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Domain-specific languages (DSLs) are prevalent across many application domains. Such languages let developers easily express computations using high-level abstractions that result in performant implementations. To leverage DSLs, however, application developers need to master the DSL’s syntax and manually rewrite existing code. Compilers can aid in this effort, but part of building a compiler requires transpiling code from the source code to the target DSL. Such transpilation is typically done via pattern-matching rules on the source code. Sadly, developing such rules is often a painstaking and error-prone process. In this paper, we describe our experience in using program synthesis to build code transpilers. To do so, we developed MetaLift, a new framework for building transpilers that transform general-purpose code into DSLs using program synthesis. To use MetaLift, transpiler developers first define the target DSL’s semantics using MetaLift’s specification language, and specify the search space for each input code fragment to be transpiled using MetaLift’s API. MetaLift then leverages program synthesizers and theorem provers to automatically find transpilations expressed in the target DSL that is provably semantic equivalent to the input code. We have used MetaLift to build three DSL transpilers targeting different programming models and application domains. Our results show that the MetaLift-based compilers can translate many benchmarks used in prior work created by specialized implementations, but can be built using orders-of-magnitude fewer lines of code as compared to prior work., LIPIcs, Vol. 263, 37th European Conference on Object-Oriented Programming (ECOOP 2023), pages 38:1-38:30

Automated synthesis of inductive invariants is an important problem in software verification. Once all the invariants have been specified, software verification reduces to checking of verification conditions. Although static analyses to infer invariants have been studied for over forty years, recent years have seen a flurry of data-driven invariant inference techniques which guess invariants from examples instead of analyzing program text. However, these techniques have been demonstrated to scale only to programs with a small number of variables. In this paper, we study these scalability issues and address them in our tool oasis that improves the scale of data-driven invariant inference and outperforms state-of-the-art systems on benchmarks from the invariant inference track of the Syntax Guided Synthesis competition.

This paper outlays constellation design and orbit formation for SmallSats for earth observation mission and review of Small Sat platforms presently used by government space organizations and aerospace corporation. The earth observation mission\'s focus is to cover primary disaster prone and densely populated areas. The paper primarily addresses the constellation formation and orbit layout designed in way to provide high revisit frequency, maximum coverage area per orbit and minimum repeat cycle and standard Small Sat platform review on which various types of COTS remote sensing equipment can be used depending on the type of earth observation mission.

We present a method for automatically generating repair feedback for syntax errors for introductory programming problems. Syntax errors constitute one of the largest classes of errors (34%) in our dataset of student submissions obtained from a MOOC course on edX. The previous techniques for generating automated feed- back on programming assignments have focused on functional correctness and style considerations of student programs. These techniques analyze the program AST of the program and then perform some dynamic and symbolic analyses to compute repair feedback. Unfortunately, it is not possible to generate ASTs for student pro- grams with syntax errors and therefore the previous feedback techniques are not applicable in repairing syntax errors. We present a technique for providing feedback on syntax errors that uses Recurrent neural networks (RNNs) to model syntactically valid token sequences. Our approach is inspired from the recent work on learning language models from Big Code (large code corpus). For a given programming assignment, we first learn an RNN to model all valid token sequences using the set of syntactically correct student submissions. Then, for a student submission with syntax errors, we query the learnt RNN model with the prefix to- ken sequence to predict token sequences that can fix the error by either replacing or inserting the predicted token sequence at the error location. We evaluate our technique on over 14, 000 student submissions with syntax errors. Our technique can completely re- pair 31.69% (4501/14203) of submissions with syntax errors and in addition partially correct 6.39% (908/14203) of the submissions.