Agile Software Development

By L. Williams, J. Tomayko. Computer Science Education, 2002

3,191 citations recorded by the source corpus.

This focus section contains papers related to agile software development. The papers address a range of research areas including the application of agile methods to safety critical software development, the relationship of agile development with user experience design and how to measure flow in lean software development.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/6713bcd188e7224cb0b0d3f4df27b05d771b4fc1

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Machine learning with operational costs (2011, background): “The simultaneous process also has a resemblance to transductive learning (see Zhu, 2007), whose goal is to produce the output labels on the set of unlabeled examples; in this case, we produce a function (namely the operational cost) applied to those output labels.”
Segmentation by transduction (2008, background): “Note that, strictly speaking, semi-supervised tasks [28] are slightly different to transductive learning since instead of starting with a labelled training set and a set of input test points, semi-supervised learning methods start with a labelled and unlabelled training set and do not know in advanc”
Semi-Supervised Learning with Spectral Graph Wavelets (2011, methodology): “Keywords— Sparse approximation, spectral graph theory, structured sparsity, transductive regression, wavelets”
Lifelong Learning CRF for Supervised Aspect Extraction (2017, background): “Note that L-CRF is different from semisupervised learning (Zhu, 2005) as the n previous (unlabeled) domain data used in extraction are not used or not available during model training.”
Agile modeling: effective practices for extreme programming and the unified process (2002, methodology): “Other people may learn about your architecture, perhaps through osmotic communication (Cockburn 2002) described in Chapter 8, “Communication,” as they walk by the figure, and perhaps you’ll choose to show the diagram to your project stakeholders to help you explain your approach (a good example of a”
Unsupervised Large Graph Embedding (2017, methodology): “…based learning methods, e.g. hashing (Li, Hu, and Nie 2017), clustering (Ng, Jordan, and Weiss 2001), semi-supervised learning (Zhou et al. 2003; Zhu 2008), dimensionality reduction (Nie et al. 2011), RBF networks (Schwenker, Kestler, and Palm 2001), etc. Anchor-based Similarity Matrix Let U〈i〉…”
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning (2016, background): “ﬁer. We show through numerous experiments that this combination leads to a competitive semi-supervised learning method. 2 Related Work There are many approaches to semi-supervised learning in general [10, 11]. Self-training and cotraining [12, 13] are two well-known classic examples. In self-trainin”
Software release planning: an evolutionary and iterative approach (2004, background): “These advantages have particularly been capitalized on in agile methods [5].”
Semi-supervised inference: General theory and estimation of means (2016, neutral): “2538 Zhu (2008), Zhu and Goldberg (2009)].”
Robustness to Adversarial Perturbations in Learning from Incomplete Data (2019, methodology): “See [14] for a review on classical SSL approaches.”
Non-negative low rank and sparse graph for semi-supervised learning (2012, background): “Semisupervised learning (SSL) can utilize both limited labeled samples and rich yet unlabeled samples, and has recently received considerable attention in computer vision and machine learning communities [22].”
Feature-based transfer learning with real-world applications (2010, background): “Note that in traditional semi-supervised learning settings [233, 34], the labeled and unlabeled data are from the same domain.”
Discriminative Semi-Supervised Feature Selection Via Manifold Regularization (2009, methodology): “This motivates us to introduce semisupervised learning [9], [68] into the feature selection process.”
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, background): “and a large amount of unlabeled data [32].”
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints (2011, methodology): “used in numerous semi-supervised learning techniques, such as [15], and in many other semi- supervised learning works [ 26 ].”
Combining Active Learning and Semi-supervised Learning Using Local and Global Consistency (2014, methodology): “Two typical methods to address this problem are semi-supervised learning [1] and active learning [2].”