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In their seminal work, Atserias et al. and independently Pipatsrisawat and Darwiche in 2009 showed that CDCL solvers can simulate resolution proofs with polynomial overhead. However, previous work does not address the tightness of the simulation, i.e., the question of how large this overhead needs to be. In this paper, we address this question by focusing on an important property of proofs generated by CDCL solvers that employ standard learning schemes, namely that the derivation of a learned clause has at least one inference where a literal appears in both premises (aka, a merge literal). Specifically, we show that proofs of this kind can simulate resolution proofs with at most a linear overhead, but there also exist formulas where such overhead is necessary or, more precisely, that there exist formulas with resolution proofs of linear length that require quadratic CDCL proofs.

In this paper we present a Java-to-Python (J2P) and Python-to-Java (P2J) back-to-back code translation method, and associated tool called CoTran, based on large language models (LLMs). Our method leverages the attention mechanism of LLMs, compilation, and symbolic execution-based test generation for equivalence testing between the input and output programs. More precisely, we modify the typical LLM training loop to incorporate compiler and symbolic execution loss. Via extensive experiments comparing CoTran with 10 other transpilers and LLM-based translation tools over a benchmark of more than 57,000 Java-Python equivalent pairs, we show that CoTran outperforms them on relevant metrics such as compilation and runtime equivalence accuracy. For example, our tool gets 97.43% compilation accuracy and 49.66% runtime equivalence accuracy for J2P translation, whereas the nearest competing tool only gets 96.44% and 6.8% respectively.

We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based fuzzer aimed at finding security vulnerabilities. BertRLFuzzer works as follows: given a list of seed inputs, the fuzzer performs grammar-adhering and attack-provoking mutation operations on them to generate candidate attack vectors. The key insight of BertRLFuzzer is the combined use of two machine learning concepts. The first one is the use of semi-supervised learning with language models (e.g., BERT) that enables BertRLFuzzer to learn (relevant fragments of) the grammar of a victim application as well as attack patterns, without requiring the user to specify it explicitly. The second one is the use of RL with BERT model as an agent to guide the fuzzer to efficiently learn grammar-adhering and attack-provoking mutation operators. The RL-guided feedback loop enables BertRLFuzzer to automatically search the space of attack vectors to exploit the weaknesses of the given victim application without the need to create labeled training data. Furthermore, these two features together enable BertRLFuzzer to be extensible, i.e., the user can extend BertRLFuzzer to a variety of victim applications and attack vectors automatically (i.e., without explicitly modifying the fuzzer or providing a grammar). In order to establish the efficacy of BertRLFuzzer we compare it against a total of 13 black box and white box fuzzers over a benchmark of 9 victim websites. We observed a significant improvement in terms of time to first attack (54% less than the nearest competing tool), time to find all vulnerabilities (40-60% less than the nearest competing tool), and attack rate (4.4% more attack vectors generated than the nearest competing tool). Our experiments show that the combination of the BERT model and RL-based learning makes BertRLFuzzer an effective, adaptive, easy-to-use, automatic, and extensible fuzzer.

In this paper, we propose a new Deep Neural Network (DNN) testing algorithm called the Constrained Gradient Descent (CGD) method, and an implementation we call CGDTest aimed at exposing security and robustness issues such as adversarial robustness and bias in DNNs. Our CGD algorithm is a gradient-descent (GD) method, with the twist that the user can also specify logical properties that characterize the kinds of inputs that the user may want. This functionality sets CGDTest apart from other similar DNN testing tools since it allows users to specify logical constraints to test DNNs not only for $\ell_p$ ball-based adversarial robustness but, more importantly, includes richer properties such as disguised and flow adversarial constraints, as well as adversarial robustness in the NLP domain. We showcase the utility and power of CGDTest via extensive experimentation in the context of vision and NLP domains, comparing against 32 state-of-the-art methods over these diverse domains. Our results indicate that CGDTest outperforms state-of-the-art testing tools for $\ell_p$ ball-based adversarial robustness, and is significantly superior in testing for other adversarial robustness, with improvements in PAR2 scores of over 1500% in some cases over the next best tool. Our evaluation shows that our CGD method outperforms competing methods we compared against in terms of expressibility (i.e., a rich constraint language and concomitant tool support to express a wide variety of properties), scalability (i.e., can be applied to very large real-world models with up to 138 million parameters), and generality (i.e., can be used to test a plethora of model architectures).

Current environmental issues and depletion of natural petroleum resources demand alternative fuels for transportation. The present study deals with mixed dairy waste and Karanja oil (DK oils) biodiesel production using Taguchi L9 orthogonal array and characterization of the DK biodiesel blends to study its suitability as a renewable alternative fuel through gas chromatography, infrared spectroscopy, element analysis, physicochemical properties investigation, thermal behavior and combustion analysis using TG–DTG and TG–DSC techniques. Gas Chromatography was carried out to examine the quantification of fatty acid methyl esters, whereas infrared spectroscopy investigates the organic, inorganic, and polymeric contents with their vibrations in different molecular bonds. IS 1448 standard and elemental analysis is followed to examine the physicochemical properties of biodiesel and CHNSO percentage. Thermogravimetric analysis was followed to investigate the thermal behavior of the blends. Thermal analysis provides the information related to the stages of mass loss with temperature and mass loss percentage. The ignition temperature, burnout temperature, maximum combustion rate temperature, final decomposition temperature, ignition index, and comprehensive ignition index are the essential properties of fuel combustion analysis that are studied. Results indicate that the lower concentration of biodiesel in B20 blend provides a higher value of ignition index (1.67 × 10–4 to 3.86 × 10–4 min−1 °C−2) and comprehensive ignition index (2.8 × 10–6 to 1.1 × 10–5 min−1 °C−3) which provides better ignition performance during combustion.

The Right to be Forgotten is part of the recently enacted General Data Protection Regulation (GDPR) law that affects any data holder that has data on European Union residents. It gives EU residents the ability to request deletion of their personal data, including training records used to train machine learning models. Unfortunately, Deep Neural Network models are vulnerable to information leaking attacks such as model inversion attacks which extract class information from a trained model and membership inference attacks which determine the presence of an example in a model's training data. If a malicious party can mount an attack and learn private information that was meant to be removed, then it implies that the model owner has not properly protected their user's rights and their models may not be compliant with the GDPR law. In this paper, we present two efficient methods that address this question of how a model owner or data holder may delete personal data from models in such a way that they may not be vulnerable to model inversion and membership inference attacks while maintaining model efficacy. We start by presenting a real-world threat model that shows that simply removing training data is insufficient to protect users. We follow that up with two data removal methods, namely Unlearning and Amnesiac Unlearning, that enable model owners to protect themselves against such attacks while being compliant with regulations. We provide extensive empirical analysis that show that these methods are indeed efficient, safe to apply, effectively remove learned information about sensitive data from trained models while maintaining model efficacy.

Multiculturalism is a mechanism of interconnected ingredients and their experiences with each other as well as with the present world. It is a kind of respecting others’ very existence and identity. In specific ethnic minorities especially the women and low caste people are not identified with their self or existence. Respect is the practice which teaches to treat others with some reverence and modesty. It protects and values the dignity and social worth of an individual. It also acknowledges the social differences, because every group has its own specificity. Multiculturalism considers it because the minorities’ contribution to the society is empowering the society and taking it to a position of next and better destination. Uma Parmeshwran rightly argues in her article, “Home is home where your feet are, and may your heart be there too” that “literature can play an important role since literature not only reflects persistence and change in society but also can lead society to a better appreciation of its multicultural and ethno-centered fabric. The ethnic emigrant writer either writes about the country in which he is presently residing, like the main stream writers do, and thus try to be like ‘them’ or he can write about his ethnic world and be different” (31). In the present paper the major focus is on the multicultural aspects in context with various situations. Characters behaviour in different multicultural situations is the angle of analysis of the researcher.

The aim of the present investigation is to examine the characteristics of diesel engine for varying concentrations of diethyl ether (DEE) as an oxygenated additive in repurpose used cooking oil (RU...

We investigate the properties of formal languages expressible in terms of formulas over quantifier-free theories of word equations, arithmetic over length constraints, and language membership predicates for the classes of regular, visibly pushdown, and deterministic context-free languages. In total, we consider 20 distinct theories and decidability questions for problems such as emptiness and universality for formal languages over them. First, we discuss their relative expressive power and observe a rough division into two hierarchies based on whether or not word equations are present. Second, we consider the decidability status of several important decision problems, such as emptiness and universality. Note that the emptiness problem is equivalent to the satisfiability problem over the corresponding theory. Third, we consider the problem of whether a language in one theory is expressible in another and show several negative results in which this problem is undecidable. These results are particularly relevant in the context of normal forms in both practical and theoretical aspects of string solving.

In this paper, we present MachSMT, an algorithm selection tool for Satisfiability Modulo Theories (SMT) solvers. MachSMT supports the entirety of the SMT-LIB language. It employs machine learning (ML) methods to construct both empirical hardness models (EHMs) and pairwise ranking comparators (PWCs) over state-of-the-art SMT solvers. Given an SMT formula $$\mathcal {I}$$ I as input, MachSMT leverages these learnt models to output a ranking of solvers based on predicted run time on the formula $$\mathcal {I}$$ I . We evaluate MachSMT on the solvers, benchmarks, and data obtained from SMT-COMP 2019 and 2020. We observe MachSMT frequently improves on competition winners, winning $$54$$ 54 divisions outright and up to a $$198.4$$ 198.4 % improvement in PAR-2 score, notably in logics that have broad applications (e.g., BV, LIA, NRA, etc.) in verification, program analysis, and software engineering. The MachSMT tool is designed to be easily tuned and extended to any suitable solver application by users. MachSMT is not a replacement for SMT solvers by any means. Instead, it is a tool that enables users to leverage the collective strength of the diverse set of algorithms implemented as part of these sophisticated solvers.

In recent years we have witnessed a dramatic increase in the adoption and application of smart contracts in a variety of contexts such as decentralized finance, supply chain management, and identity management. However, a critical stumbling block to the further adoption of smart contracts is their security. A particularly widespread class of security vulnerabilities that afflicts Ethereum smart contracts is the gas limit denial of service(DoS) on a contract via unbounded operations. These vulnerabilities result in a failed transaction with an out-of-gas error and are often present in contracts containing loops whose bounds are affected by end-user input. Note that such vulnerabilities differ from gas limit DoS on the network via block stuffing. Therefore, we present Gas Gauge, a tool aimed at detecting Out-of-Gas DoS vulnerabilities in Ethereum smart contracts. Gas Gauge consists of three major components: the Detection, Identification, and Correction Phases. The Detection Phase consists of an accurate static analysis approach that finds and summarizes all the loops in a smart contract. The Identification Phase uses a white-box fuzzing approach to generate a set of inputs that causes the contract to run out of gas. The Correction Phase uses static analysis and run-time verification to predict the maximum loop bounds consistent with allowable gas usage and suggest appropriate repairs to the user of the tool. Each part of the tool can be used separately for different purposes or all together to detect, identify and help repair the contracts vulnerable to Out-of-Gas DoS vulnerabilities. Gas Gauge was tested on 1,000 real-world solidity smart contracts deployed on the Ethereum Mainnet. The results were compared to seven state-of-the-art static and symbolic tools, and it was empirically demonstrated that Gas Gauge is far more effective than competing state-of-the-art tools., Comment: 13 pages, 12 figures

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Metagenomic next-generation sequencing (mNGS), the shotgun sequencing of RNA and DNA from clinical samples, has proved useful for broad-spectrum pathogen detection and the genomic surveillance of viral outbreaks. An additional target enrichment step is generally needed for high-sensitivity pathogen identification in low-titre infections, yet available methods using PCR or capture probes can be limited by high cost, narrow scope of detection, lengthy protocols and/or cross-contamination. Here, we developed metagenomic sequencing with spiked primer enrichment (MSSPE), a method for enriching targeted RNA viral sequences while simultaneously retaining metagenomic sensitivity for other pathogens. We evaluated MSSPE for 14 different viruses, yielding a median tenfold enrichment and mean 47% (±16%) increase in the breadth of genome coverage over mNGS alone. Virus detection using MSSPE arboviral or haemorrhagic fever viral panels was comparable in sensitivity to specific PCR, demonstrating 95% accuracy for the detection of Zika, Ebola, dengue, chikungunya and yellow fever viruses in plasma samples from infected patients. Notably, sequences from re-emerging and/or co-infecting viruses that have not been specifically targeted a priori, including Powassan and Usutu, were successfully enriched using MSSPE. MSSPE is simple, low cost, fast and deployable on either benchtop or portable nanopore sequencers, making this method directly applicable for diagnostic laboratory and field use.

We present a novel Auxiliary Truth enhanced Genetic Algorithm (GA) that uses logical or mathematical constraints as a means of data augmentation as well as to compute loss (in conjunction with the traditional MSE), with the aim of increasing both data efficiency and accuracy of symbolic regression (SR) algorithms. Our method, logic-guided genetic algorithm (LGGA), takes as input a set of labelled data points and auxiliary truths (ATs) (mathematical facts known a priori about the unknown function the regressor aims to learn) and outputs a specially generated and curated dataset that can be used with any SR method. Three key insights underpin our method: first, SR users often know simple ATs about the function they are trying to learn. Second, whenever an SR system produces a candidate equation inconsistent with these ATs, we can compute a counterexample to prove the inconsistency, and further, this counterexample may be used to augment the dataset and fed back to the SR system in a corrective feedback loop. Third, the value addition of these ATs is that their use in both the loss function and the data augmentation process leads to better rates of convergence, accuracy, and data efficiency. We evaluate LGGA against state-of-the-art SR tools, namely, Eureqa and TuringBot on 16 physics equations from "The Feynman Lectures on Physics" book. We find that using these SR tools in conjunction with LGGA results in them solving up to 30.0% more equations, needing only a fraction of the amount of data compared to the same tool without LGGA, i.e., resulting in up to a 61.9% improvement in data efficiency.

The principle of strong induction, also known as k-induction is one of the first techniques for unbounded SAT-based Model Checking (SMC). While elegant and simple to apply, properties as such are rarely k-inductive and when they can be strengthened, there is no effective strategy to guess the depth of induction. It has been mostly displaced by techniques that compute inductive strengthenings based on interpolation and property directed reachability (Pdr). In this paper, we present kAvy, an SMC algorithm that effectively uses k-induction to guide interpolation and Pdr-style inductive generalization. Unlike pure k-induction, kAvy uses Pdr-style generalization to compute and strengthen an inductive trace. Unlike pure Pdr, kAvy uses relative k-induction to construct an inductive invariant. The depth of induction is adjusted dynamically by minimizing a proof of unsatisfiability. We have implemented kAvy within the Avy Model Checker and evaluated it on HWMCC instances. Our results show that kAvy is more effective than both Avy and Pdr, and that using k-induction leads to faster running time and solving more instances. Further, on a class of benchmarks, called shift, kAvy is orders of magnitude faster than Avy, Pdr and k-induction., Comment: Accepted to CAV 19

In recent years, Deep Neural Networks (DNNs) have had a dramatic impact on a variety of problems that were long considered very difficult, e. g., image classification and automatic language translation to name just a few. The accuracy of modern DNNs in classification tasks is remarkable indeed. At the same time, attackers have devised powerful methods to construct specially-crafted malicious inputs (often referred to as adversarial examples) that can trick DNNs into mis-classifying them. What is worse is that despite the many defense mechanisms proposed to protect DNNs against adversarial attacks, attackers are often able to circumvent these defenses, rendering them useless. This state of affairs is extremely worrying, especially since machine learning systems get adopted at scale. In this paper, we propose a scientific evaluation methodology aimed at assessing the quality, efficacy, robustness and efficiency of randomized defenses to protect DNNs against adversarial examples. Using this methodology, we evaluate a variety of defense mechanisms. In addition, we also propose a defense mechanism we call Randomly Perturbed Ensemble Neural Networks (RPENNs). We provide a thorough and comprehensive evaluation of the considered defense mechanisms against a white-box attacker model, six different adversarial attack methods and using the ILSVRC2012 validation data set.

We present a new string SMT solver, Z3str3, that is faster than its competitors Z3str2, Norn, CVC4, S3, and S3P over a majority of three industrial-strength benchmarks, namely Kaluza, PISA, and IBM AppScan. Z3str3 supports string equations, linear arithmetic over length function, and regular language membership predicate. The key algorithmic innovation behind the efficiency of Z3str3 is a technique we call theory-aware branching, wherein we modify Z3's branching heuristic to take into account the structure of theory literals to compute branching activities. In the traditional DPLL(T) architecture, the structure of theory literals is hidden from the DPLL(T) SAT solver because of the Boolean abstraction constructed over the input theory formula. By contrast, the theory-aware technique presented in this paper exposes the structure of theory literals to the DPLL(T) SAT solver's branching heuristic, thus enabling it to make much smarter decisions during its search than otherwise. As a consequence, Z3str3 has better performance than its competitors., Comment: 8 pages, 2 figures, 3 tables

Over the years complexity theorists have proposed many structural parameters to explain the surprising efficiency of conflict-driven clause-learning (CDCL) SAT solvers on a wide variety of large industrial Boolean instances. While some of these parameters have been studied empirically, until now there has not been a unified comparative study of their explanatory power on a comprehensive benchmark. We correct this state of affairs by conducting a large-scale empirical evaluation of CDCL SAT solver performance on nearly 7000 industrial and crafted formulas against several structural parameters such as backdoors, treewidth, backbones, and community structure. Our study led us to several results. First, we show that while such parameters only weakly correlate with CDCL solving time, certain combinations of them yield much better regression models. Second, we show how some parameters can be used as a "lens" to better understand the efficiency of different solving heuristics. Finally, we propose a new complexity-theoretic parameter, which we call learning-sensitive with restarts (LSR) backdoors, that extends the notion of learning-sensitive (LS) backdoors to incorporate restarts and discuss algorithms to compute them. We mathematically prove that for certain class of instances minimal LSR-backdoors are exponentially smaller than minimal-LS backdoors.

In this paper, we introduce a formal notion of partial compliance, called Attack-resistance, of a computer program running together with a defense mechanism w.r.t a non-exploitability specification. In our setting, a program may contain exploitable vulnerabilities, such as buffer overflows, but appropriate defense mechanisms built into the program or the operating system render such vulnerabilities hard to exploit by certain attackers, usually relying on the strength of the randomness of a probabilistic transformation of the environment or the program and some knowledge on the attacker's goals and attack strategy. We are motivated by the reality that most large-scale programs have vulnerabilities despite our best efforts to get rid of them. Security researchers have responded to this state of affairs by coming up with ingenious defense mechanisms such as address space layout randomization (ASLR) or instruction set randomization (ISR) that provide some protection against exploitation. However, implementations of such mechanism have been often shown to be insecure, even against the attacks they were designed to prevent. By formalizing this notion of attack-resistance we pave the way towards addressing the questions: "How do we formally analyze defense mechanisms? Is there a mathematical way of distinguishing effective defense mechanisms from ineffective ones? Can we quantify and show that these defense mechanisms provide formal security guarantees, albeit partial, even in the presence of exploitable vulnerabilities?". To illustrate our approach we discuss under which circumstances ISR implementations comply with the Attack-resistance definition.

Conflict-Driven Clause-Learning SAT solvers crucially depend on the Variable State Independent Decaying Sum (VSIDS) branching heuristic for their performance. Although VSIDS was proposed nearly fifteen years ago, and many other branching heuristics for SAT solving have since been proposed, VSIDS remains one of the most effective branching heuristics. In this paper, we advance our understanding of VSIDS by answering the following key questions. The first question we pose is "what is special about the class of variables that VSIDS chooses to additively bump?" In answering this question we showed that VSIDS overwhelmingly picks, bumps, and learns bridge variables, defined as the variables that connect distinct communities in the community structure of SAT instances. This is surprising since VSIDS was invented more than a decade before the link between community structure and SAT solver performance was discovered. Additionally, we show that VSIDS viewed as a ranking function correlates strongly with temporal graph centrality measures. Putting these two findings together, we conclude that VSIDS picks high-centrality bridge variables. The second question we pose is "what role does multiplicative decay play in making VSIDS so effective?" We show that the multiplicative decay behaves like an exponential moving average (EMA) that favors variables that persistently occur in conflicts (the signal) over variables that occur intermittently (the noise). The third question we pose is "whether VSIDS is temporally and spatially focused." We show that VSIDS disproportionately picks variables from a few communities unlike, say, the random branching heuristic. We put these findings together to invent a new adaptive VSIDS branching heuristic that solves more instances than one of the best-known VSIDS variants over the SAT Competition 2013 benchmarks.

In the past several years I have written two SMT solvers called STP and HAMPI that have found widespread use in computer security research by leading groups in academia, industry and the government. In this brief note I summarize the features of STP/HAMPI that make them particularly suited for computer security research, and a listing of some of the more important projects that use them.

We propose a novel approach to improving software security called Cryptographic Path Hardening, which is aimed at hiding security vulnerabilities in software from attackers through the use of provably secure and obfuscated cryptographic devices to harden paths in programs. By "harden" we mean that certain error-checking if-conditionals in a given program P are replaced by equivalent" we mean that adversaries cannot use semi-automatic program analysis techniques to reason about the hardened program paths and thus cannot discover as-yet-unknown errors along those paths, except perhaps through black-box dictionary attacks or random testing (which we can never prevent). Other than these unpreventable attack methods, we can make program analysis aimed at error-finding "provably hard" for a resource-bounded attacker, in the same sense that cryptographic schemes are hard to break. Unlike security-through-obscurity, in Cryptographic Path Hardening we use provably-secure crypto devices to hide errors and our mathematical arguments of security are the same as the standard ones used in cryptography. One application of Cryptographic Path Hardening is that software patches or filters often reveal enough information to an attacker that they can be used to construct error-revealing inputs to exploit an unpatched version of the program. By "hardening" the patch we make it difficult for the attacker to analyze the patched program to construct error-revealing inputs, and thus prevent him from potentially constructing exploits., Comment: Accepted as part of Off-the-beaten track at POPL 2012

Administrative role-based access control (ARBAC) is the first comprehensive administrative model proposed for role-based access control (RBAC). ARBAC has several features for designing highly expressive policies, but current work has not highlighted the utility of these expressive policies. In this report, we present a case study of designing an ARBAC policy for a bank comprising 18 branches. Using this case study we provide an assessment about the features of ARBAC that are likely to be used in realistic policies.

Maintenance of DNA integrity is crucial for all cell types, but neurons are particularly sensitive to mutations in DNA repair genes, which lead to both abnormal development and neurodegeneration. We describe a previously unknown autosomal recessive disease characterized by microcephaly, early-onset, intractable seizures and developmental delay (denoted MCSZ). Using genome-wide linkage analysis in consanguineous families, we mapped the disease locus to chromosome 19q13.33 and identified multiple mutations in PNKP (polynucleotide kinase 3'-phosphatase) that result in severe neurological disease; in contrast, a splicing mutation is associated with more moderate symptoms. Unexpectedly, although the cells of individuals carrying this mutation are sensitive to radiation and other DNA-damaging agents, no such individual has yet developed cancer or immunodeficiency. Unlike other DNA repair defects that affect humans, PNKP mutations universally cause severe seizures. The neurological abnormalities in individuals with MCSZ may reflect a role for PNKP in several DNA repair pathways.

Satisfiability Modulo Theories (SMT) and SAT solvers are critical components in many formal software tools, primarily due to the fact that they are able to easily solve logical problem instances with millions of variables and clauses. This efficiency of solvers is in surprising contrast to the traditional complexity theory position that the problems that these solvers address are believed to be hard in the worst case. In an attempt to resolve this apparent discrepancy between theory and practice, theorists have proposed the study of these solvers as proof systems that would enable establishing appropriate lower and upper bounds on their complexity. For example, in recent years it has been shown that (idealized models of) SAT solvers are polynomially equivalent to the general resolution proof system for propositional logic, and SMT solvers that use the CDCL(T) architecture are polynomially equivalent to the Res*(T) proof system. In this paper, we extend this program to the MCSAT approach for SMT solving by showing that the MCSAT architecture is polynomially equivalent to the Res*(T) proof system. Thus, we establish an equivalence between CDCL(T) and MCSAT from a proof-complexity theoretic point of view. This is a first and essential step towards a richer theory that may help (parametrically) characterize the kinds of formulas for which MCSAT-based SMT solvers can perform well., 10 pages

This thesis presents NC(T), an extension of the DPLL(T) scheme [16, 29] for decision procedures for quantifier-free first-order logics. In DPLL(T), a general Boolean DPLL engine is instantiated with a theory solver for the theory T. The DPLL engine is responsible for computing Boolean implications and detecting Boolean conflicts, while the theory solver detects implications and conflicts in T, and the communication between the two parts is done through a standardized interface. The Boolean reasoning is done on a set of constraints represented as clauses, meaning that formulas have to be converted to conjunctive normal form before they can be processed. The process results in the addition of variables and a general loss of structure. NC(T) remove this constraint by extending the Boolean engine to support the detection of implications and conflicts on non–clausal constraints, using techniques working on graphical representations of formulas in negation normal form first described in [19, 21]. Conversion to negation normal form preserves the size and structure of the input formula and does not introduce new variables. The above scheme NC(T) has been implemented as a tool called fstp, where the theory T under consideration is the quantifier–free theory of uninterpreted function and predicate symbols with equality. We describe our implementation and give early experimental results.