structure patterns to help enrich the feature space for graph representation and classification. To support universum graph classification, we propose a mathematical programming algorithm, ugBoost, which integrates discriminative subgraph selection and margin maximization into a unified framework to fully exploit the universum. Because informative subgraph exploration in a universum setting requires the search of a large space, we derive an upper bound discriminative score for each subgraph and employ a branch-and-bound scheme to prune the search space. By using the explored subgraphs, our graph classification model intends to maximize the margin between positive and negative graphs and minimize the loss on the universum graph examples simultaneously. The subgraph exploration and the learning are integrated and performed iteratively so that each can be beneficial to the other. Experimental results and comparisons on real-world dataset demonstrate the performance of our algorithm.     

Performance and energy effects on task-based parallelized applications

Heterogeneity, parallelization and vectorization are key techniques to improve the performance and energy efficiency of modern computing systems. However, programming and maintaining code for these architectures poses a huge challenge due to the ever-increasing architecture complexity. Task-based environments hide most of this complexity, improving scalability and usage of the available resources. In these environments, while there has been a lot of effort to ease parallelization and improve the usage of heterogeneous resources, vectorization has been considered a secondary objective. Furthermore, there has been a swift and unstoppable burst of vector architectures at all market segments, from embedded to HPC. Vectorization can no longer be ignored, but manual vectorization is tedious, error-prone and not practical for the average programmer. This work evaluates the feasibility of user-directed vectorization in task-based applications. Our evaluation is based on the OmpSs programming model, extended to support user-directed vectorization for different SIMD architectures (i.e., SSE, AVX2, AVX512). Results show that user-directed codes achieve manually optimized code performance and energy efficiency with minimal code modifications, favoring portability across different SIMD architectures.     

A Survey on Data‐driven Dictionary‐based Methods for 3D Modeling

Dictionaries are very useful objects for data analysis, as they enable a compact representation of large sets of objects through the combination of atoms. Dictionary‐based techniques have also particularly benefited from the recent advances in machine learning, which has allowed for data‐driven algorithms to take advantage of the redundancy in the input dataset and discover relations between objects without human supervision or hard‐coded rules. Despite the success of dictionary‐based techniques on a wide range of tasks in geometric modeling and geometry processing, the literature is missing a principled state‐of‐the‐art of the current knowledge in this field. To fill this gap, we provide in this survey an overview of data‐driven dictionary‐based methods in geometric modeling. We structure our discussion by application domain: surface reconstruction, compression, and synthesis. Contrary to previous surveys, we place special emphasis on dictionary‐based methods suitable for 3D data synthesis, with applications in geometric modeling and design. Our ultimate goal is to enlight the fact that these techniques can be used to combine the data‐driven paradigm with design intent to synthesize new plausible objects with minimal human intervention. This is the main motivation to restrict the scope of the present survey to techniques handling point clouds and meshes, making use of dictionaries whose definition depends on the input data, and enabling shape reconstruction or synthesis through the combination of atoms.     

C1 continuous h-adaptive least-squares spectral element method for phase-field models

To describe the interfacial dynamics between two phases using the phase-field method, the interfacial region needs to be close enough to a sharp interface so as to reproduce the correct physics. Due to the high gradients of the solution within the interfacial region and consequent high computational cost, the use of the phase-field method has been limited to the small-scale problems whose characteristic length is similar to the interfacial thickness. By using finer mesh at the interface and coarser mesh in the rest of computational domain, the phase-field methods can handle larger scale of problems with realistic interface thicknesses. In this work, a $C^1$ continuous $h$-adaptive mesh refinement technique with the least-squares spectral element method is presented. It is applied to the Navier–Stokes-Cahn–Hilliard (NSCH) system and the isothermal Navier–Stokes–Korteweg (NSK) system. Hermite polynomials are used to give global differentiability in the approximated solution, and a space–time coupled formulation and the element-by-element technique are implemented. Two refinement strategies based on the solution gradient and the local error estimators are suggested, and they are compared in two numerical examples.     

Random Pattern and Frequency Generation Using a Photonic Reservoir Computer with Output Feedback

Reservoir computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementations matches other digital algorithms on a series of benchmark tasks. Their potential can be further increased by feeding the output signal back into the reservoir, which would allow to apply the algorithm to time series generation. This requires, in principle, implementing a sufficiently fast readout layer for real-time output computation. Here we achieve this with a digital output layer driven by a FPGA chip. We demonstrate the first opto-electronic reservoir computer with output feedback and test it on two examples of time series generation tasks: frequency and random pattern generation. We obtain very good results on the first task, similar to idealised numerical simulations. The performance on the second one, however, suffers from the experimental noise. We illustrate this point with a detailed investigation of the consequences of noise on the performance of a physical reservoir computer with output feedback. Our work thus opens new possible applications for analogue reservoir computing and brings new insights on the impact of noise on the output feedback.