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  1. Free, publicly-accessible full text available May 1, 2024
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  4. Writing and maintaining UI tests for mobile apps is a time-consuming and tedious task. While decades of research have produced auto- mated approaches for UI test generation, these approaches typically focus on testing for crashes or maximizing code coverage. By contrast, recent research has shown that developers prefer usage-based tests, which center around specific uses of app features, to help support activities such as regression testing. Very few existing techniques support the generation of such tests, as doing so requires automating the difficult task of understanding the semantics of UI screens and user inputs. In this paper, we introduce Avgust, which automates key steps of generating usage-based tests. Avgust uses neural models for image understanding to process video recordings of app uses to synthesize an app-agnostic state-machine encoding of those uses. Then, Avgust uses this encoding to synthesize test cases for a new target app. We evaluate Avgust on 374 videos of common uses of 18 popular apps and show that 69% of the tests Avgust generates successfully execute the desired usage, and that Avgust’s classifiers outperform the state of the art. 
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  5. Advances in how we build and use software, specifically the integration of machine learning for decision making, have led to widespread concern around model and software fairness. We present fairkit-learn, an interactive Python toolkit designed to support data scientists' ability to reason about and understand model fairness. We outline how fairkit-learn can support model training, evaluation, and comparison and describe the potential benefit that comes with using fairkit-learn in comparison to the state-of-the-art. Fairkit-learn is open source at https://go.gmu.edu/fairkit-learn/. 
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  6. Formally verified correctness is one of the most desirable properties of software systems. But despite great progress made via interactive theorem provers, such as Coq, writing proof scripts for verification remains one of the most effort-intensive (and often prohibitively difficult) software development activities. Recent work has created tools that automatically synthesize proofs or proof scripts. For example, CoqHammer can prove 26.6% of theorems completely automatically by reasoning using precomputed facts, while TacTok and ASTactic, which use machine learning to model proof scripts and then perform biased search through the proof-script space, can prove 12.9% and 12.3% of the theorems, respectively. Further, these three tools are highly complementary; together, they can prove 30.4% of the theorems fully automatically. Our key insight is that control over the learning process can produce a diverse set of models, and that, due to the unique nature of proof synthesis (the existence of the theorem prover, an oracle that infallibly judges a proof's correctness), this diversity can significantly improve these tools' proving power. Accordingly, we develop Diva, which uses a diverse set of models with TacTok's and ASTactic's search mechanism to prove 21.7% of the theorems. That is, Diva proves 68% more theorems than TacTok and 77% more than ASTactic. Complementary to CoqHammer, Diva proves 781 theorems (27% added value) that Coq-Hammer does not, and 364 theorems no existing tool has proved automatically. Together with CoqHammer, Diva proves 33.8% of the theorems, the largest fraction to date. We explore nine dimensions for learning diverse models, and identify which dimensions lead to the most useful diversity. Further, we develop an optimization to speed up Diva's execution by 40X. Our study introduces a completely new idea for using diversity in machine learning to improve the power of state-of-the-art proof-script synthesis techniques, and empirically demonstrates that the improvement is significant on a dataset of 68K theorems from 122 open-source software projects. 
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