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Program synthesis aims at the automatic generation of programs based on given specifications. Despite significant progress, the inherent complexity of synthesis tasks and the interplay among intention, invention and adaptation limit its scope. A promising yet challenging avenue is the integration of concurrency to enhance synthesis algorithms. While some efforts have applied basic concurrency by parallelizing search spaces, more intricate synthesis scenarios involving interdependent subproblems remain unexplored. In this paper, we focus on string transformation as the target domain and introduce the first concurrent synthesis algorithm that enables asynchronous coordination between deductive and enumerative processes, featuring an asynchronous deducer for dynamic task decomposition, a versatile enumerator for resolving enumeration requests, and an accumulative case splitter for if-then-else condition/branch search and assembling. Our implementation, Synthphonia exhibits substantial performance improvements over state-of-the-art synthesizers, successfully solving 116 challenging string transformation tasks for the first time. 

Software-defined networking (SDN) in conjunction with programmable switches revolutionizes network management, yet crafting optimal switch configurations remains complex. Traditional P4 optimizations rely on data plane level tuning. In this paper, we argue an essential piece for such optimizations is the control plane itself. We present P4CGO, a P4 compilation framework which focuses on realizing specifications based on control policies. P4CGO leverages user-defined objective functions and control plane policies to guide P4 program optimization through table merging and splitting. We have prototyped P4CGO and applied it solving real-world policy optimization problems. 

Syntax-guided synthesis has been a prevalent theme in various computer-aided programming systems. However, the domain of bit-vector synthesis poses several unique challenges that have not yet been sufficiently addressed and resolved. In this paper, we propose a novel synthesis approach that incorporates a distinct enumeration strategy based on various factors. Technically, this approach weighs in subexpression recurrence by term-graph-based enumeration, avoids useless candidates by example-guided filtration, prioritizes valuable components identified by large language models. This approach also incorporates a bottom-up deduction step to enhance the enumeration algorithm by considering subproblems that contribute to the deductive resolution. We implement all the enhanced enumeration techniques in our SyGuS solver DryadSynth, which outperforms state-of-the-art solvers in terms of the number of solved problems, execution time, and solution size. Notably, DryadSynthsuccessfully solved 31 synthesis problems for the first time, including 5 renowned Hacker’s Delight problems.