Envisioning Model-Based Performance Engineering Frameworks.

Abstract Our daily activities depend on complex software systems that must guarantee certain performance. Several approaches have been devised in the last decade to validate software systems against performance requirements. However, software designers still encounter problems in the interpretation of performance analysis results (e.g., mean values, probability distribution functions) and in the definition of design alternatives (e.g., to split a software component in two and redeploy one of them) aimed at fulfilling performance requirements. This paper describes a general model-based performance engineering framework to support designers in dealing with such problems aimed at enhancing the system. The framework relies on a formalization of the knowledge needed in order to characterize performance flaws and provide alternative system design. Such knowledge can be instantiated based on the techniques devised for interpreting performance analysis results and providing feedback to designers. Three techniques are considered in this paper for instantiating the framework and the main challenges to face during such process are pointed out and discussed

Introducing Interactions in Multi-Objective Optimization of Software Architectures

Software architecture optimization aims to enhance non-functional attributes like performance and reliability while meeting functional requirements. Multi-objective optimization employs metaheuristic search techniques, such as genetic algorithms, to explore feasible architectural changes and propose alternatives to designers. However, the resource-intensive process may not always align with practical constraints. This study investigates the impact of designer interactions on multi-objective software architecture optimization. Designers can intervene at intermediate points in the fully automated optimization process, making choices that guide exploration towards more desirable solutions. We compare this interactive approach with the fully automated optimization process, which serves as the baseline. The findings demonstrate that designer interactions lead to a more focused solution space, resulting in improved architectural quality. By directing the search towards regions of interest, the interaction uncovers architectures that remain unexplored in the fully automated process

Many-Objective Optimization of Non-Functional Attributes based on Refactoring of Software Models

Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. In this context, software refactoring is a crucial activity within development life-cycles where requirements and functionalities rapidly evolve. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on software, as for trade-off between performance and reliability (or other non-functional attributes). In such cases, multi-objective optimization can provide the designer with a wider view on these trade-offs and, consequently, can lead to identify suitable refactoring actions that take into account independent or even competing objectives. In this paper, we present an approach that exploits NSGA-II as the genetic algorithm to search optimal Pareto frontiers for software refactoring while considering many objectives. We consider performance and reliability variations of a model alternative with respect to an initial model, the amount of performance antipatterns detected on the model alternative, and the architectural distance, which quantifies the effort to obtain a model alternative from the initial one. We applied our approach on two case studies: a Train Ticket Booking Service, and CoCoME. We observed that our approach is able to improve performance (by up to 42\%) while preserving or even improving the reliability (by up to 32\%) of generated model alternatives. We also observed that there exists an order of preference of refactoring actions among model alternatives. We can state that performance antipatterns confirmed their ability to improve performance of a subject model in the context of many-objective optimization. In addition, the metric that we adopted for the architectural distance seems to be suitable for estimating the refactoring effort.Comment: Accepted for publication in Information and Software Technologies. arXiv admin note: substantial text overlap with arXiv:2107.0612

A UML Profile for the Design, Quality Assessment and Deployment of Data-intensive Applications

Big Data or Data-Intensive applications (DIAs) seek to mine, manipulate, extract or otherwise exploit the potential intelligence hidden behind Big Data. However, several practitioner surveys remark that DIAs potential is still untapped because of very difficult and costly design, quality assessment and continuous refinement. To address the above shortcoming, we propose the use of a UML domain-specific modeling language or profile specifically tailored to support the design, assessment and continuous deployment of DIAs. This article illustrates our DIA-specific profile and outlines its usage in the context of DIA performance engineering and deployment. For DIA performance engineering, we rely on the Apache Hadoop technology, while for DIA deployment, we leverage the TOSCA language. We conclude that the proposed profile offers a powerful language for data-intensive software and systems modeling, quality evaluation and automated deployment of DIAs on private or public clouds

Model-based resource analysis and synthesis of service-oriented automotive software architectures

Context Automotive software architectures describe distributed functionality by an interaction of software components. One drawback of today\u27s architectures is their strong integration into the onboard communication network based on predefined dependencies at design time. The idea is to reduce this rigid integration and technological dependencies. To this end, service-oriented architecture offers a suitable methodology since network communication is dynamically established at run-time. Aim We target to provide a methodology for analysing hardware resources and synthesising automotive service-oriented architectures based on platform-independent service models. Subsequently, we focus on transforming these models into a platform-specific architecture realisation process following AUTOSAR Adaptive. Approach For the platform-independent part, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations, i. e., mapping services to hardware resources at an early development stage. We refine these configurations to AUTOSAR Adaptive software architecture models representing the necessary input for a subsequent implementation process for the platform-specific part. Result We present deployment configurations that are optimal for the usage of a given set of computing resources currently under consideration for our next generation of E/E architecture. We also provide simulation results that demonstrate the ability of these configurations to meet the run time requirements. Both results helped us to decide whether a particular configuration can be implemented. As a possible software toolchain for this purpose, we finally provide a prototype. Conclusion The use of models and their analysis are proper means to get there, but the quality and speed of development must also be considered