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1. Developer Reputation Estimator (DRE)

   https://doi.org/10.1109/ASE.2019.00107  

   Amreen, Sadika; Karnauch, andrey; Mockus, Audris (November 2019, ASE '19: Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering)

   Evidence shows that developer reputation is extremely important when accepting pull requests or resolving reported issues. It is particularly salient in Free/Libre Open Source Software since the developers are distributed around the world, do not work for the same organization and, in most cases, never meet face to face. The existing solutions to expose developer reputation tend to be forge specific (GitHub), focus on activity instead of impact, do not leverage social or technical networks, and do not correct often misspelled developer identities. We aim to remedy this by amalgamating data from all public Git repositories, measuring the impact of developer work, expose developer's collaborators, and correct notoriously problematic developer identity data. We leverage World of Code (WoC), a collection of an almost complete (and continuously updated) set of Git repositories by first allowing developers to select which of the 34 million(M) Git commit author IDs belong to them and then generating their profiles by treating the selected collection of IDs as that single developer. As a side-effect, these selections serve as a training set for a supervised learning algorithm that merges multiple identity strings belonging to a single individual. As we evaluate the tool and the proposed impact measure, we expect to build on these findings to develop reputation badges that could be associated with pull requests and commits so developers could easier trust and prioritize them. 

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2. Unsupervised Feature Learning for Point Cloud Understanding by Contrasting and Clustering Using Graph Convolutional Neural Networks

   https://doi.org/10.1109/3DV.2019.00051  

   Zhang, Ling; Zhu, Zhigang (September 2019, Proceedings - 2019 International Conference on 3D Vision (3DV))

   To alleviate the cost of collecting and annotating large-scale "3D object" point cloud data, we propose an unsupervised learning approach to learn features from an unlabeled point cloud dataset by using part contrasting and object clustering with deep graph convolutional neural networks (GCNNs). In the contrast learning step, all the samples in the 3D object dataset are cut into two parts and put into a "part" dataset. Then a contrast learning GCNN (ContrastNet) is trained to verify whether two randomly sampled parts from the part dataset belong to the same object. In the cluster learning step, the trained ContrastNet is applied to all the samples in the original 3D object dataset to extract features, which are used to group the samples into clusters. Then another GCNN for clustering learning (ClusterNet) is trained from the orignal 3D data to predict the cluster IDs of all the training samples. The contrasting learning forces the ContrastNet to learn semantic features of objects, while the ClusterNet improves the quality of learned features by being trained to discover objects that belong to the same semantic categories by using cluster IDs. We have conducted extensive experiments to evaluate the proposed framework on point cloud classification tasks. The proposed unsupervised learning approach obtains comparable performance to the state-of-the-art with heavier shape auto-encoding unsupervised feature extraction methods. We have also tested the networks on object recognition using partial 3D data, by simulating occlusions and perspective views, and obtained practically useful results. The code of this work is publicly available at: https://github.com/lingzhang1/ContrastNet. 

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3. Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network

   Zhang, Ling; Zhu, Zhigang (June 2019, CVPR Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics)

   To alleviate the cost of collecting and annotating large- scale point cloud datasets for 3D scene understanding tasks, we propose an unsupervised learning approach to learn features from unlabeled point cloud ”3D object” dataset by using part contrasting and object clustering with deep graph neural networks (GNNs). In the contrast learn- ing step, all the samples in the 3D object dataset are cut into two parts and put into a ”part” dataset. Then a contrast learning GNN (ContrastNet) is trained to verify whether two randomly sampled parts from the part dataset belong to the same object. In the cluster learning step, the trained ContrastNet is applied to all the samples in the original 3D object dataset to extract features, which are used to group the samples into clusters. Then another GNN for cluster- ing learning (ClusterNet) is trained to predict the cluster IDs of all the training samples. The contrasting learning forces the ContrastNet to learn high-level semantic features of objects but probably ignores low-level features, while the ClusterNet improves the quality of learned features by be- ing trained to discover objects that belong to the same se- mantic categories by using cluster IDs. We have conducted extensive experiments to evaluate the proposed framework on point cloud classification tasks. The proposed unsupervised learning approach obtained comparable performance to the state-of-the-art unsupervised learning methods that used much more complicated network structures. The code and an extended version of this work is publicly available via: https://github.com/lingzhang1/ContrastNet 

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4. Detecting and Characterizing Bots that Commit Code

   https://doi.org/10.1145/3379597.3387478  

   Dey, Tapajit; Mousavi, Sara; Ponce, Eduardo; Fry, Tanner; Vasilescu, Bogdan; Filippova, Anna; Mockus, Audris (June 2020, IEEE International Working Conference on Mining Software Repositories)

   Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such automation tools as bots and, in many software mining scenarios related to developer productivity or code quality it is desirable to identify bots in order to separate their actions from actions of individuals. Aim: Find an automated way of identifying bots and code committed by these bots, and to characterize the types of bots based on their activity patterns. Method and Result: We propose BIMAN, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the ommits. For our test data, the value for AUC-ROC was 0.9. We also characterized these bots based on the time patterns of their code commits and the types of files modified, and found that they primarily work with documentation files and web pages, and these files are most prevalent in HTML and JavaScript ecosystems. We have compiled a shareable dataset containing detailed information about 461 bots we found (all of whom have more than 1000 commits) and 13,762,430 commits they created. 

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5. StarCraftImage: A Dataset for Prototyping Spatial Reasoning Methods for Multi-Agent Environments

   https://doi.org/10.1109/CVPR52729.2023.02107  

   Kulinski, Sean; Waytowich, Nicholas R.; Hare, James Z.; Inouye, David I. (January 2023, IEEE Conference on Computer Vision and Pattern Recognition)

   Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e.g., autonomous surveillance over sensor networks and subtasks for reinforcement learning (RL)). StarCraft II game replays encode intelligent (and adversarial) multiagent behavior and could provide a testbed for these tasks; however, extracting simple and standardized representations for prototyping these tasks is laborious and hinders reproducibility. In contrast, MNIST and CIFAR10, despite their extreme simplicity, have enabled rapid prototyping and reproducibility of ML methods. Following the simplicity of these datasets, we construct a benchmark spatial reasoning dataset based on StarCraft II replays that exhibit complex multi-agent behaviors, while still being as easy to use as MNIST and CIFAR10. Specifically, we carefully summarize a window of 255 consecutive game states to create 3.6 million summary images from 60,000 replays, including all relevant metadata such as game outcome and player races. We develop three formats of decreasing complexity: Hyperspectral images that include one channel for every unit type (similar to multispectral geospatial images), RGB images that mimic CIFAR10, and grayscale images that mimic MNIST. We show how this dataset can be used for prototyping spatial reasoning methods. All datasets, code for extraction, and code for dataset loading can be found at https://starcraftdata.davidinouye.com/. 

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