Forming members of an organisation into coherent groups or teams is an important issue in any large-scale software engineering endeavour, especially so in agile software development where teams rely heavily on self-organisation and organisational flexibility. Butis there a recurrent organisational structure pattern in agile software engineering teams?and if sowhat does that pattern imply, in terms of software architecture quality?We address these questions using mixed-methods research in industry featuring interviews, surveys, and Delphi studies of real agile teams. In our study of 30 agile software teams we found that, out of seven organisational structure patterns that recur across our dataset, a single organisational pattern occurs over 37% of the time. This pattern: (a) reflects young communities (1-12 months old); (b) disappears in established ones (13+ months); and (c) reflects the highest number of architecture smells reported. Finally, we observe a negative correlation between a proposed organisational measure and architecture smells. On the one hand, these insights may serve to aid architects in designing not only their architectures but also their communities to best support their co-evolution. On the other hand, we observe that organisational structures in software engineering influence much more than simply software architectures, and we expect our results to lay the foundations of more structured and rigorous approaches to organisational structure studies and use in software engineering research and practice.

Optical coherence tomography (OCT) is a powerful method for imaging the retinal layers. In this paper, we develop a novel 3D fully convolutional deep architecture for automated segmentation of retinal layers in OCT scans. This model extracts features from both the spatial and the inter-frame dimensions by performing 3D convolutions, thereby capturing the information encoded in multiple adjacent frames. The proposed network is based on an encoder-decoder framework in which the convolution layers are interlaced with pooling layers in the encoder and with unpooling layers in the decoder, respectively. Consequently, a hierarchy of shrinking 3D feature maps are learned in the encoder and enlarged to the size of original input image for semantic segmentation in the decoder. The framework is validated on thirteen 3D OCTs captured by the Topcon 3D OCT with comparisons against two state-of-the-art segmentation methods including one recent 2D deep learning based approach to substantiate its effectiveness.

We present three new filtering algorithms for the Disjunctive constraint that all have a linear running time complexity in the number of tasks. The first algorithm filters the tasks according to the rules of the time tabling. The second algorithm performs an overload check that could also be used for the Cumulative constraint. The third algorithm enforces the rules of detectable precedences. The two last algorithms use a new data structure that we introduce and that we call the time line. This data structure provides many constant time operations that were previously implemented in logarithmic time by the Theta-tree data structure. Experiments show that these new algorithms are competitive even for a small number of tasks and outperform existing algorithms as the number of tasks increases.