Abstract Distributed runtime verification (DRV) addresses the problem of checking the correctness of distributed systems during execution, coping with partial knowledge, dynamic topologies, and the absence of global time. These challenges are particularly prominent in proximity-based networks, such as those arising in IoT and Far Edge computing scenarios, where large numbers of devices interact through local communication. This tutorial presents an approach to DRV based on Aggregate Programming (AP), a paradigm for designing distributed collective systems via high-level abstractions over computational fields. We show how temporal and spatial properties (expressed in past-CTL and SLCS, respectively) can be systematically compiled into aggregate monitors grounded in the eXchange Calculus and executed using the FCPP C++ framework and simulator for AP. The tutorial combines conceptual foundations with practical guidance: participants learn how to specify spatio-temporal properties, generate corresponding monitors, and execute them in a 3D simulation environment. Examples are drawn from ongoing industrial collaborations and research projects, which we use to illustrate realistic monitoring scenarios and motivate open challenges for AP-based DRV.

Feature model evolution plans (FMEPs) describe how feature models for software product lines (SPLs) evolve over time. While different feature models can exist for different points in time over the lifetime of the product line, an FMEP describes how to compute a feature model for a given time point. SPLs capitalise on the variability and reusability of the software through combining optional and mandatory features. As business requirements change over time, FMEPs should support intermediate update. A plan hence contains updates to an initial model by adding, deleting, moving or changing elements at different points in time, in line with the evolving business requirements on the SPL, potentially affecting feature models that should be derived in the future from the plan. A recurring challenge in maintaining FMEPs is that updates may lead to inconsistent intermediate feature models, most notably so-called paradoxes. A paradox may not materialise at the first point in time an update on the plan is performed to obtain a particular feature model, but may only in combination with a later modification prescribed by the plan create a structurally invalid model. Correspondingly, a single modification to a plan may require multiple checks over the liftetime of the affected elements to rule out paradoxes. Current approaches require the analysis from the point in time an update is applied to an FMEP throughout the entire lifetime of the plan. In this paper, we define a so-called interval-based feature model (IBFM) to represent FMEPs, with a precise definition of spatial and temporal scopes that narrow the time interval and the sub-models that an update can affect. We propose a rule system for updating IBFMs, and also prove the soundness of the proposed rules and show their modularity, i.e., that each rule operates strictly within its temporal and spatial scopes. We have conducted a detailed evaluation on our modular approach and present the experimental results, which show that we outperform an existing linear approach.

To help analyze time series in data mining applications, many time series representation approaches have been proposed to convert a raw time series into another series for representing the original time series. However, existing approaches are not designed for open-ended time series (which is a sequence of data points being continuously collected at a fixed interval without any length limit) because these approaches need to know the total length of the target time series in advance and preprocess the entire time series using a normalization method. Furthermore, many representation approaches require users to configure and tune some parameters beforehand in order to achieve satisfactory representation results. In this paper, we propose NP-Free, a real-time Normalization-free and Parameter-tuning-free representation approach for open-ended time series. Without needing to use any normalization method or tune any parameter, NP-Free can generate a representation for a raw time series on the fly by converting each data point of the time series into a root-mean-square error (RMSE) value based on Long Short-Term Memory (LSTM) and a Look-Back and Predict-Forward strategy. To demonstrate the capability of NP-Free in representing time series, we conducted several experiments based on real-world open-source time series datasets. We also evaluated the time consumption of NP-Free in generating representations.

This paper presents a search-based approach relying on multi-objective reinforcement learning and optimization for test case generation in model-based software testing. Our approach considers test case generation as an exploration versus exploitation dilemma, and we address this dilemma by implementing a particular strategy of multi-objective multi-armed bandits with multiple rewards. After optimizing our strategy using the jMetal multi-objective optimization framework, the resulting parameter setting is then used by an extended version of the Modbat tool for model-based testing. We experimentally evaluate our search-based approach on a collection of examples, such as the ZooKeeper distributed service and PostgreSQL database system, by comparing it to the use of random search for test case generation. Our results show that test cases generated using our search-based approach can obtain more predictable and better state/transition coverage, find failures earlier, and provide improved path coverage.

We present a novel, non-intrusive approach to MC/DC coverage measurement using modern processor-based tracing facilities. Our approach does not require recompilation or instrumentation of the software under test. Instead, we use the Intel Processor Trace (Intel PT) facility present on modern Intel CPUs. Our tooling consists of the following parts: a frontend that detects so-called decisions (Boolean expressions) that are used in conditionals in C source code, a mapping from conditional jumps in the object code back to those decisions, and an analysis that computes satisfaction of the MC/DC coverage relation on those decisions from an execution trace. This analysis takes as input a stream of instruction addresses decoded from Intel PT trace data, which was recorded while running the software under test. We describe our architecture and discuss limitations and future work.

This paper extends our behavioural type and effect system for detecting deadlocks by polymorphism and formalizing type inference (with respect to lock types). Our inference is defined for a simple concurrent, first-order language. From the inferred effects, after suitable abstractions to keep the state space finite, we either obtain the verdict that the program will not deadlock, or that it may deadlock. We show soundness and completeness of the type inference.

We extend our previous approach to run-time verification of a single finite path against a formula in next-free Linear-Time Logic (LTL) with free variables and quantification. We discuss the design space of quantification and introduce a binary operator that binds values based on the current state. The binding semantics of propositions containing quantified variables is a pure top-down evaluation. The alternating binding automaton corresponding to a formula is evaluated in a breadth-first manner, allowing us to detect refuted formulae during execution.

Modern software applications ranging from enterprise to embedded systems are becoming increasingly complex, and require very high levels of dependability assurance. The most effective means to handle complexity is separation of concerns and incremental development, and assurance of dependability requires formal methods. We report here our experience on these issues in an application of a formal calculus, rCOS, to a component-based design of the point of sale system (POS). We demonstrate the possibility in scaling-up correctness by design and discuss how rCOS may be integrated with current and emerging software engineering tools.