Towards Automatic Support of Software Model Evolution with Large Language~Models

Modeling structure and behavior of software systems plays a crucial role, in various areas of software engineering. As with other software engineering artifacts, software models are subject to evolution. Supporting modelers in evolving models by model completion facilities and providing high-level edit operations such as frequently occurring editing patterns is still an open problem. Recently, large language models (i.e., generative neural networks) have garnered significant attention in various research areas, including software engineering. In this paper, we explore the potential of large language models in supporting the evolution of software models in software engineering. We propose an approach that utilizes large language models for model completion and discovering editing patterns in model histories of software systems. Through controlled experiments using simulated model repositories, we conduct an evaluation of the potential of large language models for these two tasks. We have found that large language models are indeed a promising technology for supporting software model evolution, and that it is worth investigating further in the area of software model evolution

Mining domain-specific edit operations from model repositories with applications to semantic lifting of model differences and change profiling

Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-) automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specifc edit operations are the ones that compress the model diferences. It employs frequent subgraph mining to discover frequent structures in model diference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model diferences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model diferences and change profles) for the discovered edit operations in this industrial setting. We fnd that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models

Solving heterogeneity for a successful service market

Diese Dissertation ist im Kontext eines neuen Paradigmas im Software Engineering mit dem Namen On-The-Fly Computing entstanden. OTF Computing basiert auf der Idee von spezialisierten On-The-Fly Märkten. OTF Märkte haben unterschiedliche Eigenschaften und die Marktakteure in diesen Märkten benutzen verschiedene Modellierungstechniken für das Service Engineering. Diese Unterschiede resultieren in Heterogenität und erschweren deshalb die Ausführung von automatisierten Marktoperationen, da Servicebeschreibungen nicht automatisch miteinander verglichen werden können. Für das beschriebene Problem bietet diese Dissertation eine Lösung um einen erfolgreichen OTF Markt zu ermöglichen. Für die Vergleichbarkeit von Servicebeschreibungen in einem OTF Markt wird eine formale Zwischenrepräsentation (Kernsprache) eingeführt. Die Marktoperationen werden auf Basis der Kernsprache definiert, die die optimale Ausführung der automatisierten Marktoperationen in einem OTF Markt unterstützt. Der erste Beitrag dieser Dissertation ist der Ansatz Language Optimizer (LOpt). LOpt nutzt als Basis eine Kernsprache, die strukturelle, verhaltensbezogene und nicht-funktionale Serviceeigenschaften beinhaltet. LOpt konfiguriert diese Sprache basierend auf formalisierten Markteigenschaften und einer Wissensbasis mit Konfigurationsexpertise, um eine optimale Kernsprache zur Servicespezifikation im jeweiligen OTF Markt zu erstellen. Der zweite Beitrag dieser Dissertation ist die Anwendung des Model Transformation By-Example Ansatzes um den Marktakteuren ohne Expertise im Sprachdesign Transformationen von ihren proprietären Sprachen in die optimale Kernsprache zu ermöglichen. Der beschriebene Ansatz generiert Transformationen auf Basis von Beispielabbildungen zwischen Servicebeschreibungen zweier Sprachen. Dabei wird die Idee genetischer Algorithmen angewendet.This PhD thesis is written in the context of a new software development paradigm called On-The-Fly Computing. It is based on the idea of specialized service markets called On-The-Fly (OTF) markets. OTF markets have different properties and their participants use different modeling techniques to perform the activity of service engineering. Such differences result in heterogeneity in OTF markets and complicate the execution of automated market operations like service matching as service specifications cannot be automatically compared with each other. This PhD thesis proposes a solution to cope with the mentioned heterogeneity to foster the success of OTF markets and the OTF Computing paradigm. In order to achieve the comparability of specifications in an OTF market, a formal intermediate representation called core language is introduced. Automated market operations are defined on a core language that optimally supports the execution of these operations in this market. The first contribution of this PhD thesis is the approach language Optimizer (LOpt), which supports the systematic design of a service specification language optimal for the execution of automated market operations in an OTF market. LOpt uses a comprehensive core language covering various structural, behavioral, and non-functional service properties. LOpt performs a configuration of this language based on formalized market properties and a knowledge base containing the configuration expertise. The second contribution of this PhD thesis is the application of the Model Transformations By-Example technique to define transformations from proprietary specification languages of market actors to the optimal core language. The approach generates transformations based on example mappings between concrete specifications in both languages given by market actors. ...by Svetlana Arifulina, M.Sc. ; Thesis Supervisors: Prof. Dr. Gregor Engels and Jun. Prof. Dr. Heiko HamannTag der Verteidigung: 08.12.2016Universität Paderborn, Univ., Dissertation, 201

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.