State machines are used in engineering many types of software-intensive systems. UML State Machines extend simple finite state machines with powerful constructs. Among the many extensions, there is one seemingly simple and innocent language construct that fundamentally changes state machines’ reactive model of computation: doActivity behaviors. DoActivity behaviors describe behavior that is executed independently from the state machine once entered in a given state, typically modeling complex computation or communication as background tasks. However, the UML specification or textbooks are vague about how the doActivity behavior construct should be appropriately used. This lack of guidance is a severe issue as, when improperly used, doActivities can cause concurrent, non-deterministic bugs that are especially challenging to find and could ruin a seemingly correct software design. The Precise Semantics of UML State Machines (PSSM) specification introduced detailed operational semantics for state machines. To the best of our knowledge, there is no rigorous review yet of doActivity's semantics as specified in PSSM. We analyzed the semantics by collecting evidence from cross-checking the text of the specification, its semantic model and executable test cases, and the simulators supporting PSSM. We synthesized insights about subtle details and emergent behaviors relevant to tool developers and advanced modelers. We reported inconsistencies and missing clarifications in more than 20 issues to the standardization committee. Based on these insights, we studied 11 patterns for doActivities detailing the consequences of using a doActivity in a given situation and discussing countermeasures or alternative design choices. We hope that our analysis of the semantics and the patterns help vendors develop conformant simulators or verification tools and engineers design better state machine models.

Abstract Modelling languages play a central role in developing complex, critical systems. A precise, comprehensible, and high-quality modelling language specification is essential to all stakeholders using, implementing, or extending the language. Many good practices can be found that improve the understandability or consistency of the languages’ semantics. However, designing a modelling language intended for a large audience is still challenging. In this paper, we investigate the challenges and typical issues with assessing the specifications of behavioural modelling language semantics. Our key insight is that the various stakeholder’s understandings of the language’s semantics are often misaligned, and the semantics defined in various artefacts (simulators, test suites) are inconsistent. Therefore assessment of semantics should focus on identifying and resolving these inconsistencies. To illustrate these challenges and techniques, we assessed parts of a state-of-the-art specification for a general-purpose modelling language, the Precise Semantics of UML State Machines (PSSM). We reviewed the text of the specification, analysed and executed PSSM’s conformance test suite, and categorised our experiences according to questions generally relevant to modelling languages. Finally, we made recommendations for improving the development of future modelling languages by representing the semantic domain and traces more explicitly, applying diverse test design techniques to obtain conformance test suites, and using various tools to support early-phase language design.

Abstract To cope with the increased complexity of systems, models are used to capture what is considered the essence of a system. Such models are typically represented as a graph, which is queried to gain insight into the modelled system. Often, the results of these queries need to be adjusted according to updated requirements and are therefore a subject of maintenance activities. It is thus necessary to support writing model queries with adequate languages. However, in order to stay meaningful, the analysis results need to be refreshed as soon as the underlying models change. Therefore, a good execution speed is mandatory in order to cope with frequent model changes. In this paper, we propose a benchmark to assess model query technologies in the presence of model change sequences in the domain of social media. We present solutions to this benchmark in a variety of 11 different tools and compare them with respect to explicitness of incrementalization, asymptotic complexity and performance.