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1. Grammar Filtering for Syntax-Guided Synthesis

   https://doi.org/10.1609/aaai.v34i02.5522  

   Morton, Kairo ; Hallahan, William ; Shum, Elven ; Piskac, Ruzica ; Santolucito, Mark ( June 2020 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Programming-by-example (PBE) is a synthesis paradigm that allows users to generate functions by simply providing input-output examples. While a promising interaction paradigm, synthesis is still too slow for realtime interaction and more widespread adoption. Existing approaches to PBE synthesis have used automated reasoning tools, such as SMT solvers, as well as works applying machine learning techniques. At its core, the automated reasoning approach relies on highly domain specific knowledge of programming languages. On the other hand, the machine learning approaches utilize the fact that when working with program code, it is possible to generate arbitrarily large training datasets. In this work, we propose a system for using machine learning in tandem with automated reasoning techniques to solve Syntax Guided Synthesis (SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural network, we can use a data driven approach to reduce the size of the search space, then allow automated reasoning-based solvers to more quickly find a solution analytically. Our system is able to run atop existing SyGuS PBE synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS Competition for the PBE Strings track by 47.65% to outperform all of the competing tools. 

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2. LooPy: interactive program synthesis with control structures

   https://doi.org/10.1145/3485530  

   Ferdowsifard, Kasra ; Barke, Shraddha ; Peleg, Hila ; Lerner, Sorin ; Polikarpova, Nadia ( October 2021 , Proceedings of the ACM on Programming Languages)

   One vision for program synthesis, and specifically for programming by example (PBE), is an interactive programmer's assistant, integrated into the development environment. To make program synthesis practical for interactive use, prior work on Small-Step Live PBE has proposed to limit the scope of synthesis to small code snippets, and enable the users to provide local specifications for those snippets. This paradigm, however, does not work well in the presence of loops. We present LooPy, a synthesizer integrated into a live programming environment, which extends Small-Step Live PBE to work inside loops and scales it up to synthesize larger code snippets, while remaining fast enough for interactive use. To allow users to effectively provide examples at various loop iterations, even when the loop body is incomplete, LooPy makes use of live execution , a technique that leverages the programmer as an oracle to step over incomplete parts of the loop. To enable synthesis of loop bodies at interactive speeds, LooPy introduces Intermediate State Graph , a new data structure, which compactly represents a large space of code snippets composed of multiple assignment statements and conditionals. We evaluate LooPy empirically using benchmarks from competitive programming and previous synthesizers, and show that it can solve a wide variety of synthesis tasks at interactive speeds. We also perform a small qualitative user study which shows that LooPy's block-level specifications are easy for programmers to provide. 

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3. Pinpoint: A Record, Replay, and Extract System to Support Code Comprehension and Reuse

   https://doi.org/10.1109/VL/HCC53370.2022.9833105  

   Wang, Wengran ; Fraser, Gordon ; Bobbadi, Mahesh ; Tabarsi, Benyamin T. ; Barnes, Tiffany ; Martens, Chris ; Jiao, Shuyin ; Price, Thomas ( September 2022 , Proceedings of the Visual Languages and Human-centered Computing Conference (VL/HCC))

   Block-based programming environments, such as Scratch and Snap!, engage users to create programming artifacts such as games and stories, and share them in an online community. Many Snap! users start programming by reusing and modifying an example project, but encounter many barriers when searching and identifying the relevant parts of the program to learn and reuse. We present Pinpoint, a system that helps Snap! programmers understand and reuse an existing program by isolating the code responsible for specific events during program execution. Specifically, a user can record an execution of the program (including user inputs and graphical output), replay the output, and select a specific time interval where the event of interest occurred, to view code that is relevant to this event. We conducted a small-scale user study to compare users’ program comprehension experience with and without Pinpoint, and found suggestive evidence that Pinpoint helps users understand and reuse a complex program more efficiently. 

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4. Phononic Bandgap Programming in Kirigami By Unique Mechanical Input Sequencing

   https://doi.org/10.1002/admt.202202129  

   Khosravi, Hesameddin ; Li, Suyi ( April 2023 , Advanced Materials Technologies)

   This study investigates the programming of elastic wave propagation bandgaps in periodic and multi‐stable metamaterials by intentionally and uniquely sequencing its constitutive mechanical bits. To this end, stretched kirigami is used as a simple and versatile testing platform. Each mechanical bit in the stretched kirigami can switch between two stable equilibria with different external shapes (aka. “(0)” and “(1)” states). Therefore, by designing the sequence of (0) and (1) bits, one can fundamentally change the underlying periodicity and thus program the phononic bandgap frequencies. This study develops an algorithm to identify the unique periodicities generated by assembling “n‐bit strings” consisting ofnmechanical bits. Based on a simplified geometry of thesen‐bit strings, this study also formulates a theory to uncover the rich mapping between input sequencing and output bandgaps. The theoretical prediction and experiment results confirm that the (0) and (1) bit sequencing is effective for programming the phonic bandgap frequencies. Moreover, one can additionally fine‐tune the bandgaps by adjusting the global stretch. Overall, the results of this study elucidate new strategies for programming the dynamic responses of architected material systems.

    

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5. COAWST model simulations of warm and cool nearshore rip-current plumes

   https://doi.org/10.5281/zenodo.4272080  

   Moulton, Melissa ( January 2020 , Zenodo)

   This archive contains COAWST model input, grids and initial conditions, and output used to produce the results in a submitted manuscript. The files are:

   model_input.zip: input files for simulations presented in this paper
     ocean_rip_current.in: ROMS ocean model input file
     swan_rip_current.in: SWAN wave model input file (example with Hs=1m)
     coupling_rip_current.in: model coupling file
     rip_current.h: model header file
     
   model_grids_forcing.zip: bathymetry and initial condition files
        hbeach_grid_isbathy_2m.nc: ROMS bathymetry input file
        hbeach_grid_isbathy_2m.bot: SWAN bathymetry input file
        hbeach_grid_isbathy_2m.grd: SWAN grid input file
        hbeach_init_isbathy_14_18_17.nc: Initial temperature, cool surf zone dT=-1C case
        hbeach_init_isbathy_14_18_19.nc: Initial temperature, warm surf zone dT=+1C case
        hbeach_init_isbathy_14_18_16.nc: Initial temperature, cool surf zone dT=-2C case
        hbeach_init_isbathy_14_18_20.nc: Initial temperature, warm surf zone dT=+2C case
        hbeach_init_isbathy_14_18_17p5.nc: Initial temperature, cool surf zone dT=-0.5C case
        hbeach_init_isbathy_14_18_18p5.nc: Initial temperature, warm surf zone dT=+0.5C case

   model_output files: model output used to produce the figures
        netcdf files, zipped
        variables included:
             x_rho (cross-shore coordinate, m)
             y_rho (alongshore coordinate, m)
             z_rho (vertical coordinate, m)
             ocean_time (time since initialization, s, output every 5 mins)
             h (bathymetry, m)
             temp (temperature, Celsius)
             dye_02 (surfzone-released dye)
             Hwave (wave height, m)
             Dissip_break (wave dissipation W/m2) 
             ubar (cross-shore depth-average velocity, m/s, interpolated to rho-points)
        Case_141817.nc: cool surf zone dT=-1C Hs=1m
        Case_141819.nc: warm surf zone dT=+1C Hs=1m
        Case_141816.nc: cool surf zone dT=-2C Hs=1m
        Case_141820.nc: warm surf zone dT=-2C Hs=1m
        Case_141817p5.nc: cool surf zone dT=-0.5C Hs=1m
        Case_141818p5.nc: warm surf zone dT=+0.5C Hs=1m
        Case_141817_Hp5.nc: cool surf zone dT=-1C Hs=0.5m
        Case_141819_Hp5.nc: warm surf zone dT=+1C Hs=0.5m
        Case_141817_Hp75.nc: cool surf zone dT=-1C Hs=0.75m
        Case_141819_Hp75.nc: warm surf zone dT=+1C Hs=0.75m

   COAWST is an open source code and can be download at https://coawstmodel-trac.sourcerepo.com/coawstmodel_COAWST/. Descriptions of the input and output files can be found in the manual distributed with the model code and in the glossary at the end of the ocean.in file.

   Corresponding author: Melissa Moulton, mmoulton@uw.edu

    

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