Verifying safety and persistence in hybrid systems using flowpipes and continuous invariants

We describe a method for verifying the temporal property of persistence in non-linear hybrid systems. Given some system and an initial set of states, the method establishes that system trajectories always eventually evolve into some specified target subset of the states of one of the discrete modes of the system, and always remain within this target region. The method also computes a time-bound within which the target region is always reached. The approach combines flowpipe computation with deductive reasoning about invariants and is more general than each technique alone. We illustrate the method with a case study showing that potentially destructive stick-slip oscillations of an oil-well drill eventually die away for a certain choice of drill control parameters. The case study demonstrates how just using flowpipes or just reasoning about invariants alone can be insufficient and shows the richness of systems that one can handle with the proposed method, since the systems features modes with non-polynomial ODEs. We also propose an alternative method for proving persistence that relies solely on flowpipe computation

Synthesis of Probabilistic Models for Quality-of-Service Software Engineering

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability

Search-Based Synthesis of Probabilistic Models for Quality-of-Service Software Engineering

The formal verification of finite-state probabilistic models supports the engineering of software with strict quality-of-service (QoS) requirements. However, its use in software design is currently a tedious process of manual multiobjective optimisation. Software designers must build and verify probabilistic models for numerous alternative architectures and instantiations of the system parameters. When successful, they end up with feasible but often suboptimal models. The EvoChecker search-based software engineering approach and tool introduced in our paper employ multiobjective optimisation genetic algorithms to automate this process and considerably improve its outcome. We evaluate EvoChecker for six variants of two software systems from the domains of dynamic power management and foreign exchange trading. These systems are characterised by different types of design parameters and QoS requirements, and their design spaces comprise between 2E+14 and 7.22E+86 relevant alternative designs. Our results provide strong evidence that EvoChecker significantly outperforms the current practice and yields actionable insights for software designers

FORMAL ANALYSIS OF WEB SERVICE COMPOSITION

Ph.DDOCTOR OF PHILOSOPH

Runtime Quantitative Verification of Self-Adaptive Systems

Software systems used in mission- and business-critical applications in domains including defence, healthcare, and finance must comply with strict dependability, performance, and other Quality-of-Service (QoS) requirements. Self-adaptive systems achieve this compliance under changing environmental conditions, evolving requirements and system failures by using closed-loop control to modify their behaviour and structure in response to these events. Runtime quantitative verification (RQV) is a mathematically-based approach that implements the closed-loop control of self-adaptive systems. Using runtime observations of a system and its environment, RQV updates stochastic models whose formal analysis underpins the adaptation decisions made within the control loop. The approach can identify and, under certain conditions, predict violation of QoS requirements, and can drive self-adaptation in ways guaranteed to restore or maintain compliance with these requirements. Despite its merits, RQV has significant computation and memory overheads, which restrict its applicability to small systems and to adaptations affecting only the configuration parameters of the system. In this thesis, we introduce RQV variants that improve the efficiency and scalability of the approach and extend its applicability to larger and more complex self-adaptive software systems, and to adaptations that modify the structure of a system. First, we integrate RQV with established efficiency improvement techniques from other software engineering areas. We use caching of recent analysis results, limited lookahead to precompute suitable adaptations for potential future changes, and nearly-optimal reconfiguration to eliminate the need for an exhaustive analysis of the entire reconfiguration space. Second, we introduce an RQV variant that incorporates evolutionary algorithms into the RQV process facilitating the efficient search through large reconfiguration spaces and enabling adaptations that include structural changes. Third, we propose an RQV-driven approach that decentralises the control loops in distributed self-adaptive systems. Finally, we devise an RQV-based methodology for the engineering of trustworthy self-adaptive systems. We evaluate the proposed RQV variants using prototype self-adaptive systems from several application domains, including an embedded system for unmanned underwater vehicles and a foreign exchange service-based system. Our results, subject to the adaptation scenarios used in the evaluation, demonstrate the effectiveness and generality of the new RQV variants

Multi-objective Optimization in Traffic Signal Control

Traffic Signal Control systems are one of the most popular Intelligent Transport Systems and they are widely used around the world to regulate traffic flow. Recently, complex optimization techniques have been applied to traffic signal control systems to improve their performance. Traffic simulators are one of the most popular tools to evaluate the performance of a potential solution in traffic signal optimization. For that reason, researchers commonly optimize traffic signal timing by using simulation-based approaches. Although evaluating solutions using microscopic traffic simulators has several advantages, the simulation is very time-consuming. Multi-objective Evolutionary Algorithms (MOEAs) are in many ways superior to traditional search methods. They have been widely utilized in traffic signal optimization problems. However, running MOEAs on traffic optimization problems using microscopic traffic simulators to estimate the effectiveness of solutions is time-consuming. Thus, MOEAs which can produce good solutions at a reasonable processing time, especially at an early stage, is required. Anytime behaviour of an algorithm indicates its ability to provide as good a solution as possible at any time during its execution. Therefore, optimization approaches which have good anytime behaviour are desirable in evaluation traffic signal optimization. Moreover, small population sizes are inevitable for scenarios where processing capabilities are limited but require quick response times. In this work, two novel optimization algorithms are introduced that improve anytime behaviour and can work effectively with various population sizes. NS-LS is a hybrid of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a local search which has the ability to predict a potential search direction. NS-LS is able to produce good solutions at any running time, therefore having good anytime behaviour. Utilizing a local search can help to accelerate the convergence rate, however, computational cost is not considered in NS-LS. A surrogate-assisted approach based on local search (SA-LS) which is an enhancement of NS-LS is also introduced. SA-LS uses a surrogate model constructed using solutions which already have been evaluated by a traffic simulator in previous generations. NS-LS and SA-LS are evaluated on the well-known Benchmark test functions: ZDT1 and ZDT2, and two real-world traffic scenarios: Andrea Costa and Pasubio. The proposed algorithms are also compared to NSGA-II and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D). The results show that NS-LS and SA-LS can effectively optimize traffic signal timings of the studied scenarios. The results also confirm that NS-LS and SA-LS have good anytime behaviour and can work well with different population sizes. Furthermore, SA-LS also showed to produce mostly superior results as compared to NS-LS, NSGA-II, and MOEA/D.Ministry of Education and Training - Vietna

Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies

We present a new approach for synthesising Pareto- optimal Markov decision process (MDP) policies that satisfy complex combinations of quality-of-service (QoS) software requirements. These policies correspond to optimal designs or configurations of software systems, and are obtained by translating MDP models of these systems into parametric Markov chains, and using multi-objective genetic algorithms to synthesise Pareto-optimal parameter values that define the required MDP policies. We use case studies from the service-based systems and robotic control software domains to show that our MDP policy synthesis approach can handle a wide range of QoS requirement combinations unsupported by current probabilistic model checkers. Moreover, for requirement combinations supported by these model checkers, our approach generates better Pareto-optimal policy sets according to established quality metrics

Temporal Information in Data Science: An Integrated Framework and its Applications

Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems.Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems

Model-based symbolic design space exploration at the electronic system level: a systematic approach

In this thesis, a novel, fully systematic approach is proposed that addresses the automated design space exploration at the electronic system level. The problem is formulated as multi-objective optimization problem and is encoded symbolically using Answer Set Programming (ASP). Several specialized solvers are tightly coupled as background theories with the foreground ASP solver under the ASP modulo Theories (ASPmT) paradigm. By utilizing the ASPmT paradigm, the search is executed entirely systematically and the disparate synthesis steps can be coupled to explore the search space effectively.In dieser Arbeit wird ein vollständig systematischer Ansatz präsentiert, der sich mit der Entwurfsraumexploration auf der elektronischen Systemebene befasst. Das Problem wird als multikriterielles Optimierungsproblem formuliert und symbolisch mit Hilfe von Answer Set Programming (ASP) kodiert. Spezialisierte Solver sind im Rahmen des ASP modulo Theories (ASPmT) Paradigmas als Hintergrundtheorien eng mit dem ASP Solver gekoppelt. Durch die Verwendung von ASPmT wird die Suche systematisch ausgeführt und die individuellen Schritte können gekoppelt werden, um den Suchraum effektiv zu durchsuchen

A data-based approach for dynamic classification of functional scenarios oriented to industrial process plants

L'objectif principal de cette thèse est de développer un algorithme dynamique de partitionnement de données (classification non supervisée ou " clustering " en anglais) qui ne se limite pas à des concepts statiques et qui peut gérer des distributions qui évoluent au fil du temps. Cet algorithme peut être utilisé dans les systèmes de surveillance du processus, mais son application ne se limite pas à ceux-ci. Les contributions de cette thèse peuvent être présentées en trois groupes: 1. Contributions au partitionnement dynamique de données en utilisant : un algorithme de partitionnement dynamique basé à la fois sur la distance et la densité des échantillons est présenté. Cet algorithme ne fait aucune hypothèse sur la linéarité ni la convexité des groupes qu'il analyse. Ces clusters, qui peuvent avoir des densités différentes, peuvent également se chevaucher. L'algorithme développé fonctionne en ligne et fusionne les étapes d'apprentissage et de reconnaissance, ce qui permet de détecter et de caractériser de nouveaux comportements en continu tout en reconnaissant l'état courant du système. 2. Contributions à l'extraction de caractéristiques : une nouvelle approche permettant d'extraire des caractéristiques dynamiques est présentée. Cette approche, basée sur une approximation polynomiale par morceaux, permet de représenter des comportements dynamiques sans perdre les informations relatives à la magnitude et en réduisant simultanément la sensibilité de l'algorithme au bruit dans les signaux analysés. 3. Contributions à la modélisation de systèmes à événements discrets évolutifs a partir des résultats du clustering : les résultats de l'algorithme de partitionnement sont utilisés comme base pour l'élaboration d'un modèle à événements discrets du processus. Ce modèle adaptatif offre une représentation du comportement du processus de haut niveau sous la forme d'un automate dont les états représentent les états du processus appris par le partitionnement jusqu'à l'instant courant et les transitions expriment l'atteignabilité des états.The main objective of this thesis is to propose a dynamic clustering algorithm that can handle not only dynamic data but also evolving distributions. This algorithm is particularly fitted for the monitoring of processes generating massive data streams, but its application is not limited to this domain. The main contributions of this thesis are: 1. Contribution to dynamic clustering by the proposal of an approach that uses distance- and density-based analyses to cluster non-linear, non-convex, overlapped data distributions with varied densities. This algorithm, that works in an online fashion, fusions the learning and lassification stages allowing to continuously detect and characterize new concepts and at the same time classifying the input samples, i.e. which means recognizing the current state of the system in a supervision application. 2. Contribution to feature extraction by the proposal of a novel approach to extract dynamic features. This approach ,based on piece-polynomial approximation, allows to represent dynamic behaviors without losing magnitude related information and to reduce at the same time the algorithm sensitivity to noise corrupting the signals. 3. Contribution to automatic discrete event modeling for evolving systems by exploiting informations brought by the clustering. The generated model is presented as a timed automaton that provides a high-level representation of the behavior of the process. The latter is adaptive in the sense that its construction is elaborated following the discovery of new concepts by the clustering algorithm