Equivalence Checking a Floating-point Unit against a High-level C Model

Semiconductor companies have increasingly adopted a methodology that starts with a system-level design specification in C/C++/SystemC. This model is extensively simulated to ensure correct functionality and performance. Later, a Register Transfer Level (RTL) implementation is created in Verilog, either manually by a designer or automatically by a high-level synthesis tool. It is essential to check that the C and Verilog programs are consistent. In this paper, we present a two-step approach, embodied in two equivalence checking tools, VERIFOX and HW-CBMC, to validate designs at the software and RTL levels, respectively. VERIFOX is used for equivalence checking of an untimed software model in C against a high-level reference model in C. HW-CBMC verifies the equivalence of a Verilog RTL implementation against an untimed software model in C. To evaluate our tools, we applied them to a commercial floating-point arithmetic unit (FPU) from ARM and an open-source dual-path floating-point adder

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution

Structure Discovery in Bayesian Networks: Algorithms and Applications

Bayesian networks are a class of probabilistic graphical models that have been widely used in various tasks for probabilistic inference and causal modeling. A Bayesian network provides a compact, flexible, and interpretable representation of a joint probability distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure from the data. This is called structure discovery. Structure discovery in Bayesian networks is a host of several interesting problem variants. In the optimal Bayesian network learning problem (we call this structure learning), one aims to find a Bayesian network that best explains the data and then utilizes this optimal Bayesian network for predictions or inferences. In others, we are interested in finding the local structural features that are highly probable (we call this structure discovery). Both structure learning and structure discovery are considered very hard because existing approaches to these problems require highly intensive computations. In this dissertation, we develop algorithms to achieve more accurate, efficient and scalable structure discovery in Bayesian networks and demonstrate these algorithms in applications of systems biology and educational data mining. Specifically, this study is conducted in five directions. First of all, we propose a novel heuristic algorithm for Bayesian network structure learning that takes advantage of the idea of curriculum learning and learns Bayesian network structures by stages. We prove theoretical advantages of our algorithm and also empirically show that it outperforms the state-of-the-art heuristic approach in learning Bayesian network structures. Secondly, we develop an algorithm to efficiently enumerate the k-best equivalence classes of Bayesian networks where Bayesian networks in the same equivalence class are equally expressive in terms of representing probability distributions. We demonstrate our algorithm in the task of Bayesian model averaging. Our approach goes beyond the maximum-a-posteriori (MAP) model by listing the most likely network structures and their relative likelihood and therefore has important applications in causal structure discovery. Thirdly, we study how parallelism can be used to tackle the exponential time and space complexity in the exact Bayesian structure discovery. We consider the problem of computing the exact posterior probabilities of directed edges in Bayesian networks. We present a parallel algorithm capable of computing the exact posterior probabilities of all possible directed edges with optimal parallel space efficiency and nearly optimal parallel time efficiency. We apply our algorithm to a biological data set for discovering the yeast pheromone response pathways. Fourthly, we develop novel algorithms for computing the exact posterior probabilities of ancestor relations in Bayesian networks. Existing algorithm assumes an order-modular prior over Bayesian networks that does not respect Markov equivalence. Our algorithm allows uniform prior and respects the Markov equivalence. We apply our algorithm to a biological data set for discovering protein signaling pathways. Finally, we introduce Combined student Modeling and prerequisite Discovery (COMMAND), a novel algorithm for jointly inferring a prerequisite graph and a student model from student performance data. COMMAND learns the skill prerequisite relations as a Bayesian network, which is capable of modeling the global prerequisite structure and capturing the conditional independence between skills. Our experiments on simulations and real student data suggest that COMMAND is better than prior methods in the literature. COMMAND is useful for designing intelligent tutoring systems that assess student knowledge or that offer remediation interventions to students

Extracting Rules from Neural Networks with Partial Interpretations

We investigate the problem of extracting rules, expressed in Horn logic, from neural network models. Our work is based on the exact learning model, in which a learner interacts with a teacher (the neural network model) via queries in order to learn an abstract target concept, which in our case is a set of Horn rules. We consider partial interpretations to formulate the queries. These can be understood as a representation of the world where part of the knowledge regarding the truthness of propositions is unknown. We employ Angluin’s algorithm for learning Horn rules via queries and evaluate our strategy empirically.publishedVersio

Extracting Rules from Neural Networks with Partial Interpretations

We investigate the problem of extracting rules, expressed in Horn logic, from neural network models. Our work is based on the exact learning model, in which a learner interacts with a teacher (the neural network model) via queries in order to learn an abstract target concept, which in our case is a set of Horn rules. We consider partial interpretations to formulate the queries. These can be understood as a representation of the world where part of the knowledge regarding the truthiness of propositions is unknown. We employ Angluin s algorithm for learning Horn rules via queries and evaluate our strategy empirically

Parallel Algorithms for Bayesian Networks Structure Learning with Applications in Systems Biology

The expression levels of thousands to tens of thousands of genes in a living cell are controlled by internal and external cues which act in a combinatorial manner that can be modeled as a network. High-throughput technologies, such as DNA-microarrays and next generation sequencing, allow for the measurement of gene expression levels on a whole-genome scale. In recent years, a wealth of microarray data probing gene expression under various biological conditions has been accumulated in public repositories, which facilitates uncovering the underlying transcriptional networks (gene networks). Due to the high data dimensionality and inherent complexity of gene interactions, this task inevitably requires automated computational approaches. Various models have been proposed for learning gene networks, with Bayesian networks (BNs) showing promise for the task. However, BN structure learning is an NP-hard problem and both exact and heuristic methods are computationally intensive with limited ability to produce large networks. To address these issues, we developed a set of parallel algorithms. First, we present a communication efficient parallel algorithm for exact BN structure learning, which is work-optimal provided that 2^n \u3e p.log(p), where n is the total number of variables, and p is the number of processors. This algorithm has space complexity within 1.41 of the optimal. Our empirical results demonstrate near perfect scaling on up to 2,048 processors. We further extend this work to the case of bounded node in-degree, where a limit d on the number of parents per variable is imposed. We characterize the algorithm\u27s run-time behavior as a function of d, establishing the range [n/3 - log(mn), ceil(n/2)) of values for d where it affects performance. Consequently, two plateaus regions are identified: for d \u3c n/3 - log(mn), where the run-time complexity remains the same as for d=1, and for d \u3e= ceil(n/2), where the run-time complexity remains the same as for d=n-1. Finally, we present a parallel heuristic approach for large-scale BN learning. This approach aims to combine the precision of exact learning with the scalability of heuristic methods. Our empirical results demonstrate good scaling on various high performance platforms. The quality of the learned networks for both exact and heuristic methods are evaluated using synthetically generated expression data. The biological relevance of the networks learned by the exact algorithm is assessed by applying it to the carotenoid biosynthesis pathway in Arabidopsis thaliana

Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Formal Specification and Verification for Automated Production Systems

Complex industrial control software often drives safety- and mission-critical systems, like automated production plants or control units embedded into devices in automotive systems. Such controllers have in common that they are reactive systems, i.e., that they periodically read sensor stimuli and cyclically execute the same program to produce actuator signals. The correctness of software for automated production is rarely verified using formal techniques. Although, due to the Industrial Revolution 4.0 (IR4.0), the impact and importance of software have become an important role in industrial automation. What is used instead in industrial practice today is testing and simulation, where individual test cases are used to validate an automated production system. Three reasons why formal methods are not popular are: (a) It is difficult to adequately formulate the desired temporal properties. (b) There is a lack of specification languages for reactive systems that are both sufficiently expressive and comprehensible for practitioners. (c) Due to the lack of an environment model the obtained results are imprecise. Nonetheless, formal methods for automated production systems are well studied academically---mainly on the verification of safety properties via model checking. In this doctoral thesis we present the concept of (1) generalized test tables (GTTs), a new specification language for functional properties, and their extension (2) relational test tables (RTTs) for relational properties. The concept includes the syntactical notion, designed for the intuition of engineers, and the semantics, which are based on game theory. We use RTTs for a novel confidential property on reactive systems, the provably forgetting of information. Moreover, for regression verification, an important relational property, we are able to achieve performance improvements by (3) creating a decomposing rule which splits large proofs into small sub-task. We implemented the verification procedures and evaluated them against realistic case studies, e.g., the Pick-and-Place-Unit from the Technical University of Munich. The presented contribution follows the idea of lowering the obstacle of verifying the dependability of reactive systems in general, and automated production systems in particular for the engineer either by introducing a new specification language (GTTs), by exploiting existing programs for the specification (RTTs, regression verification), or by improving the verification performance