Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems

[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated.Comment: 10 pages, 8 figures, accepted for the 39th International Conference on Software Engineering (ICSE'17

Classifying Relations by Ranking with Convolutional Neural Networks

Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN). We propose a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes. We perform experiments using the the SemEval-2010 Task 8 dataset, which is designed for the task of classifying the relationship between two nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results show that: (1) our approach is more effective than CNN followed by a softmax classifier; (2) omitting the representation of the artificial class Other improves both precision and recall; and (3) using only word embeddings as input features is enough to achieve state-of-the-art results if we consider only the text between the two target nominals.Comment: Accepted as a long paper in the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015

New method for calculating helicity amplitudes of jet--like QED processes for high--energy colliders I. Bremsstrahlung processes

Inelastic QED processes, the cross sections of which do not drop with increasing energy, play an important role at high-energy colliders. Such reactions have the form of two-jet processes with the exchange of a virtual photon in the t-channel. We consider them in the region of small scattering angles $m/E \lesssim \theta \ll 1$, which yields the dominant contribution to their total cross sections. A new effective method is presented and applied to QED processes with emission of real photons to calculate the helicity amplitudes of these processes. Its basic idea is similar to the well-known equivalent-lepton method. Compact analytical expressions for those amplitudes up to $e^8$ are derived omitting only terms of the order of $m^2/E^2, \theta^2$, $\theta m/E$ and higher order. The helicity amplitudes are presented in a compact form in which large compensating terms are already cancelled. Some common properties for all jet-like processes are found and we discuss their origin.Comment: 17 pages, LATEX (svjour style files included

Implementation and complexity of the watershed-from-markers algorithm computed as a minimal cost forest

The watershed algorithm belongs to classical algorithms in mathematical morphology. Lotufo et al. published a principle of the watershed computation by means of an Image Foresting Transform (IFT), which computes a shortest path forest from given markers. The algorithm itself was described for a 2D case (image) without a detailed discussion of its computation and memory demands for real datasets. As IFT cleverly solves the problem of plateaus and as it gives precise results when thin objects have to be segmented, it is obvious to use this algorithm for 3D datasets taking in mind the minimizing of a higher memory consumption for the 3D case without loosing low asymptotical time complexity of O(m+C) (and also the real computation speed). The main goal of this paper is an implementation of the IFT algorithm with a priority queue with buckets and careful tuning of this implementation to reach as minimal memory consumption as possible. The paper presents five possible modifications and methods of implementation of the IFT algorithm. All presented implementations keep the time complexity of the standard priority queue with buckets but the best one minimizes the costly memory allocation and needs only 19-45% of memory for typical 3D medical imaging datasets. Memory saving was reached by an IFT algorithm simplification, which stores more elements in temporary structures but these elements are simpler and thus need less memory. The best presented modification allows segmentation of large 3D medical datasets (up to 512x512x680 voxels) with 12-or 16-bits per voxel on currently available PC based workstations.Comment: v1: 10 pages, 6 figures, 7 tables EUROGRAPHICS conference, Manchester, UK, 2001. v2: 12 pages, reformated for letter, corrected IFT to "Image Foresting Tranform

Write Free or Die: Vol. 01, No. 02

Writing at UNH, Page 1 Upcoming Events, Page 1 Writing Committee Members, Page 2 Dangling Modifier, Page 2 Ask Patty, Page 3 Les Perelman, Page 4 Grammar Box, Page 4 Tom Newkirk and Self-Conferencing, Page 5 Notes on Oxford Comma, Page Page 6 Past Perfect, Page 9 Faculty Resources, Page

Optimal power harness routing for small-scale satellites

This paper presents an approach to optimal power harness design based on a modified ant colony optimisation algorithm. The optimisation of the harness routing topology is formulated as a constrained multi-objective optimisation problem in which the main objectives are to minimise the length (and therefore the mass) of the harness. The modified ant colony optimisation algorithm automatically routes different types of wiring, creating the optimal harness layout. During the optimisation the length, mass and bundleness of the cables are computed and used as cost functions. The optimisation algorithm works incrementally on a finite set of waypoints, forming a tree, by adding and evaluating one branch at a time, utilising a set of heuristics using the cable length and cable bundling as criteria to select the optimal path. Constraints are introduced as forbidden waypoints through which digital agents (hereafter called ants) cannot travel. The new algorithm developed will be applied to the design of the harness of a small satellite, with results highlighting the capabilities and potentialities of the code