Keeping Pace with the History of Evolving Runtime Models

Fundamental Approaches to Software Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-030-71500-7_13 ◽

2021 ◽

pp. 262-268

Author(s):

Lucas Sakizloglou ◽

Matthias Barkowsky ◽

Holger Giese

Keyword(s):

Memory Storage ◽

Tool Support ◽

Memory Consumption ◽

Modeling Framework ◽

Systematic Method ◽

Operation Modes ◽

History Of ◽

Graph Queries ◽

Key Features ◽

Runtime Models

AbstractStructural runtime models provide a snapshot of the constituents of a system and their state. Capturing the history of runtime models, i.e., previous snapshots, has been shown to be useful for a number of aims. Handling, however, history at runtime poses important challenges to tool support. We present the InTempo tool which is based on the Eclipse Modeling Framework and encodes runtime models as graphs. Key features of InTempo, such as, the integration of temporal requirements into graph queries, the in-memory storage of the model, and a systematic method to contain the model’s memory consumption, intend to address issues which seemingly place limitations on the available tool support. InTempo offers two operation modes which support both runtime and postmortem application scenarios.

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Incremental execution of temporal graph queries over runtime models with history and its applications

Software & Systems Modeling ◽

10.1007/s10270-021-00950-6 ◽

2021 ◽

Author(s):

Lucas Sakizloglou ◽

Sona Ghahremani ◽

Matthias Barkowsky ◽

Holger Giese

Keyword(s):

Temporal Logic ◽

Structural Changes ◽

Memory Storage ◽

Software Systems ◽

Multiple Model ◽

Attributed Graph ◽

History Of ◽

Graph Queries ◽

Temporal Graph ◽

Runtime Models

AbstractModern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. This setting has sparked an interest in solutions that use a runtime model which reflects the system state and operational context to monitor and adapt the system in reaction to changes during its runtime. Few solutions focus on the evolution of the model over time, i.e., its history, although history is required for monitoring temporal behaviors and may enable more informed decision-making. One reason is that handling the history of a runtime model poses an important technical challenge, as it requires tracing a part of the model over multiple model snapshots in a timely manner. Additionally, the runtime setting calls for memory-efficient measures to store and check these snapshots. Following the common practice of representing a runtime model as a typed attributed graph, we introduce a language which supports the formulation of temporal graph queries, i.e., queries on the ordering and timing in which structural changes in the history of a runtime model occurred. We present a querying scheme for the execution of temporal graph queries over history-aware runtime models. Features such as temporal logic operators in queries, the incremental execution, the option to discard history that is no longer relevant to queries, and the in-memory storage of the model, distinguish our scheme from relevant solutions. By incorporating temporal operators, temporal graph queries can be used for runtime monitoring of temporal logic formulas. Building on this capability, we present an implementation of the scheme that is evaluated for runtime querying, monitoring, and adaptation scenarios from two application domains.

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