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The complexity of cyber-physical systems is increasing, driven by integration of more functionality and trends towards mass-customization. This has resulted in complex systems with many variants that require long time to develop and are difficult to adapt to changing requirements and introduction of new technology. New methodologies are hence required to reduce development time, simplify customization for a particular customer, and improve evolvability both during development and after deployment. This chapter explains how these challenges are addressed by an approach to model-based engineering (MBE) based on domainspecific languages (DSLs). However, applying the approach in industry has resulted in 5 technical research questions, namely how to: RQ1) achieve modularity and reuse in a DSL ecosystem, RQ2) achieve consistency between model and realizations, RQ3) manage an evolving DSL eco-system, RQ4) ensure model quality, RQ5) ensure quality of generated code. The five research questions are explored in the context of the published state-ofthe-art, as well as practically investigated through a case study from the defense domain.

This volume contains the selected and revised versions of papers that have been presented at three international workshops co-located with the 18th edition of the International Conference on Software Engineering and Formal Methods (SEFM 2020). Because of the COVID-19 pandemic, both the conference and the workshops were held virtually. They were hosted by the Centre for Mathematics and Informatics (CWI) in Amsterdam, The Netherlands, and took place on September 14- 17, 2020. The SEFM 2020 international conference offered a virtual interactive platform for leading researchers and practitioners from academia, industry, and government to advance the state of the art in formal methods, to facilitate their uptake in the software industry, and to encourage their integration within practical software engineering methods and tools.

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics.This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

Model-based approaches promote the use of models and related artifacts (such as metamodels and model transformations) as central elements to tackle the complexity of building systems. Both in academia and in industry there is a growing need to efficiently i) store; ii) analyze; and ii) search & navigate, and iii) curate large collections of models. Such collections include for example large sets of software models such as the Lindholmen UML dataset [1], or of heterogeneous models in large MDE ecosystems and systems-of-systems, including e.g. software, hardware, and business models. The workshop Analytics and Mining of Model Repositories (AMMoRe) aims to gather modelling researchers and practitioners to discuss the emerging problems and propose solutions. The scope ranges from industrial reports and empirical analyses in the problem domain to novel cross-disciplinary approaches for large-scale analytics and management, e.g. exploiting techniques from data analytics, repository mining and machine learning.

Model-based languages such as MATLAB/Simulink play an essential role in the model-driven development of software systems. During their development, these systems can be subject to modification numerous times. For large-scale systems, to manually identify performed modifications is infeasible. However, their precise identification and subsequent validation is essential for the evolution of model-based systems. If not fully identified, modifications may cause unaccountable behavior as the system evolves and their redress can significantly delay the entire development process. In this paper, we propose a fully automated technique called Reverse Signal Propagation Analysis, which identifies and clusters variations within evolving MATLAB/Simulink models. With each cluster representing a clearly delimitable variation point between models, we allow model engineers not only to specifically focus on single variations, but by using their domain knowledge, to also relate and verify them. By identifying variation points, we assist model engineers in validating the respective parts and reduce the risk of improper system behavior as the system evolves. To assess the applicability of our technique, we present a feasibility study with real-world models from the automotive domain and show our technique to be very fast and highly precise.

Correctness-by-construction (CbC) is an approach for developing algorithms inline with rigorous correctness arguments. A high-level specification is evolved into an implementation in a sequence of small, tractable refinement steps guaranteeing the resulting implementation to be correct. CbC facilitates the design of algorithms that are more efficient and more elegant than code that is hacked into correctness. In this paper, we discuss another benefit of CbC, i.e., that it supports the deep comprehension of algorithm families. We organise the different refinements of the algorithms carried out during CbC-based design in a taxonomy. The constructed taxonomy provides a classification of the commonality and variability of the algorithm family and, hence, provides deep insights into their structural relationships. Such taxonomies together with the implementation of the algorithms as toolkits provide an excellent starting point for extractive and proactive software product line engineering.

Quantum leap matching is introduced as a generic pattern matching strategy for the single keyword exact pattern matching problem, that can be used on top of existing Boyer-Moore-style string matching algorithms. The cost of the technique is minimal: an additional shift table (of one dimension, for shifts in the opposite direction to the parent algorithm’s shifts), and the replacement of a simple table lookup assignment statement in the original algorithm with a similar conditional assignment. Together with each of the conventional shift table lookups, the additional shift table is typically also indexed on the text character that is at a distance of z away from the current sliding window. Under conditions that are identified, the returned values from the two shift tables allow a “quantum leap” of distance more than the length of the keyword for the next matching attempt. If the conditions are not met, then there is a fall back is to the traditional shift. Quick Search (by Sunday) is used as a case study to illustrate the technique. The performance of the derived “Quantum Leap Quick Search” algorithm is compared against Quick Search. When searching for shorter patterns over natural language and genomic texts, the technique improves on Quick Search’s time for most values of z. Improvements are also sometimes seen for various values of z on larger patterns. Most interestingly, under best case conditions it performs, on average, at about three times faster than Quick Search.

The paper discusses a language operation that we call context restriction. This operation is closely associated with context restriction rules (Kosken- niemi, 1983; Kiraz, 2000), right-arrow rules or implication rules (Kosken- niemi et al., 1992; Voutilainen, 1997) and the restriction operator (Beesley and Karttunen, 2003). The operation has been used in finite-state phonology and morphology in certain limited ways. A more general setting involves re- stricting overlapping occurrences of a center language under context condi- tions. Recently, star-free regular languages (and all regular languages) have been shown to be closed under context restrictions with such “overlapping centers” (Yli-Jyra ̈, 2003), but the construction involved is overly complex and becomes impractical when the number of operands grows. In this paper, we improve this recent result by presenting a more practical construction. This construction is not only simpler but it also leads to a generalization where contexts and centers may appear as conditions at both sides of the implication arrow (⇒): licensing conditions on the right-hand side specify the restriction and triggering conditions on the left-hand side regulate activation of the restriction. One application of the generalization is to facilitate splitting certain context restriction rules in grammars into a conjunction of separate rules.