Search for: All records

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. 

When managing wide-area networks, network architects must decide how to balance multiple conflicting metrics, and ensure fair allocations to competing traffic while prioritizing critical traffic. The state of practice poses challenges since architects must precisely encode their intent into formal optimization models using abstract notions such as utility functions, and ad-hoc manually tuned knobs. In this paper, we present the first effort to synthesize optimal network designs with indeterminate objectives using an interactive program-synthesis-based approach. We make three contributions. First, we present comparative synthesis, an interactive synthesis framework which produces near-optimal programs (network designs) through two kinds of queries (Validate and Compare), without an objective explicitly given. Second, we develop the first learning algorithm for comparative synthesis in which a voting-guided learner picks the most informative query in each iteration. We present theoretical analysis of the convergence rate of the algorithm. Third, we implemented Net10Q, a system based on our approach, and demonstrate its effectiveness on four real-world network case studies using black-box oracles and simulation experiments, as well as a pilot user study comprising network researchers and practitioners. Both theoretical and experimental results show the promise of our approach. 

Constraint-based program synthesis techniques have been widely used in numerous settings. However, synthesizing programs that use libraries remains a major challenge. To handle complex or black-box libraries, the state of the art is to provide carefully crafted mocks or models to the synthesizer, requiring extra manual work. We address this challenge by proposing TOSHOKAN, a new synthesis framework as an alternative approach in which library-using programs can be generated without any user-provided artifacts at the cost of moderate performance overhead. The framework extends the classic counterexample-guided synthesis framework with a bootstrapping, log-based library model. The model collects input-output samples from running failed candidate programs on witness inputs. We prove that the framework is sound when a sound, bounded verifier is available, and also complete if the underlying synthesizer and verifier promise to produce minimal outputs. We implement and incorporate the framework to JSKETCH, a Java sketching tool. Experiments show that TOSHOKAN can successfully synthesize programs that use a variety of libraries, ranging from mathematical functions to data structures. Comparing to state-of-the-art synthesis algorithms which use mocks or models, TOSHOKAN reduces up to 159 lines of code of required manual inputs, at the cost of less than 40s of performance overheads.