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Novice programmers often struggle with code understanding and debugging. Live Programming environments visualize the runtime values of a program each time it is modified to provide immediate feedback, which help with tracing the program execution. This paper presents the use of a Live Programming tool in a CS1 course to better understand the impact of Live Programming on novices’ learning metrics and their perceptions of the tool. We conducted a within-subjects study at a large public university in a CS1 course in Python (N=237) where students completed tasks in a lab setting, in some cases with a Live Programming environment, and in some cases without. Through post-lab surveys and open-ended feedback, we measured how well students understood the material and how students perceived the programming environment. To understand the impact of Live Programming, we compared the collected data for students who used Live Programming with the data for students who did not. We found that while learning outcomes were the same regardless of whether Live Programming was used or not, students who used the Live Programming tool completed some code tracing tasks faster. Furthermore, students liked the Live Programming environment more, and rated it as more helpful for their learning. 

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.