Automated software testing techniques have become increasingly popular in recent years, with fuzzing being one of the most prevalent approaches. However, fuzzing Finite State Machines (FSMs) poses a significant challenge due to state and input dependency, resulting in exponential exploration time required to unlock the Finite State Machine. To address this issue, we present a novel approach in this research paper by introducing FINITEFUZZ, a Grey Box Fuzzer explicitly designed to fuzz Finite State Machines. Unlike the Blackbox fuzzers, FINITEFUZZ employs a mutational technique that utilizes feedback to steer the fuzzing process. FINITEFUZZ takes a random set of states and compares them with the desired FSM and records the states that increase the coverage of the Finite State Machine. The next seed incorporates the feedback received from all the previous seed inputs. This avoids exploring the same path multiple times and results in linear performance for all the types of Finite State machines possible. Our findings reveal that the use of FINITEFUZZ significantly reduces the exploration time required to uncover each state of the machine, making it a promising solution for generating Finite State Machines. We tested our FINITEFUZZ on 4 different types of Finite State Machines with each scenario resulting in at least 5X performance improvement in FSM generation. The potential applications of FSMs are vast, and our research suggests that the proposed approach can be used to generate any type of Finite State Machine. / Master of Science / Fuzzing, also known as Fuzz testing is a technique used to test software for security vulner- abilities, errors, and unexpected behavior. It involves generating random or semi-random input to a software application such as an operating system, or network service to test how it responds. Once input is generated, it is sent to the target application, which may crash, hang or produce unexpected results in response to the input. The results are then analyzed to identify potential vulnerabilities such as buffer overflows, input validation errors, and re- source leaks. Fuzzing is also used to test software that is difficult to test through other means, such as closed-source software or embedded systems. We generated a Fuzzer,FINITEFUZZ for Finite State Machine that unlocks the FSM starting from the random input and exploring only those seeds that increases the test coverage

This is a study taking an information theoretic approach toward quantum contextuality. The approach is that of using the memory complexity of finite-state machines to quantify quantum contextuality. These machines simulate the outcome behaviour of sequential measurements on systems of quantum bits as predicted by quantum mechanics. Of interest is the question of whether or not classical representations by finite-state machines are able to effectively represent the state-independent contextual outcome behaviour. Here we consider spatial efficiency, rather than temporal efficiency as considered by D. Gottesman (1999), for the particular measurement dynamics in systems of quantum bits. Extensions of cases found in the adjacent study of Kleinmann et al. (2010) are established by which upper bounds on memory complexity for particular scenarios are found. Furthermore, an optimal machine structure for simulating any n-partite system of quantum bits is found, by which a lower bound for the memory complexity is found for each n in the natural numbers. Within this finite-state machine approach questions of foundational concerns on quantum mechanics were sought to be addressed. Alas, nothing of novel thought on such concerns is here reported on.

This paper proposes new approaches to improve the coding performance of intra block coding in H.264/AVC via finite state machine and residual prediction. Grounding on high correlation between neighboring blocks, finite state machine is employed both at encoder and decoder to reduce the number of bits required for encoding to enhance coding performance. Two extra intra prediction modes are created in our proposed method. Through these two modes, the number of bits required to denote the current block is greatly reduced and low bit rate can be achieved. According to spatial correlation, intra-coded residual prediction reduces residual block by neighboring residual block. In this paper, we combine finite state machine with intra-coded residual prediction to achieve better coding performance. Experimental results show that the proposed method can greatly improve coding efficiency of intra macroblock coding in H.264/AVC.

Software needs to be adequately tested in order to increase the confidence that the system being developed is reliable. However, testing is a complicated and expensive process. Formal specification based models such as finite state machines have been widely used in system modelling and testing. In this PhD thesis, we primarily investigate fault detection and identification when testing from finite state machines. The research in this thesis is mainly comprised of three topics - construction of multiple Unique Input/Output (UIO) sequences using Metaheuristic Optimisation Techniques (MOTs), the improved fault coverage by using robust Unique Input/Output Circuit (UIOC) sequences, and fault diagnosis when testing from finite state machines. In the studies of the construction of UIOs, a model is proposed where a fitness function is defined to guide the search for input sequences that are potentially UIOs. In the studies of the improved fault coverage, a new type of UIOCs is defined. Based upon the Rural Chinese Postman Algorithm (RCPA), a new approach is proposed for the construction of more robust test sequences. In the studies of fault diagnosis, heuristics are defined that attempt to lead to failures being observed in some shorter test sequences, which helps to reduce the cost of fault isolation and identification. The proposed approaches and techniques were evaluated with regard to a set of case studies, which provides experimental evidence for their efficacy.

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Managing the respiratory diseases could be hard for many patients. Usually patients use the inhaler to administrate medicine on a regular basis. Even though the inhaler guideline is well-accepted, most patients make mistakes. In the recent years, smart inhalers with sensors have shown a great potential of guiding the daily use of the inhaler and better understanding the diseases. KTH MedTech startup Andning Med AB specializes on developing smart add-on hardware device to the inhaler. This thesis work is the continuation of the prototyping of the embedded software for the add-on device. The main goal of the thesis work is to develop a robust software for the hardware device to guide the inhaler use in real time, and collect and manage the inhaler data. To approach the problem, I use the Finite-state machine modelling and the object-oriented programming mindset. After the software development and testing, all the designed functionalities are achieved. The user could be visually guided by the device. The inhaler data could be correctly collected and uploaded to the mobile device. The thesis work could serve as a basis for further embedded software development for the device that will end up in the smart inhaler market in the future. It could also give reference to the similar IoT device development. / Att hantera luftvägssjukdomarna kan vara svårt för många patienter. Vanligtvis använder patienter inhalatorn för att administrera medicin regelbundet. Även om inhalatorns riktlinje är väl accepterad gör de flesta patienter misstag. Under de senaste åren har smarta inhalatorer med sensorer visat en stor potential att vägleda den dagliga användningen av inhalatorn och bättre förstå sjukdomarna. KTH MedTech startup Andning Med AB har specialiserat sig på att utveckla smarta tilläggsutrustning till inhalatorn. Detta examensarbete är en fortsättning på prototypframställningen av den inbäddade programvaran för tilläggsenheten. Huvudmålet med examensarbetet är att utveckla en robust mjukvara för hårdvaruenheten för att styra inhalatoranvändningen i realtid, samt samla in och hantera inhalatordata. För att närma mig problemet använder jag Finite-state maskinmodellering och det objektorienterade programmeringstänket. Efter mjukvaruutveckling och testning uppnås alla designade funktioner. Användaren kan visuellt guidas av enheten. Inhalatordata kunde samlas in korrekt och laddas upp till den mobila enheten. Examensarbetet kan fungera som en grund för ytterligare inbäddad mjukvaruutveckling för enheten som kommer att hamna på marknaden för smarta inhalatorer i framtiden. Det kan också hänvisa till liknande utveckling av IoT-enheter.

Fighting videogames (also known as fighting games) are ever growing in popularity and accessibility. The isolated console experiences of 20th century gaming has been replaced by online gaming services that allow gamers to play from almost anywhere in the world with one another. This gives rise to competitive gaming on a global scale enabling them to experience fresh play styles and challenges by playing someone new. Fighting games can typically be played either as a single player experience, or against another human player, whether it is via a network or a traditional multiplayer experience. However, there are two issues with these approaches. First, the single player offering in many fighting games is regarded as being simplistic in design, making the moves by the computer predictable. Secondly, while playing against other human players can be more varied and challenging, this may not always be achievable due to the logistics involved in setting up such a bout. Game Artificial Intelligence could provide a solution to both of these issues, allowing a human player s strategy to be learned and then mimicked by the AI fighter. In this thesis, game AI techniques have been researched to provide a means of mimicking human player strategies in strategic fighting games with multiple parameters. Various techniques and their current usages are surveyed, informing the design of two separate solutions to this problem. The first solution relies solely on leveraging k nearest neighbour classification to identify which move should be executed based on the in-game parameters, resulting in decisions being made at the operational level and being fed from the bottom-up to the strategic level. The second solution utilises a number of existing Artificial Intelligence techniques, including data driven finite state machines, hierarchical clustering and k nearest neighbour classification, in an architecture that makes decisions at the strategic level and feeds them from the top-down to the operational level, resulting in the execution of moves. This design is underpinned by a novel algorithm to aid the mimicking process, which is used to identify patterns and strategies within data collated during bouts between two human players. Both solutions are evaluated quantitatively and qualitatively. A conclusion summarising the findings, as well as future work, is provided. The conclusions highlight the fact that both solutions are proficient in mimicking human strategies, but each has its own strengths depending on the type of strategy played out by the human. More structured, methodical strategies are better mimicked by the data driven finite state machine hybrid architecture, whereas the k nearest neighbour approach is better suited to tactical approaches, or even random button bashing that does not always conform to a pre-defined strategy.

This research has two facets within separate research areas. The research area of Belief, Desire and Intention (BDI) agent capability development was extended. Deception detection research has been advanced with the development of automation using BDI agents. BDI agents performed tasks automatically and autonomously. This study used these characteristics to automate deception detection with limited intervention of human users. This was a useful research area resulting in a capability general enough to have practical application by private individuals, investigators, organizations and others. The need for this research is grounded in the fact that humans are not very effective at detecting deception whether in written or spoken form. This research extends the deception detection capability research in that typical deception detection tools are labor intensive and require extraction of the text in question following ingestion into a deception detection tool. A neural network capability module was incorporated to lend the resulting prototype Machine Learning attributes. The prototype developed as a result of this research was able to classify online data as either "deceptive" or "not deceptive" with 85% accuracy. The false discovery rate for "deceptive" online data entries was 20% while the false discovery rate for "not deceptive" was 10%. The system showed stability during test runs. No computer crashes or other anomalous system behavior were observed during the testing phase. The prototype successfully interacted with an online data communications server database and processed data using Neural Network input vector generation algorithms within seconds

Máquinas de Estados Finitos, além de suas inúmeras aplicações, são amplamente utilizadas na Engenharia de Software para modelar especificações de sistemas. Nesses modelos, projetistas podem inserir, inadvertidamente, estados redundantes, ou seja, que exibem o mesmo comportamento. A eliminação desses estados traz diversos benefícios para as atividades que utilizam o modelo, como menor complexidade e menos recursos físicos para implementação. O processo de eliminação desses estados é denominado minimização, e pode ser realizado em tempo polinomial para máquinas completamente especificadas. Por outro lado, a minimização de máquinas parciais, cuja especificação não cobre todo o domínio de entrada, somente pode ser obtida em tempo polinomial com o uso de abordagens não determinísticas, ou seja, trata-se de um problema NP-Completo. Este trabalho apresenta uma estratégia para a minimização de máquinas de estados finitos parciais que faz o uso de heurísticas e otimizações para tornar o processo mais eficiente. Visando mensurar tal ganho de eficiência, foram realizados experimentos, nos quais os tempos de execução de uma implementação do método proposto foram medidos, juntamente com os tempos de implementações de dois outros métodos conhecidos. Os resultados mostraram vantagens significativas de performance para o novo método em relação aos métodos anteriores / Finite State Machines are largely used on Software Engineering to model systems specifications. In these models, designers may inadvertently include redundant states, i.e., states which exhibit the same input/output behavior. The absence of such states brings benefits to the modeling activities, reducing the complexity and taking less physical resources on implementations. The process of eliminating redundant states is known as minimization, and can be accomplished in polynomial time for completely specified machines. On the other hand, the minimization of partially specified machines, i.e., machines which have undefined behavior for some inputs, can only be done in polynomial time when non-deterministic approaches are applied. It is a known NP-Complete problem. This work presents a deterministic approach to minimize incompletely specified Finite State Machines, using heuristics and optimizations to accomplish the task more efficiently. In order to measure the performance improvements, experiments were done, observing the running time of an implementation of the proposed method, along with running times of implementations of two other known methods. The results revealed a significant performance advantage when using the proposed approach

O teste baseado em modelos tem como objetivo auxiliar a atividade de testes, gerando conjuntos de casos de teste a partir de modelos, como Máquinas de Estados Finitos (MEFs). Diversos métodos de geração de conjuntos de caso de teste têm sido propostos ao longo das últimas décadas, com algumas contribuições recentes. Dentre esses trabalhos, há os que geram seqüências de verificação que são conjuntos de caso de teste formados por uma única seqüência e que são capazes de detectar os defeitos de uma implementação cujo comportamento pode ser modelado a partir de uma MEF. Neste trabalho é proposto um algoritmo de geração de seqüências de verificação que tem a finalidade de gerar seqüências menores que as seqüências geradas pelos métodos existentes. O algoritmo, que é baseado na técnica de algoritmos genéticos e nas condições de suficiência para a completude de casos de teste, consiste basicamente em criar novas seqüências a partir de seqüências menores. Por meio de mutações, novas seqüências são geradas pelo algoritmo. As condições de suficiência são utilizadas para determinar quais seqüências geradas são seqüências de verificação. Também são apresentados neste trabalho os estudos experimentais realizados para determinar o comportamento do algoritmo diante de diferentes contextos / Model-based testing aims at aiding the testing activity, generating test cases from models such as Finite State Machines (FSM). Several test cases generation methods have been proposed along the last decades, with some recent contributions. Among these works, there are those that generate checking sequences, which are test cases formed by a single sequence and which are capable of detecting faults in an implementation whose behavior can be modeled as an FSM. This work proposes a checking sequences generation algorithm which aims at generating sequences smaller than the sequences generated by existing methods. The algorithm, which is based on the genetic algorithms technique and sufficient conditions for completeness of test cases, basically consists of creating new sequences from small sequences. Through mutations, new sequences are generated by the algorithm. The suffcient conditions are used to determine which sequences are checking sequences. Experimental studies are presented in this work to determine the behavior of the algorithm on different contexts