No description available.

No description available.

No description available.

No description available.

<p> The Haskell programming language uses type classes to deal with overloading. Functions are overloaded by defining some types to be instances of a class. A meaningful instance should satisfy the invariants of the class.</p> <p> In this thesis we present one method to validate the type instances of classes informally, and another one to verify them in a formal way.</p> <p> The first method uses QuickCheck, which is an automatic testing tool for Haskell programs. We introduce how to specify the properties of type classes in QuickCheck by some examples, and I also present testing for Haskell standard types and classes.</p> <p> The second method I adopted uses the theorem prover Isabelle/HOL. To facilitate the usage of Isabelle/HOL for Haskell programmers, I define a set of translation rules from Haskell programs to Isabelle/HOL, and design a simple automatic translating tool based on those rules. Logical differences between Haskell and Isabelle/HOL need to be considered in the translation. For example Isabelle/HOL is not suitable to describe the semantics of lazy evaluation and of Haskell functions that are non-terminating. I also prove some type instances to illustrate how the properties are verified in Isabelle/HOL.</p> / Thesis / Master of Applied Science (MASc)

This is the final revision of my M.Sc. Thesis. / Runtime Verification is a technique to extract information from a running system in order to detect executions violating a given correctness specification. In this thesis, we study distributed synchronous/asynchronous runtime verification of systems. In our setting, there is a set of distributed monitors that have only partial views of a large system and are subject to failures. In this context, it is unavoidable that monitors may have different views of the underlying system, and therefore may have different valuations of the correctness property. In this thesis, we propose an automata-based synchronous monitoring algorithm that copes with f crash failures in a distrbuted setting. The algorithm solves the synchronous monitoring problem in f + 1 rounds of communication, and significantly reduces the message size overhead. We also propose an algorithm for distributed crash-resilient asynchronous monitoring that consistently monitors the system under inspection without any communication between monitors. Each local monitor emits a verdict set solely based on its own partial observation, and the intersection of the verdict sets will be the same as the verdict computed by a centralized monitor that has full view of the system. / Thesis / Master of Science (MSc)

No / A two-tiered architecture with resource-rich master nodes at the upper tier and resource-poor sensor nodes at the lower tier is expected to be adopted in large scale sensor networks. In a hostile environment, adversaries are more motivated to compromise the master nodes to break the authenticity and completeness of query results, whereas it is lack of light and secure query processing protocol in tiered sensor networks at present. In this paper, we study the problem of verifiable fine-grained top- queries in two-tiered sensor networks, and propose a novel verification scheme, which is named Verification Scheme for Fine-grained Top- Queries (VSFTQ). To make top- query results verifiable, VSFTQ establishes relationships among data items of each sensor node using their orders, which are encrypted together with the scores of the data items and the interested time epoch number using distinct symmetric keys kept by each sensor node and the network owner. Both theoretical analysis and simulation results show that VSFTQ can not only ensure high probability of detecting forged and/or incomplete query results, but also significantly decrease the amount of verification information when compared with existing schemes.

Yes / Kernel P systems have been introduced as a unifying formalism allowing to specify, simulate and analyse various problems. Several applications of this model have been considered and a powerful tool built in order to support their development and analysis. Testing represents an important aspect of any system analysis and correctness. In this paper we introduce for the first time a bounded test generation approach for kernel P systems by considering bounded input sequences. A learning algorithm for kernel P systems is based on learning X-machine models that are equivalent to these systems for sequences of steps up to a certain limit, ℓ. The Lℓ learning algorithm is used. The testing approach is then devised from the inferred X-machines. The method is applied to a case study illustrating the key parts of the approach. / This research was supported by the European Regional Development Fund, Competitiveness Operational Program 2014-2020 through project IDBC (code SMIS 2014+: 121512). Raluca Lefticaru, Savas Konur and Marian Gheorghe have been partially supported by the Royal Society grant IES╲R3╲213176, 2022-2024. The work of Savas Konur is also supported by EPSRC (EP/R043787/1).

With the growing importance of software on which human lives increasingly depend, the correctness requirement of the underlying software becomes especially critical. However, the increasing complexities and sizes of modern software systems pose special challenges on the effectiveness as well as efficiency of software verification. Two major obstacles include the quality of test generation in terms of error detection in software testing and the state space explosion problem in software formal verification (model checking). In this dissertation, we investigate several hybrid techniques that explore dynamic (with program execution), static (without program execution) as well as the synergies of multiple approaches in software verification from the perspectives of testing and model checking. For software testing, a new simulation-based internal variable range coverage metric is proposed with the goal of enhancing the error detection capability of the generated test data when applied as the target metric. For software model checking, we utilize various dynamic analysis methods, such as data mining, swarm intelligence (ant colony optimization), to extract useful high-level information from program execution data. Despite being incomplete, dynamic program execution can still help to uncover important program structure features and variable correlations. The extracted knowledge, such as invariants in different forms, promising control flows, etc., is then used to facilitate code-level program abstraction (under-approximation/over-approximation), and/or state space partition, which in turn improve the performance of property verification. In order to validate the effectiveness of the proposed hybrid approaches, a wide range of experiments on academic and real-world programs were designed and conducted, with results compared against the original as well as the relevant verification methods. Experimental results demonstrated the effectiveness of our methods in improving the quality as well as performance of software verification. For software testing, the newly proposed coverage metric constructed based on dynamic program execution data is able to improve the quality of test cases generated in terms of mutation killing — a widely applied measurement for error detection. For software model checking, the proposed hybrid techniques greatly take advantage of the complementary benefits from both dynamic and static approaches: the lightweight dynamic techniques provide flexibility in extracting valuable high-level information that can be used to guide the scope and the direction of static reasoning process. It consequently results in significant performance improvement in software model checking. On the other hand, the static techniques guarantee the completeness of the verification results, compensating the weakness of dynamic methods. / Ph. D.

With the ever increasing complexity of digital systems, functional verification has become a daunting task to circuit designers. Functional verification alone often surpasses 70% of the total development cost and the situation has been projected to continue to worsen. The most critical limitations of existing techniques are the capacity issue and the run-time issue. This dissertation addresses the functional verification problem using a unified approach, which utilizes different core algorithms at various abstraction levels. At the logic level, we focus on incorporating a set of novel ideas to existing formal verification approaches. First, we present a number of powerful optimizations to improve the performance and capacity of a typical SAT-based bounded model checking framework. Secondly, we present a novel method for performing dynamic abstraction within a framework for abstraction-refinement based model checking. Experiments on a wide range of industrial designs have shown that the proposed optimizations consistently provide between 1-2 orders of magnitude speedup and can be extremely useful in enhancing the efficacy of existing formal verification algorithms. At the register transfer level, where the formal verification is less likely to succeed, we developed an efficient ATPG-based validation framework, which leverages the high-level circuit information and an improved observability-enhanced coverage to generate high quality validation sequences. Experiments show that our approach is able to generate high quality validation vectors, which achieve both high tag coverage and high bug coverage with extremely low computational cost. / Ph. D.