Automated Learning Setups in Automata Learning

International audienc

Active automata learning for real life applications

Automata learning is a concept discussed in the literature for decades. Accordingly, the theoretical framework for learning automata from observations has been in place already for a considerable time. Despite the ever-increasing theoretical maturity of the field, real-life applications are few and far between. In part this can certainly be attributed to the lack of ready-made infrastructure, e.g., frameworks that support automata learning with the goal of learning realistic systems. Additionally, the degree of automation in this field is low, meaning that learning setups have to be instantiated manually and per-system, making this a time-consuming and laborious undertaking. The central question of this thesis is "How can active automata learning be readied for application on real-life systems?". Contributions presented includes work on learning frameworks and tools, learning algorithms, scalability of learning solutions, and automated configuration and execution of learning setups

Simplicity-oriented lifelong learning of web applications

Nowadays, web applications are ubiquitous. Entire business models revolve around making their services available over the Internet, anytime, anywhere in the world. Due to today’s rapid development practices, software changes are released faster than ever before, creating the risk of losing control over the quality of the delivered products. To counter this, appropriate testing methodologies must be deeply integrated into each phase of the development cycle to identify potential defects as early as possible and to ensure that the product operates as expected in production. The use of low- and no-code tools and code generation technologies can drastically reduce the implementation effort by using well-tailored (graphical) Domain-Specific Languages (DSLs) to focus on what is important: the product. DSLs and corresponding Integrated Modeling Environments (IMEs) are a key enabler for quality control because many system properties can already be verified at a pre-product level. However, to verify that the product fulfills given functional requirements at runtime, end-to-end testing is still a necessity. This dissertation describes the implementation of a lifelong learning framework for the continuous quality control of web applications. In this framework, models representing user-level behavior are mined from running systems using active automata learning, and system properties are verified using model checking. All this is achieved in a continuous and fully automated manner. Code changes trigger testing, learning, and verification processes which generate feedback that can be used for model refinement or product improvement. The main focus of this framework is simplicity. On the one hand, it allows Quality Assurance (QA) engineers to apply learning-based testing techniques to web applications with minimal effort, even without writing code; on the other hand, it allows automation engineers to easily implement these techniques in modern development workflows driven by Continuous Integration and Continuous Deployment (CI/CD). The effectiveness of this framework is leveraged by the Language-Driven Engineering (LDE) approach to web development. Key to this is the text-based DSL iHTML, which enables the instrumentation of user interfaces to make web applications learnable by design, i.e., they adhere to practices that allow fully automated inference of behavioral models without prior specification of an input alphabet. By designing code generators to generate instrumented web-based products, the effort for quality control in the LDE ecosystem is minimized and reduced to formulating runtime properties in temporal logic and verifying them against learned models

Machine learning for emergent middleware

Highly dynamic and heterogeneous distributed systems are challenging today's middleware technologies. Existing middleware paradigms are unable to deliver on their most central promise, which is offering interoperability. In this paper, we argue for the need to dynamically synthesise distributed system infrastructures according to the current operating environment, thereby generating "Emergent Middleware'' to mediate interactions among heterogeneous networked systems that interact in an ad hoc way. The paper outlines the overall architecture of Enablers underlying Emergent Middleware, and in particular focuses on the key role of learning in supporting such a process, spanning statistical learning to infer the semantics of networked system functions and automata learning to extract the related behaviours of networked systems

Ais-Psmaca: Towards Proposing an Artificial Immune System for Strengthening Psmaca: An Automated Protein Structure Prediction using Multiple Attractor Cellular Automata

Predicting the structure of proteins from their amino acid sequences has gained a remarkable attention in recent years. Even though there are some prediction techniques addressing this problem, the approximate accuracy in predicting the protein structure is closely 75%. An automated procedure was evolved with MACA (Multiple Attractor Cellular Automata) for predicting the structure of the protein. Artificial Immune System (AIS-PSMACA) a novel computational intelligence technique is used for strengthening the system (PSMACA) with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-PSMACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, strand, and coil) for the secondary structure. Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes. We have tested the proposed classifier with twilight-zone and 1-high-similarity benchmark datasets with over three dozens of modern competing predictors shows that AIS-PSMACA provides the best overall accuracy that ranges between 80% and 89.8% depending on the dataset

Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability

Despite significant effort, building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems. In general, rule-based and linear models lack accuracy, while deep learning interpretability is based on rough approximations of the underlying inference. Using a linear combination of conjunctive clauses in propositional logic, Tsetlin Machines (TMs) have shown competitive performance on diverse benchmarks. However, to do so, many clauses are needed, which impacts interpretability. Here, we address the accuracy-interpretability challenge in machine learning by equipping the TM clauses with integer weights. The resulting Integer Weighted TM (IWTM) deals with the problem of learning which clauses are inaccurate and thus must team up to obtain high accuracy as a team (low weight clauses), and which clauses are sufficiently accurate to operate more independently (high weight clauses). Since each TM clause is formed adaptively by a team of Tsetlin Automata, identifying effective weights becomes a challenging online learning problem. We address this problem by extending each team of Tsetlin Automata with a stochastic searching on the line (SSL) automaton. In our novel scheme, the SSL automaton learns the weight of its clause in interaction with the corresponding Tsetlin Automata team, which, in turn, adapts the composition of the clause by the adjusting weight. We evaluate IWTM empirically using five datasets, including a study of interpetability. On average, IWTM uses 6.5 times fewer literals than the vanilla TM and 120 times fewer literals than a TM with real-valued weights. Furthermore, in terms of average F1-Score, IWTM outperforms simple Multi-Layered Artificial Neural Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random Forest, XGBoost, Explainable Boosting Machines, and standard and real-value weighted TMs.Comment: 20 pages, 10 figure