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Library learning compresses a given corpus of programs by extracting common structure from the corpus into reusable library functions. Prior work on library learning suffers from two limitations that prevent it from scaling to larger, more complex inputs. First, it explores too many candidate library functions that are not useful for compression. Second, it is not robust to syntactic variation in the input. We propose library learning modulo theory (LLMT), a new library learning algorithm that additionally takes as input an equational theory for a given problem domain. LLMT uses e-graphs and equality saturation to compactly represent the space of programs equivalent modulo the theory, and uses a novel e-graph anti-unification technique to find common patterns in the corpus more directly and efficiently. We implemented LLMT in a tool named babble. Our evaluation shows that babble achieves better compression orders of magnitude faster than the state of the art. We also provide a qualitative evaluation showing that babble learns reusable functions on inputs previously out of reach for library learning. 

With the rise of software-as-a-service and microservice architectures, RESTful APIs are now ubiquitous in mobile and web applications. A service can have tens or hundreds of API methods, making it a challenge for programmers to find the right combination of methods to solve their task. We present APIphany, a component-based synthesizer for programs that compose calls to RESTful APIs. The main innovation behind APIphany is the use of precise semantic types, both to specify user intent and to direct the search. APIphany contributes three novel mechanisms to overcome challenges in adapting component-based synthesis to the REST domain: (1) a type inference algorithm for augmenting REST specifications with semantic types; (2) an efficient synthesis technique for “wrangling” semi-structured data, which is commonly required in working with RESTful APIs; and (3) a new form of simulated execution to avoid executing APIs calls during synthesis. We evaluate APIphany on three real-world APIs and 32 tasks extracted from GitHub repositories and StackOverflow. In our experiments, APIphany found correct solutions to 29 tasks, with 23 of them reported among top ten synthesis results. 

Abstract PCPDTBT‐SO₃K (CPE‐K), a conjugated polyelectrolyte, is presented as a mixed conductor material that can be used to fabricate high transconductance accumulation mode organic electrochemical transistors (OECTs). OECTs are utilized in a wide range of applications such as analyte detection, neural interfacing, impedance sensing, and neuromorphic computing. The use of interdigitated contacts to enable high transconductance in a relatively small device area in comparison to standard contacts is demonstrated. Such characteristics are highly desired in applications such as neural‐activity sensing, where the device area must be minimized to reduce invasiveness. The physical and electrical properties of CPE‐K are fully characterized to allow a direct comparison to other top performing OECT materials. CPE‐K demonstrates an electrical performance that is among the best reported in the literature for OECT materials. In addition, CPE‐K OECTs operate in the accumulation mode, which allows for much lower energy consumption in comparison to commonly used depletion mode devices.