Inductive Synthesis Research Articles

Superfusion: Eliminating Intermediate Data Structures via Inductive Synthesis

Superfusion: Eliminating Intermediate Data Structures via Inductive Synthesis

Semantic Code Refactoring for Abstract Data Types

Modifications to the data representation of an abstract data type (ADT) can require significant semantic refactoring of the code. Motivated by this observation, this paper presents a new method to automate semantic code refactoring tasks. Our method takes as input the original ADT implementation, a new data representation, and a so-called relational representation invariant (relating the old and new data representations), and automatically generates a new ADT implementation that is semantically equivalent to the original version. Our method is based on counterexample-guided inductive synthesis (CEGIS) but leverages three key ideas that allow it to handle real-world refactoring tasks. First, our approach reduces the underlying relational synthesis problem to a set of (simpler) programming-by-example problems, one for each method in the ADT. Second, it leverages symbolic reasoning techniques, based on logical abduction, to deduce code snippets that should occur in the refactored version. Finally, it utilizes a notion of partial equivalence to make inductive synthesis much more effective in this setting. We have implemented the proposed approach in a new tool called Revamp ‍ for automatically refactoring Java classes and evaluated it on 30 Java class mined from Github. Our evaluation shows that Revamp can correctly refactor the entire ADT in 97% of the cases and that it can successfully re-implement 144 out of the 146 methods that require modifications.

Verification of Diagnosability for Cyber-Physical Systems via Hybrid Barrier Certificates

In this paper, we provide an automata-based framework for verifying diagnosability property of Cyber-Physical Systems leveraging a notion of so-called hybrid barrier certificates. Concretely, we first construct a so-called (δ,K)-deterministic finite automata ((δ,K)-DFA) associated with the desired diagnosability property, which captures the occurrence of the fault to be diagnosed. Having a (δ,K)-DFA, we show that the verification of diagnosability properties is equivalent to a safety verification problem over a product system between this DFA and the dynamical system of interest. We further show that such a verification problem can be solved via computing hybrid barrier certificates for the product system. To compute the hybrid barrier certificates, we provide a systematic technique leveraging a counter-example guided inductive synthesis framework. Finally, we showcase the effectiveness of our results through a case study.

Multiplicative Barrier Certificates for Probabilistic Safety of Markov Jump Systems

This work offers a formal framework for providing safety certificates over discrete-time Markov jump systems (MJSs). Our proposed scheme centers around a new concept of multiplicative barrier certificates (MBCs), enabling us to establish a probabilistic lower bound for the safety property in infinite time horizons within this class of models. In particular, while a summative barrier certificate, as commonly proposed in the relevant literature, may not be available to ensure the safety of MJS models, we introduce an alternative approach in a multiplicative form, as opposed to the conventional “decreasing in mean” formulation. We demonstrate that in scenarios where a summative barrier does not exist, there is potential for the existence of a multiplicative counterpart. We also provide a systematic methodology grounded on the counterexample-guided inductive synthesis (CEGIS) scheme to systematically construct the proposed multiplicative certificates. We showcase the efficacy of our approach through its application in an air traffic management system.

Disruption of serotonin biosynthesis increases resistance to striped stem borer without changing innate defense response in rice.

Striped stem borer (SSB) is one of the most damaging pests in rice production worldwide. Previously, we preliminarily demonstrated that indica rice Jiazhe LM, an OsT5H (encoding tryptamine-5-hydroxylase) knockout mutant deficient in serotonin, had increased resistance to SSB as compared with its wildtype parent Jiazhe B. However, the full scenario of SSB resistance and the underlying mechanism remain unknown. In this study, we first demonstrated that the OsT5H knockout could generally increase rice resistance to SSB and then proved that the OsT5H knockout does not disrupt the innate defense response of rice plants to SSB infestation, that is, OsT5H knockout mutations neither had significant effect on the transcriptional response of defense genes upon SSB infestation, nor the profile of defense related metabolites and plant hormones, such as lignin, salicylic acid, jasmonic acid, and abscisic acid, nor the activity of reactive oxygen species(ROS) scavenging enzymes and the ROS contents. We then demonstrated that supplementation of serotonin promoted SSB growth and performance in artificial diet feeding experiments. We observed that SSB larvae feeding on Jiazhe B had serotonin 1.72- to 2.30-fold that of those feeding on Jiazhe LM at the whole body level, and more than 3.31 and 1.84 times in the hemolymph and head, respectively. Further studies showed that the expression of genes involved in serotonin biosynthesis and transport was ~88.1% greater in SSB larvae feeding on Jiahze LM than those feeding on Jiazhe B. These observations indicated that SSB increases serotonin synthesis when feeding on serotonin deficient rice but is unable to fully compensate the dietary serotonin deficiency. Put together, the present study strongly suggests that it is the deficiency of serotonin, not the secondary effect of OsT5H knockout on innate defense response confers the SSB resistance in rice, which implies that reducing serotonin level, particularly through inhibition of its inductive synthesis upon SSB damage, could be an efficient strategy for breeding SSB resistant varieties.

Trace-Guided Inductive Synthesis of Recursive Functional Programs

We propose a novel trace-guided approach to tackle the challenges of ambiguity and generalization in synthesis of recursive functional programs from input-output examples. Our approach augments the search space of programs with recursion traces consisting of recursive subcalls of the programs. Our method is based on a new version space algebra (VSA) for succinct representation and efficient manipulation of pairs of recursion traces and programs that are consistent with each other. We have implemented this approach in a tool called SyRup and evaluated it on benchmarks from prior work. Our evaluation demonstrates that SyRup not only requires fewer examples to achieve a certain success rate than existing synthesizers, but is also less sensitive to the quality of the examples.

Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. In this paper, we propose a novel approach to inductive program synthesis via iterative forward-backward abstract interpretation. The forward abstract interpretation computes possible outputs of a program given inputs, while the backward abstract interpretation computes possible inputs of a program given outputs. By iteratively performing the two abstract interpretations in an alternating fashion, we can effectively determine if any completion of each partial program as a candidate can satisfy the input-output examples. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a wide range of inductive synthesis tasks. We have implemented our approach and evaluated it on a set of benchmarks from the prior work. The experimental results show that our approach significantly outperforms the state-of-the-art approaches thanks to the sophisticated abstract interpretation techniques.

Reconceptualising innovation failure

This study examines the concept of innovation failure. It is a problematic subject without an accepted definition. For different stakeholders the same innovation can be both a success and a failure at the same time. The academic literature has concentrated on the determinants of innovation success. Yet, there is a notable lack of academic literature that deals with innovation failure as a topic in its own right. As a result, there is limited attention to, and little consensus on, the meaning of innovation failure. Existing definitions imply a highly contingent conceptualisation of innovation failure informed by the different theoretical framings and disciplinary interests of the researchers. We adopt a systematic literature review methodology that examines the concept of innovation failure at the level of the firm and from an innovation management perspective. The findings of this review are based on a total of 69 peer-reviewed articles from 1977 to 2021. We find the concept is widely used yet poorly defined and frequently lacks any theoretical underpinning. By means of a theory-building inductive synthesis our findings contribute to research by reconceptualising the concept of innovation failure along three processual dimensions: failure-as-experimentation; −judgement and -event.

Automated Translation of Functional Big Data Queries to SQL

Big data analytics frameworks like Apache Spark and Flink enable users to implement queries over large, distributed databases using functional APIs. In recent years, these APIs have grown in popularity because their functional interfaces abstract away much of the minutiae of distributed programming required by traditional query languages like SQL. However, the convenience of these APIs comes at a cost because functional queries are often less efficient than their SQL counterparts. Motivated by this observation, we present a new technique for automatically transpiling functional queries to SQL. While our approach is based on the standard paradigm of counterexample-guided inductive synthesis, it uses a novel column-wise decomposition technique to split the synthesis task into smaller subquery synthesis problems. We have implemented this approach as a new tool called RDD2SQL for translating Spark RDD queries to SQL and empirically evaluate the effectiveness of RDD2SQL on a set of real-world RDD queries. Our results show that (1) most RDD queries can be translated to SQL, (2) our tool is very effective at automating this translation, and (3) performing this translation offers significant performance benefits.

Improving Oracle-Guided Inductive Synthesis by Efficient Question Selection

Oracle-guided inductive synthesis (OGIS) is a widely-used framework to apply program synthesis techniques in practice. The question selection problem aims at reducing the number of iterations in OGIS by selecting a proper input for each OGIS iteration. Theoretically, a question selector can generally improve the performance of OGIS solvers on both interactive and non-interactive tasks if it is not only effective for reducing iterations but also efficient. However, all existing effective question selectors fail in satisfying the requirement of efficiency. To ensure effectiveness, they convert the question selection problem into an optimization one, which is difficult to solve within a short time. In this paper, we propose a novel question selector, named LearnSy . LearnSy is both efficient and effective and thus achieves general improvement for OGIS solvers for the first time. Since we notice that the optimization tasks in previous studies are difficult because of the complex behavior of operators, we estimate these behaviors in LearnSy as simple random events. Subsequently, we provide theoretical results for the precision of this estimation and design an efficient algorithm for its calculation. According to our evaluation, when dealing with interactive tasks, LearnSy can offer competitive performance compared to existing selectors while being more efficient and more general. Moreover, when working on non-interactive tasks, LearnSy can generally reduce the time cost of existing CEGIS solvers by up to 43.0%.

Inductive Synthesis of Structurally Recursive Functional Programs from Non-recursive Expressions

We present a novel approach to synthesizing recursive functional programs from input-output examples. Synthesizing a recursive function is challenging because recursive subexpressions should be constructed while the target function has not been fully defined yet. We address this challenge by using a new technique we call block-based pruning. A block refers to a recursion- and conditional-free expression (i.e., straight-line code) that yields an output from a particular input. We first synthesize as many blocks as possible for each input-output example, and then we explore the space of recursive programs, pruning candidates that are inconsistent with the blocks. Our method is based on an efficient version space learning, thereby effectively dealing with a possibly enormous number of blocks. In addition, we present a method that uses sampled input-output behaviors of library functions to enable a goal-directed search for a recursive program using the library. We have implemented our approach in a system called Trio and evaluated it on synthesis tasks from prior work and on new tasks. Our experiments show that Trio outperforms prior work by synthesizing a solution to 98% of the benchmarks in our benchmark suite.

From SMT to ASP: Solver-Based Approaches to Solving Datalog Synthesis-as-Rule-Selection Problems

Given a set of candidate Datalog rules, the Datalog synthesis-as-rule-selection problem chooses a subset of these rules that satisfies a specification (such as an input-output example). Building off prior work using counterexample-guided inductive synthesis, we present a progression of three solver-based approaches for solving Datalog synthesis-as-rule-selection problems. Two of our approaches offer some advantages over existing approaches, and can be used more generally to solve arbitrary SMT formulas containing Datalog predicates; the third—an encoding into standard, off-the-shelf answer set programming (ASP)—leads to significant speedups (∼ 9× geomean) over the state of the art while synthesizing higher quality programs. Our progression of solutions explores the space of interactions between SAT/SMT and Datalog, identifying ASP as a promising tool for working with and reasoning about Datalog. Along the way, we identify Datalog programs as monotonic SMT theories, which enjoy particularly efficient interactions in SMT; our plugins for popular SMT solvers make it easy to load an arbitrary Datalog program into the SMT solver as a custom monotonic theory. Finally, we evaluate our approaches using multiple underlying solvers to provide a more thorough and nuanced comparison against the current state of the art.

Counter-Example Guided Inductive Synthesis of Control Lyapunov Functions for Uncertain Systems

We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems affected by parametric uncertainty with arbitrary shape. In the CEGIS framework, a learner iteratively proposes a candidate control Lyapunov function and a tailored controller by solving a linear matrix inequality (LMI) feasibility problem, while a verifier either falsifies the current candidate by producing a counter-example to be considered at the next iteration, or it certifies that the tentative control Lyapunov function actually enjoys such feature. We investigate the Lipschitz continuity of the objective function of the global optimization problem solved by the verifier, which is key to establish the convergence of our method in a finite number of iterations. Numerical simulations confirm the effectiveness of the proposed approach.

Formal synthesis of non-fragile state-feedback digital controllers considering performance requirements for step response

This work describes an approach for synthesizing state-feedback controllers for discrete-time systems, taking into account performance aspects. The proposed methodology is based on counterexample-guided inductive synthesis (CEGIS), producing safe controllers based on step response performance requirements, such as settling time and maximum-overshoot. Controller candidates are generated through constrained optimization based on genetic algorithms. Each iteration that does not satisfy the initial system requirements is learned as a failed result and then used in another attempt. During the verification phase, it is considered the controller fragility to ensure deployable implementations. Such an approach assists the discrete-time control system design since weaknesses occur during implementation on digital platforms, where systems that meet design requirements are employed. The proposed method is implemented in DSVerifier, a tool that uses bounded (and unbounded) model checking based on satisfiability modulo theories. Experimental results showed that our approach is practical and sound regarding the synthesis of discrete state-feedback control systems that present performance requirements. It considers finite word-length effects, unlike other methods that routinely ignore them.

Compositional construction of control barrier functions for continuous-time stochastic hybrid systems

In this work, we propose a compositional framework for the construction of control barrier functions for networks of continuous-time stochastic hybrid systems enforcing complex logic specifications expressed by finite-state automata. The proposed scheme is based on a notion of so-called pseudo-barrier functions computed for subsystems, by employing which one can synthesize hybrid controllers for interconnected systems enforcing complex specifications over a finite-time horizon. Particularly, we first leverage sufficient small-gain type conditions to compositionally construct control barrier functions for interconnected systems based on the corresponding pseudo-barrier functions computed for subsystems. Then, using the constructed control barrier functions, we provide probabilistic guarantees on the satisfaction of given complex specifications in a bounded time horizon. In this respect, we decompose the given complex specification to simpler reachability tasks based on automata representing the complements of original finite-state automata. We then provide systematic approaches to solve those simpler reachability tasks by computing corresponding pseudo-barrier functions. Two different systematic techniques are provided based on (i) the sum-of-squares (SOS) optimization program and (ii) counter-example guided inductive synthesis (CEGIS) to search for pseudo-barrier functions of subsystems while synthesizing local controllers. We demonstrate the effectiveness of our proposed results by applying them to a fully-interconnected Kuramoto network of 100 nonlinear oscillators with Markovian switching signals.

Formal synthesis of closed-form sampled-data controllers for nonlinear continuous-time systems under STL specifications

We propose a counterexample-guided inductive synthesis framework for the formal synthesis of closed-form sampled-data controllers for nonlinear systems to meet STL specifications over finite-time trajectories. Rather than stating the STL specification for a single initial condition, we consider an (infinite and bounded) set of initial conditions. Candidate solutions are proposed using genetic programming, which evolves controllers based on a finite number of simulations. Subsequently, the best candidate is verified using reachability analysis; if the candidate solution does not satisfy the specification, an initial condition violating the specification is extracted as a counterexample. Based on this counterexample, candidate solutions are refined until eventually a solution is found (or a user-specified number of iterations is met). The resulting sampled-data controller is expressed as a closed-form expression, enabling both interpretability and the implementation in embedded hardware with limited memory and computation power. The effectiveness of our approach is demonstrated for multiple systems.

Veteran model of preventing community-acquired pressure injuries associated with spinal cord injury: A qualitative descriptive study

Context/Objective Pressure injuries (PrIs) are the second leading cause of hospitalization in people with spinal cord injury (SCI). The majority of PrIs occur in the community, but there is little known about prevention of community acquired PrI (CAPrI). The purpose of this study was to better understand risks, resources, and actions associated with CAPrI prevention from the perspective of veterans living with SCI in the community. Methods A qualitative descriptive design using photovoice (n = 30) with or without guided tours (n = 15) was conducted with Veterans living with SCI at 3 sites across the United States to determine environmental/lifestyle risks at home, prevention activities, resources used, and challenges in CAPrI prevention. Interviews were audio-recorded, transcribed and analyzed using thematic analysis, followed by inductive thematic synthesis. Results Qualitative analyses revealed a model describing veteran’s perspective of preventing CAPrIs associated with SCI. The model included 3 themes and 9 subthemes: Factors that Lead to Risk for Pressure Injury (Challenges and Barriers), Factors that Affect Chosen Actions (Veteran Motivators, Veteran Values, Veteran Satisfaction with Provider Relationships, and Veteran Supports), and Preventive Activities within Context of Life (Physical Care, Coping, Advocacy). Conclusion Incorporating the veteran model of prevention within the context of life into clinical care could support provider-veteran collaboration to identify and integrate successful strategies that prevent CAPrIs while also improving veteran quality of life.

Semantic programming by example with pre-trained models

The ability to learn programs from few examples is a powerful technology with disruptive applications in many domains, as it allows users to automate repetitive tasks in an intuitive way. Existing frameworks on inductive synthesis only perform syntactic manipulations, where they rely on the syntactic structure of the given examples and not their meaning. Any semantic manipulations, such as transforming dates, have to be manually encoded by the designer of the inductive programming framework. Recent advances in large language models have shown these models to be very adept at performing semantic transformations of its input by simply providing a few examples of the task at hand. When it comes to syntactic transformations, however, these models are limited in their expressive power. In this paper, we propose a novel framework for integrating inductive synthesis with few-shot learning language models to combine the strength of these two popular technologies. In particular, the inductive synthesis is tasked with breaking down the problem in smaller subproblems, among which those that cannot be solved syntactically are passed to the language model. We formalize three semantic operators that can be integrated with inductive synthesizers. To minimize invoking expensive semantic operators during learning, we introduce a novel deferred query execution algorithm that considers the operators to be oracles during learning. We evaluate our approach in the domain of string transformations: the combination methodology can automate tasks that cannot be handled using either technologies by themselves. Finally, we demonstrate the generality of our approach via a case study in the domain of string profiling.

UDF to SQL translation through compositional lazy inductive synthesis

Many data processing systems allow SQL queries that call user-defined functions (UDFs) written in conventional programming languages. While such SQL extensions provide convenience and flexibility to users, queries involving UDFs are not as efficient as their pure SQL counterparts that invoke SQL’s highly-optimized built-in functions. Motivated by this problem, we propose a new technique for translating SQL queries with UDFs to pure SQL expressions. Unlike prior work in this space, our method is not based on syntactic rewrite rules and can handle a much more general class of UDFs. At a high-level, our method is based on counterexample-guided inductive synthesis (CEGIS) but employs a novel compositional strategy that decomposes the synthesis task into simpler sub-problems. However, because there is no universal decomposition strategy that works for all UDFs, we propose a novel lazy inductive synthesis approach that generates a sequence of decompositions that correspond to increasingly harder inductive synthesis problems. Because most realistic UDF-to-SQL translation tasks are amenable to a fine-grained decomposition strategy, our lazy inductive synthesis method scales significantly better than traditional CEGIS. We have implemented our proposed technique in a tool called CLIS for optimizing Spark SQL programs containing Scala UDFs. To evaluate CLIS, we manually study 100 randomly selected UDFs and find that 63 of them can be expressed in pure SQL. Our evaluation on these 63 UDFs shows that CLIS can automatically synthesize equivalent SQL expressions in 92% of the cases and that it can solve 2.4× more benchmarks compared to a baseline that does not use our compositional approach. We also show that CLIS yields an average speed-up of 3.5× for individual UDFs and 1.3× to 3.1× in terms of end-to-end application performance.

Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs

The paper introduces a method for adaptive deductive synthesis of state models, of complex objects, with multilevel variable structures. The method makes it possible to predict the state of objects using the data coming from them. The data from the objects are collected with sensors installed on them. Multilevel knowledge graphs (KG) are used to describe the observed objects. The new adaptive synthesis method develops previously proposed inductive and deductive synthesis methods, allowing the context to be taken into account when predicting the states of the monitored objects based on the data obtained from them. The article proposes the algorithm for the suggested method and presents its computational complexity analysis. The software system, based on the proposed method, and the algorithm for multilevel adaptive synthesis of the object models developed, are described in the article. The effectiveness of the proposed method is shown in the results from modeling the states of telecommunication networks of cable television operators.