Russell Zaretzki

Horacio Silva-Saravia, Hector Pulgar-Painemal, Russell Zaretzki

This paper proposes a new probabilistic energy-based method to determine the optimal installation location of electronically-interfaced resources (EIRs) considering dynamic reinforcement under wind variability in systems with high penetration of wind power. The oscillation energy and total action are used to compare the dynamic performance for different EIR locations. A linear approximation of the total action critically reduces the computational time from hours to minutes. Simulating an IEEE-39 bus system with 30% of power generation sourced from wind, a chance-constrained optimization is carried out to decide the location of an energy storage system (ESS) adding damping to the system oscillations. The results show that the proposed method, selecting the bus location that guarantees the best dynamic performance with highest probability, is superior to both traditional dominant mode analysis and arbitrary benchmarks for damping ratios.

Yuxing Ma, Tapajit Dey, Chris Bogart et al.

Open source software (OSS) is essential for modern society and, while substantial research has been done on individual (typically central) projects, only a limited understanding of the periphery of the entire OSS ecosystem exists. For example, how are the tens of millions of projects in the periphery interconnected through. technical dependencies, code sharing, or knowledge flow? To answer such questions we: a) create a very large and frequently updated collection of version control data in the entire FLOSS ecosystems named World of Code (WoC), that can completely cross-reference authors, projects, commits, blobs, dependencies, and history of the FLOSS ecosystems and b) provide capabilities to efficiently correct, augment, query, and analyze that data. Our current WoC implementation is capable of being updated on a monthly basis and contains over 18B Git objects. To evaluate its research potential and to create vignettes for its usage, we employ WoC in conducting several research tasks. In particular, we find that it is capable of supporting trend evaluation, ecosystem measurement, and the determination of package usage. We expect WoC to spur investigation into global properties of OSS development leading to increased resiliency of the entire OSS ecosystem. Our infrastructure facilitates the discovery of key technical dependencies, code flow, and social networks that provide the basis to determine the structure and evolution of the relationships that drive FLOSS activities and innovation.

Yuxing Ma, Audris Mockus, Beth Milhollin et al.

Motivation: The question of what combination of attributes drives the adoption of a particular software technology is critical to developers. It determines both those technologies that receive wide support from the community and those which may be abandoned, thus rendering developers' investments worthless. Aim and Context: We model software technology adoption by developers and provide insights on specific technology attributes that are associated with better visibility among alternative technologies. Approach: We leverage social contagion theory and statistical modeling to identify, define, and test empirically measures that are likely to affect software adoption. More specifically, we leverage a large collection of open source version control repositories to construct a software dependency chain for a specific set of R language source-code files. We formulate logistic regression models, to investigate the combination of technological attributes that drive adoption among competing data frame implementations in the R language: tidy and data.table. We quantify key project attributes that might affect adoption and also characteristics of developers making the selection. Results: We find that a quick response to raised issues, a larger number of overall deployments, and a larger number of high-quality StackExchange questions are associated with higher adoption. Decision makers tend to adopt the technology that is closer to them in the technical dependency network and in author collaborations networks while meeting their performance needs. Future work: We hope that our methodology encompassing social contagion that captures both rational and irrational preferences and the elucidation of key measures from large collections of version control data provides a general path toward increasing visibility, driving better informed decisions, and producing more sustainable and widely adopted software

Sadika Amreen, Audris Mockus, Chris Bogart et al.

Graphs of developer networks are important for software engineering research and practice. For these graphs to realistically represent the networks, accurate developer identities are imperative. We aim to identify developer identity errors from open source software repositories in VCS, investigate the nature of these errors, design corrective algorithms, and estimate the impact of the errors on networks inferred from this data. We investigate these questions using over 1B Git commits with over 23M recorded author identities. By inspecting the author strings that occur most frequently, we group identity errors into categories. We then augment the author strings with 3 behavioral fingerprints: time-zone frequencies, the set of files modified, and a vector embedding of the commit messages. We create a manually validated set of identities for a subset of OpenStack developers using an active learning approach and use it to fit supervised learning models to predict the identities for the remaining author strings in OpenStack. We compare these predictions with a commercial effort and a leading research method. Finally, we compare network measures for file-induced author networks based on corrected and raw data. We find commits done from different environments, misspellings, organizational IDs, default values, and anonymous IDs to be the major sources of errors. We also find supervised learning methods to reduce errors by several times in comparison to existing methods and the active learning approach to be an effective way to create validated datasets and that correction of developer identity has a large impact on the inference of the social network. We believe that our proposed Active Learning Fingerprint Based Anti-Aliasing (ALFAA) approach will expedite research progress in the software engineering domain for applications that depend upon graphs of developers or other social networks.

Li He, Hairong Qi, Russell Zaretzki

In this paper, a color transfer framework to evoke different emotions for images based on color combinations is proposed. The purpose of this color transfer is to change the "look and feel" of images, i.e., evoking different emotions. Colors are confirmed as the most attractive factor in images. In addition, various studies in both art and science areas have concluded that other than single color, color combinations are necessary to evoke specific emotions. Therefore, we propose a novel framework to transfer color of images based on color combinations, using a predefined color emotion model. The contribution of this new framework is three-fold. First, users do not need to provide reference images as used in traditional color transfer algorithms. In most situations, users may not have enough aesthetic knowledge or path to choose desired reference images. Second, because of the usage of color combinations instead of single color for emotions, a new color transfer algorithm that does not require an image library is proposed. Third, again because of the usage of color combinations, artifacts that are normally seen in traditional frameworks using single color are avoided. We present encouraging results generated from this new framework and its potential in several possible applications including color transfer of photos and paintings.