Anvik, John

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    Program wars: a card game for learning programming and cybersecurity concepts
    (ACM, 2019) Anvik, John; Cote, Vincent; Riehl, Jace
    Although there are many computer science learning games with the goal of teaching programming, such games typically require the person to either learn an existing programming language or the game's own specialized language. This can be intimidating, confusing or frustrating for an individual when they cannot get their "program" to work correctly (e.g. syntax error, infinite loop). Additionally, such games commonly use a puzzle-solving approach that does not appeal to some demographics. This paper presents a programming-language-independent approach to teaching fundamental programming and cybersecurity concepts using simple vocabulary. This approach also uses the familiar activity of playing cards against opponents to create a more dynamic and engaging learning experience. The approach is demonstrated by a web-based game called Program Wars. Results from a user study show that players are able to effectively connect game concepts to actual programming language structures; however, whether players' comprehension of computer programming is improved is unclear.
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    Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations
    (Oxford University Press, 2004) Szafron, Duane; Lu, Paul; Greiner, Russell; Wishart, David S.; Poulin, Brett; Eisner, Roman; Lu, Zhiyong; Anvik, John; Macdonell, Cam; Fyshe, Alona; Meeuwis, David
    Proteome Analyst (PA) ( is a publicly available, high-throughput, web-based system for predicting various properties of each protein in an entire proteome. Using machine-learned classifiers, PA can predict, for example, the GeneQuiz general function and Gene Ontology (GO) molecular function of a protein. In addition, PA is currently the most accurate and most comprehensive system for predicting subcellular localization, the location within a cell where a protein performs its main function. Two other capabilities of PA are notable. First, PA can create a custom classifier to predict a new property, without requiring any programming, based on labeled training data (i.e. a set of examples, each with the correct classification label) provided by a user. PA has been used to create custom classifiers for potassium-ion channel proteins and other general function ontologies. Second, PA provides a sophisticated explanation feature that shows why one prediction is chosen over another. The PA system produces a Naïve Bayes classifier, which is amenable to a graphical and interactive approach to explanations for its predictions; transparent predictions increase the user's confidence in, and understanding of, PA.
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    Evaluating an assistant for creating bug report assignment recommenders
    (2016) Anvik, John
    Software development projects receive many change requests each day and each report must be examined to decide how the request will be handled by the project. One decision that is frequently made is to which software developer to assign the change request. Efforts have been made toward semi automating this decision,with most approaches using machine learning algorithms. However, using machine learning to create an assignment recommender is a complex process that must be tailored to each individual software development project. The Creation Assistant for Easy Assignment (CASEA) tool leverages a project member’s knowledge for creating an assignment recommender. This paper presents the results of a user study using CASEA. The user study shows that users with limited project knowledge can quickly create accurate bug report assignment recommenders.