Abstract MDE enables the centrality of the models in semi-automated development processes. However, its level of usage in industrial settings is still not adequate for the benefits MDE can introduce. This paper proposes a semi-automatic approach for the completion of high-level models in the lifecycle of critical systems, which exhibit an event-driven behaviour. The proposal suggests a specification guideline that starts from a partial SysML model of a system and on a set of requirements, expressed in the well-known Given–When–Then paradigm. On the basis of such requirements, the approach enables the semi-automatic generation of new SysML state machines model elements. Accordingly, the approach focuses on the completion of the state machines by adding proper transitions (with triggers, guards and effects) among pre-existing states. Also, traceability modelling elements are added to the model. Two case studies demonstrate the feasibility of the proposed approach.

Abstract In recent years, we have seen many performance fiascos in the deployment of new systems, such as the US health insurance web. This paper describes the functionality and architecture, as well as success stories, of a tool that helps address these types of issues. The tool allows assessing software designs regarding quality, in particular performance and reliability. Starting from a UML design with quality annotations, the tool applies model-transformation techniques to yield analyzable models. Such models are then leveraged by the tool to compute quality metrics. Finally, quality results, over the design, are presented to the engineer, in terms of the problem domain. Hence, the tool is an asset for the software engineer to evaluate system quality through software designs. While leveraging the Eclipse platform, the tool uses UML and the MARTE, DAM and DICE profiles for the system design and the quality modeling.

The sixth ACM Workshop on Challenges in Performance Methods for Software Development is co-located with the 12th ACM/SPEC International Conference on Performance Engineering (ICPE 2021), 19-23 April 2021. The Conference initially hosted in Rennes, France, will be a virtual event due to COVID-19. The purpose of the workshop is to open up new venues of research on methods for software developers to address performance problems. The software world is changing continuously, and there are new challenges every day. The acronym WOSP-C was chosen to recall the original WOSP, the ACM International Workshop on Software and Performance, which has been a co-organizer of ICPE since 2010.

Model-driven techniques can be used to automatically produce formal models from different views of a system realised by using several modelling languages and notations. Specifications are transformed into formal models so facilitating the analysis of complex system for design, validation or verification purposes. However, no single formalism suits for representing all system’s views. In particular, the assessment of non-functional properties often requires integrated modelling approaches. The ultimate goal of the research work described in this paper is to develop a comprehensive, theoretical and practical framework able to support the development and the integration of new or existing model-driven approaches for the automatic generation of multi-formalism models. This paper defines the core theoretical ideas on which the framework is based and demonstrates their concrete applicability to the development of a multi-formalism approach for performability assessment.

Model-driven engineering (MDE) often features quality assurance (QA) techniques to help developers creating software that meets reliability, efficiency, and safety requirements. In this paper, we consider the question of how quality-aware MDE should support data-intensive software systems. This is a difficult challenge, since existing models and QA techniques largely ignore properties of data such as volumes, velocities, or data location. Furthermore, QA requires the ability to characterize the behavior of technologies such as Hadoop/MapReduce, NoSQL, and stream-based processing, which are poorly understood from a modeling standpoint. To foster a community response to these challenges, we present the research agenda of DICE, a quality-aware MDE methodology for data-intensive cloud applications. DICE aims at developing a quality engineering tool chain offering simulation, verification, and architectural optimization for Big Data applications. We overview some key challenges involved in developing these tools and the underpinning models.

Critical infrastructures, usually designed to handle disruptions caused by human errors or random acts of nature, define assets whose normal operation must be guaranteed to maintain its essential services for human daily living. Malicious intended attacks to these targets need to be considered during system design. To face these situations, defence plans must be developed in advance. In this paper, we present a Unified Modelling Language profile, named SecAM, that enables the modelling and security specification for critical infrastructures during the early phases (requirements, design) of system development life cycle. SecAM enables security assessment, through survivability analysis, of different security solutions before system deployment. As a case study, we evaluate the survivability of the Saudi Arabia crude-oil network under two different attack scenarios. The stochastic analysis, carried out with Generalized Stochastic Petri nets, quantitatively estimates the minimization of attack damages on the crude-oil network.

Many artificial systems can be modeled as discrete dynamic systems in which resources are shared among different tasks. The performance of such systems, which is usually a system requirement, heavily relies on the number and distribution of such resources. The goal of this paper is twofold: first, to design a technique to estimate the steady-state performance of a given system with shared resources, and second, to propose a heuristic strategy to distribute shared resources so that the system performance is enhanced as much as possible. The systems under consideration are assumed to be large systems, such as service-oriented architecture (SOA) systems, and modeled by a particular class of Petri nets (PNs) called process PNs. In order to avoid the state explosion problem inherent to discrete models, the proposed techniques make intensive use of linear programming (LP) problems.

Software is often embedded in dynamic contexts where it is subjected to high variable, non-stable, and usually bursty workloads. A key requirement for a software system is to be able to self-react to workload changes by adapting its behavior dynamically, to ensure both the correct functionalities and the required performance. Research on fitting variable workload traces into formal models has been carried out using Markovian Modulated Poisson Processes (MMPP). These works concentrate on modeling stable workload states, but accurate modeling of transient times still deserves attention since they are critical moments for the self-adaptation. In this work, we build on research in the area of MMPP trace fitting and we propose a Petri net fine-grained model for highly variable workloads that also accounts for transient times. We analyze differences between models of adaptive software that accurately represent workload state changes and models that do not. We evaluate their performance and availability and compare the results.

Service Oriented Architecture (SOA) is a paradigm where applications are built on services offered by third party providers. Behavior of providers evolves and makes a challenge the performance prediction of SOA applications. A proper decision about when a provider should be substituted can dramatically improve the performance of the application. We propose hidden Markov models (HMM) to help service integrators to foretell the current state of third-parties. The paper leverages different algorithms that change providers based on predictions about their states. We also integrate these algorithms and HMMs in an architectural solution to coordinate them with other challenges in the SOA world.

Software systems that do not meet their timing constraints can cause risks. In this work, we propose a comprehensive method for assessing the risk of timing failure by evaluating the software design. We show how to apply best practises in software engineering and well-known Time Petri Net (TPN) modeling and analysis techniques, and we demonstrate the effectiveness of the method with reference to a case study in the domain of real-time embedded systems. The method customizes the Australian standard risk management process, where the system context is the UML-based software specification, enriched with standard MARTE profile annotations to capture nonfunctional system properties. During the risk analysis, a TPN is derived, via model transformation, from the software design specification and TPN bound techniques are applied to estimate the probability of timing failure. TPN bound techniques are also exploited, within the risk evaluation and treatment steps, to identify the risk causes in the software design.

Nowadays, there exist web sites that allow users to retrieve and install software in an easy way. The performance of these sites may be poor if they are used in wireless networks; the reason is the inadequate use of the net resources they need. If this kind of systems are designed using mobile agent technology the previous problem might be avoided. In this paper, we present a comparison between the performance of a software retrieval system especially designed to be used in wireless networks (e.g., mobile computers) and the performance of a software retrieval system similar to the well-known Tucows.com or Download.com web sites.