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Microservices are quite widely impacting on the software industry in recent years. Rapid evolution and continuous deployment represent specific benefits of microservice-based systems, but they may have a significant impact on non-functional properties like performance. Despite the obvious relevance of this property, there is still a lack of systematic approaches that explicitly take into account performance issues in the lifecycle of microservice-based systems. In such a context of evolution and re-deployment, Model-Driven Engineering techniques can provide major support to various software engineering activities, and in particular they can allow managing the relationships between a running system and its architectural model. In this paper, we propose a model-driven integrated approach that exploits traceability relationships between the monitored data of a microservice-based running system and its architectural model to derive recommended refactoring actions that lead to performance improvement. The approach has been applied and validated on two microservice-based systems, in the domain of e-commerce and ticket reservation, respectively, whose architectural models have been designed in UML profiled with MARTE.

Context: Software architecture refactoring can be induced by multiple reasons, such as satisfying new functional requirements or improving non-functional properties. Multi-objective optimization approaches have been widely used in the last few years to introduce automation in the refactoring process, and they have revealed their potential especially when quantifiable attributes are targeted. However, the effectiveness of such approaches can be heavily affected by configuration characteristics of the optimization algorithm, such as the composition of solutions. Objective: In this paper, we analyze the behavior of E A S I E R , which is an Evolutionary Approach for Software archItecturE Refactoring, while varying its configuration characteristics, with the objective of studying its potential to find near-optimal solutions under different configurations. Method: In particular, we use two different solution space inspection algorithms (i.e., N S G A − I I and S P E A 2 ) while varying the genome length and the solution composition. Results: We have conducted our experiments on a specific case study modeled in AEmilia ADL, on which we have shown the ability of E A S I E R to identify performance-critical elements in the software architecture where refactoring is worth to be applied. Beside this, from the comparison of multi-objective algorithms, N S G A − I I has revealed to outperform S P E A 2 in most of cases, although the latter one is able to induce more diversity in the proposed solutions. Conclusion: Our results show that the E A S I E R thoroughly automated process for software architecture refactoring allows to identify configuration contexts of the evolutionary algorithm in which multi-objective optimization more effectively finds near-optimal Pareto solutions.

Refactoring is often needed to ensure that software systems meet their performance requirements in deployments with different operational profiles, or when these operational profiles are not fully known or change over time. This is a complex activity in which software engineers have to choose from numerous combinations of refactoring actions. Our paper introduces a novel approach that uses performance antipatterns and stochastic modelling to support this activity. The new approach computes the performance antipatterns present across the operational profile space of a software system under development, enabling engineers to identify operational profiles likely to be problematic for the analysed design, and supporting the selection of refactoring actions when performance requirements are violated for an operational profile region of interest. We demonstrate the application of our approach for a software system comprising a combination of internal (i.e., in-house) components and external third-party services.

Performance in heterogeneous service-based systems shows non-determistic trends. Even for the same request type, latency may vary from one request to another. These variations can occur due to several reasons on different levels of the software stack: operating system, network, software libraries, application code or others. Furthermore, a request may involve several Remote Procedure Calls (RPC), where each call can be subject to performance variation. Performance analysts inspect distributed traces and seek for recurrent patterns in trace attributes, such as RPCs execution time, in order to cluster traces in which variations may be induced by the same cause. Clustering "similar" traces is a prerequisite for effective performance debugging. Given the scale of the problem, such activity can be tedious and expensive. In this paper, we present an automated approach that detects relevant RPCs execution time patterns associated to request latency degradation, i.e. latency degradation patterns. The presented approach is based on a genetic search algorithm driven by an information retrieval relevance metric and an optimized fitness evaluation. Each latency degradation pattern identifies a cluster of requests subject to latency degradation with similar patterns in RPCs execution time. We show on a microservice-based application case study that the proposed approach can effectively detect clusters identified by artificially injected latency degradation patterns. Experimental results show that our approach outperforms in terms of F-score a state-of-art approach for latency profile analysis and widely popular machine learning clustering algorithms. We also show how our approach can be easily extended to trace attributes other than RPC execution time (e.g. HTTP headers, execution node, etc.).

Context Software refactoring is a common practice aimed at addressing requirements or fixing bugs during the software development. While refactoring related to functional requirements has been widely studied in the last few years, non-functional-driven refactoring is still critical, mostly because non-functional characteristics of software are hard to assess and appropriate refactoring actions can be difficult to identify. In the context of performance, which is the focus of this paper, antipatterns represent effective instruments to tackle this issue, because they document common mistakes leading to performance problems as well as their solutions. Objective In order to effectively reuse the knowledge beyond performance antipatterns, automation is required to detect and remove them. In this paper we introduce a framework that enables, in an unique tool context, the refactoring of software models driven by performance antipattern detection and removal. Method We have implemented, within the EPSILON platform, detection rules and refactoring actions on UML models for a set of well-known performance antipatterns. By exploiting the EPSILON languages to check properties and apply refactoring on models, we enable three types of refactoring sessions. Results We experiment our framework on a Botanical Garden Management System to show, on one side, that antipatterns can effectively drive software refactoring towards models that satisfy performance requirements and, on the other side, that the automation introduced by EPSILON-based sessions enables to inspect multiple paths and to propose a variety of solutions. Conclusion This work demonstrates that automation in performance-driven software model refactoring can be beneficial, and that performance antipatterns can be powerful instruments in the hands of software engineers for detecting (and solving) performance problems usually hidden to traditional bottleneck analysis. This work also opens the road to the integration of well-known techniques for software refactoring driven by functional requirements with novel techniques addressing non-functional requirements like performance.

Context awareness is a first-class attribute of today software systems. Indeed, many applications need to be aware of their context in order to adapt their structure and behavior for offering the best quality of service even in case the software and hardware resources are limited. Modeling the context, its evolution, and its influence on the services provided by (possibly resource constrained) applications are becoming primary activities throughout the whole software life cycle, although it is still difficult to capture the multidimensional nature of context. We propose a framework for modeling and reasoning on the context and its evolution along multiple dimensions. Our approach enables (1) the representation of dependencies among heterogeneous context attributes through a formally defined semantics for attribute composition and (2) the stochastic analysis of context evolution. As a result, context can be part of a model-based software development process, and multidimensional context analysis can be used for different purposes, such as non-functional analysis. We demonstrate how certain types of analysis, not feasible with context-agnostic approaches, are enabled in our framework by explicitly representing the interplay between context evolution and non-functional attributes. Such analyses allow the identification of critical aspects or design errors that may not emerge without jointly taking into account multiple context attributes. The framework is shown at work on a case study in the eHealth domain.

Context: While the performance analysis of a software architecture is a quite well-assessed task nowadays, the issue of interpreting the performance results for providing feedback to software architects is still very critical. Performance antipatterns represent effective instruments to tackle this issue, because they document common mistakes leading to performance problems as well as their solutions.Objective: Up today performance antipatterns have been only studied in the context of software modeling languages like UML, whereas in this manuscript our objective is to catch them in the context of ADL-based software architectures to investigate their effectiveness.Method: We have implemented a model-driven approach that allows the automatic detection of four performance antipatterns in AEmilia, that is a stochastic process algebraic ADL for performance-aware component-oriented modeling of software systems.Results: We evaluate the approach by applying it to three case studies in different application domains. Experimental results demonstrate the effectiveness of our approach to support the performance improvement of ADL-based software architectures.Conclusion: We can conclude that the detection of performance antipatterns, from the earliest stages of software development, represents an effective instrument to tackle the issue of identifying flaws and improving system performance.

Model-Driven Engineering techniques may achieve a major support to the software development when they allow to manage relationships between a running system and its architectural model. These relationships can be exploited for different goals, such as the software evolution due to new functional requirements. In this paper, we define and use relationships that work as support to the performance improvement of a running system. In particular, we combine: (i) a bidirectional model transformation framework tailored to define relationships between performance monitoring data and an architectural model, with (ii) a technique for detecting performance antipatterns and for suggesting architectural changes, aimed at removing performance problems identified on the basis of runtime information. The result is an integrated approach that exploits traceability relationships between the monitoring data and the architectural model to derive recommended refactoring solutions for the system performance improvement. The approach has been applied to an e-commerce application based on microservices that has been designed by means of UML software models profiled with MARTE.

The satisfaction of ever more stringent performance requirements is one of the main reasons for software evolution. However, it is complex to determine the primary causes of performance degradation, because they may depend on the joint combination of multiple factors (e.g., workload, software deployment, hardware utilization). With the increasing complexity of software systems, classical bottleneck analysis shows limitations in capturing complex performance problems. Hence, in the last decade, the detection of performance antipatterns has gained momentum as an effective way to identify performance degradation causes. We introduce PADRE (Performance Antipattern Detection and REfactoring), that is a tool for: (i) detecting performance antipattern in UML models, and (ii) refactoring models with the aim of removing the detected antipatterns. PADRE has been implemented within Epsilon, an open-source platform for model-driven engineering. It is based on a methodology that allows performance antipattern detection and refactoring within the same implementation context.

Multi-objective optimization has demonstrated, in the last few years, to be an effective paradigm to tackle different architectural problems, such as service selection, composition and deployment. In particular, multi-objective approaches for searching architectural configurations that optimize quality properties (such as performance, reliability and cost) have been introduced in the last decade. However, a relevant amount of complexity is introduced in this context when performance are considered, often due to expensive iterative generation and solution of performance models. In this paper we introduce EASIER (Evolutionary Approach for multi-objective Software archItecturE Refactoring), that is an approach for applying architecture refactoring based on performance aspects and on the cost of architectural changes. In order to mitigate the complexity related to performance, we exploit the knowledge dwelling in performance antipatterns for more effectively driving the evolutionary algorithm towards optimal solutions. We have implemented our approach on AEmilia ADL, so to carry out performance analysis, antipatterns detection and architecture refactoring within the same environment. We demonstrate the effectiveness and applicability of our approach through a non-trivial experimentation on a case study.

Model-Driven Engineering techniques may achieve a major support to the software development when they allow to manage relationships between a running system and its architectural model. These relationships can be exploited for different goals, such as the software evolution due to new functional requirements. In this paper, we define and use relationships that work as support to the performance improvement of a running system. In particular, we combine: (i) a bidirectional model transformation framework tailored to define relationships between performance monitoring data and an architectural model, with (ii) a technique for detecting performance antipatterns and for suggesting architectural changes, aimed at removing performance problems identified on the basis of runtime information. The result is an integrated approach that exploits traceability relationships between the monitoring data and the architectural model to derive recommended refactoring solutions for the system performance improvement. The approach has been applied to an e-commerce application based on microservices that has been designed by means of UML software models profiled with MARTE.

International audience; Context: Managing Non-Functional Requirements (NFRs) in software projects is challenging, and projects that adopt Model-Driven Development (MDD) are no exception. Although several methods and techniques have been proposed to face this challenge, there is still little evidence on how NFRs are handled in MDD by practitioners. Knowing more about the state of the practice may help researchers to steer their research and practitioners to improve their daily work. Objective: In this paper, we present our findings from an interview-based survey conducted with practitioners working in 18 different companies from 6 European countries. From a practitioner's point of view, the paper shows what barriers and benefits the management of NFRs as part of the MDD process can bring to companies, how NFRs are supported by MDD approaches, and which strategies are followed when (some) types of NFRs are not supported by MDD approaches. Results: Our study shows that practitioners perceive MDD adoption as a complex process with little to no tool support for NFRs, reporting productivity and maintainability as the types of NFRs expected to be supported when MDD is adopted. But in general, companies adapt MDD to deal with NFRs. When NFRs are not supported, the generated code is sometimes changed manually, thus compromising the maintainability of the software developed. However, the interviewed practitioners claim that the benefits of using MDD outweight the extra effort required by these manual adaptations. Conclusion: Overall, the results indicate that it is important for practitioners to handle NFRs in MDD, but further research is necessary in order to lower the barrier for supporting a broad spectrum of NFRs with MDD. Still, much conceptual and tool implementation work seems to be necessary to lower the barrier of integrating the broad spectrum of NFRs in practice.

Context With the ever-increasing evolution of software systems, their architecture is subject to frequent changes due to multiple reasons, such as new requirements. Appropriate architectural changes driven by non-functional requirements are particularly challenging to identify because they concern quantitative analyses that are usually carried out with specific languages and tools. A considerable number of approaches have been proposed in the last decades to derive non-functional analysis models from architectural ones. However, there is an evident lack of automation in the backward path that brings the analysis results back to the software architecture. Objective In this paper, we propose a model-driven approach to support designers in improving the availability of their software systems through refactoring actions. Method The proposed framework makes use of bidirectional model transformations to map UML models onto Generalized Stochastic Petri Nets (GSPN) analysis models and vice versa. In particular, after availability analysis, our approach enables the application of model refactoring, possibly based on well-known fault tolerance patterns, aimed at improving the availability of the architectural model. Results We validated the effectiveness of our approach on an Environmental Control System. Our results show that the approach can generate: (i) an analyzable availability model from a software architecture description, and (ii) valid software architecture models back from availability models. Finally, our results highlight that the application of fault tolerance patterns significantly improves the availability in each considered scenario. Conclusion The approach integrates bidirectional model transformation and fault tolerance techniques to support the availability-driven refactoring of architectural models. The results of our experiment showed the effectiveness of the approach in improving the software availability of the system.

The reconfiguration of Wireless Sensor Networks is a well-known problem that has been tackled in the last few years mostly with approaches operating at the network level. In this paper, we introduce an approach for the automated reconfiguration of WSNs, driven by energy problems, at the level of software application. In particular, we define reconfiguration actions with Proteus, that is a framework to specify and implement reconfiguration plans on top of Agilla, that is a mobile agent middleware for Wireless Sensor Networks. A Reconfiguration Engine acts on agents within a WSN to activate reconfiguration plans when required, e.g. when the batteries of several sensors located in a critical geographical area are exhausted. We have started to experiment the approach on a real application.

Refactoring has become a valuable activity during the software development lifecycle, because it can be induced by different causes, like new requirements or quality improvement. In code-based development contexts this activity has been widely studied, whereas in model-driven ones, where models are first-class development entities, there are many issues yet to be tackled. In this paper, we present a metamodel that supports the specification of pre- and post- conditions of model refactoring actions, and the automated derivation and verification of such conditions in specific modeling languages. Our work is aimed at helping users to implement refactoring actions in the adopted modelling language by providing an environment for guaranteeing the feasibility of refactoring actions. Our primary focus is on the definition of applicable sequences of refactoring actions, rather than on the user-driven step-by-step application of refactoring actions. As an example, we illustrate the applicability of our metamodel for UML models refactoring.

Software architecture is nowadays subject to frequent changes due to multiple reasons, such as evolution induced by new requirements. Architectural changes driven by non-functional requirements are particularly difficult to identify, because they attain quantitative analyses that are usually carried out with specific languages and tools. A considerable number of approaches, based on model transformations, have been proposed in the last decades to derive non-functional models from software architectural descriptions. However, there is a clear lack of automation in the backward path that brings the analysis results back to the software architecture. In this paper we address this problem in the context of software availability. We introduce a bidirectional model transformation between UML State Machines (SM), annotated with availability properties, and Generalized Stochastic Petri Nets (GSPN). Such transformation, implemented in the JTL language, is used both to derive a GSPN-based availability model from a SM-based software architecture and, after the analysis, to propagate back on the SM the changes carried out on the GSPN. We demonstrate the effectiveness of our approach on an Environmental Control System to which we apply well-known fault tolerance patterns aimed at improving its software availability.

Cyber-foraging is a technique to enable mobile de- vices to extend their computing power and storage by offloading computation or data to more powerful servers located in the cloud or in single-hop proximity. While there is a large amount of research in this area, the reality is that there are not many deployed, operational cyber-foraging systems. As these systems become more prevalent due to their proven benefits, combined with the emergence of micro data centers and edge clouds, a need will arise for guidance on their architecture and development. To start providing this guidance, we present a decision model based on a mapping of functional and non-functional requirements for cyber-foraging systems to a set of architectural tactics. The decision model was validated by developers to obtain an expert opinion on its correctness and usefulness for guiding the architecture, design, and evolution of cyber-foraging systems that meet their intended functional and non-functional requirements, while understanding the effects of decisions.

Antipatterns are conceptually similar to patterns in that they document recurring solutions to common design problems. Software performance antipatterns document common performance problems in the design as well as their solutions. The definition of performance antipatterns concerns software properties that can include static, dynamic, and deployment aspects. To make use of such knowledge, we propose an approach that helps software architects to identify and solve performance antipatterns. Our approach provides software performance feedback to architects, since it suggests the design alternatives that allow overcoming the detected performance problems. The feedback process may be quite complex since architects may have to assess several design options before achieving the architectural model that best fits the end-user expectations. In order to optimise such process we introduce a ranking methodology that identifies, among a set of detected antipatterns, the ''guilty'' ones, i.e. the antipatterns that more likely contribute to the violation of specific performance requirements. The introduction of our ranking process leads the system to converge towards the desired performance improvement by discarding a consistent part of design alternatives. Four case studies in different application domains have been used to assess the validity of the approach.

Although performance represents a crucial non-functional attribute of software, few model-based approaches have been introduced up today for reducing the gap between performance analysis results (e.g., mean response time) and the feedback expected by software engineers when performance problems are detected (i.e., refactoring actions). However, existing approaches aimed at performance-driven refactoring of software models suffer from fragmentation across different paradigms, languages, and meta-models. This paper aims at reducing such fragmentation by exploiting the EPSILON environment, which provides a suite of languages for checking properties and applying refactoring on models. In particular, we introduce automation aimed at translating performance antipattern detection rules and refactoring actions among three EPSILON languages. Such automation helps to reduce code writing effort, in the context of performance-driven refactoring of UML models, while exploiting the specific support provided by the different execution semantics of considered languages.

Control theory has recently started to be applied to software engineering domain, mostly for managing the behavior of adaptive software systems under external disturbances. In general terms, the main advantage of control theory is that it can be formally proven that controllers achieve their goals (with certain characteristics), whereas the price to pay is that controllers and system-to-be-controlled have to be modeled by equations. The investigation of how suited are control theory techniques to address performance problems is, however, still at the beginning. In this paper we devise the main challenges behind the adoption of control theory in the context of Software Performance Engineering applied to adaptive software systems.

The problem of interpreting the results of performance analysis is quite critical in the software performance domain. Mean values, variances and probability distributions are hard to interpret for providing feedback to software architects. Instead, what architects expect are solutions to performance problems, possibly in the form of architectural alternatives (e.g. split a software component in two components and re-deploy one of them). In a software performance engineering process, the path from analysis results to software design or implementation alternatives is still based on the skills and experience of analysts. In this paper, we propose an approach for the generation of feedback based on performance antipatterns. In particular, we focus on the representation and detection of antipatterns. To this goal, we model performance antipatterns as logical predicates and we build an engine, based on such predicates, aimed at detecting performance antipatterns in an XML representation of the software system. Finally, we show the approach at work on a case study.

Building a software architecture that meets functional requirements is a quite consolidated activity, whereas keeping high quality attributes is still an open challenge. In this paper we introduce an optimization framework that supports the decision whether to buy software components or to build them in-house upon designing a software architecture. We devise a non-linear cost/quality optimization model based on decision variables indicating the set of architectural components to buy and to build in order to minimize the software cost while keeping satisfactory values of quality attributes. From this point of view, our tool can be ideally embedded into a Cost Benefit Analysis Method to provide decision support to software architects. The novelty of our approach consists in building costs and quality attributes on a common set of decision variables related to software development. We start from a special case of the framework where the quality constraints are related to the delivery time and the product reliability, and the model solution also devises the amount of unit testing to be performed on built components. We generalize the framework formulation to represent a broader class of architectural cost-minimization problems under quality constraints, and discuss advantages and limitations of such approach.

The ability to predict Quality of Service (QoS) of a software architecture supports a large set of decisions across multiple lifecycle phases that span from design through implementation-integration to adaptation phase. However, due to the different amount and type of information available, different prediction approaches can be introduced in each phase. A major issue in this direction is that QoS attribute cannot be analyzed separately, because they (sometime adversely) affect each other. Therefore, approaches aimed at the tradeoff analysis of different attributes have been recently introduced (e.g., reliability versus cost, security versus performance). In this chapter we focus on modeling and analysis of QoS tradeoffs of a software architecture based on optimization models. A particular emphasis will be given to two aspects of this problem: (i) the mathematical foundations of QoS tradeoffs and their dependencies on the static and dynamic aspects of a software architecture, and (ii) the automation of architectural decisions driven by optimization models for QoS tradeoffs.

International audience; Model-Driven Development (MDD) is no longer a novel development paradigm. It has become mature from a research perspective and recent studies show its adoption in industry. Still, some issues remain a challenge. Among them, we are interested in the treatment of non-functional requirements (NFRs) in MDD processes. Very few MDD approaches have been reported to deal with NFRs (and they do it in a limited way). However, it is clear that NFRs need to be considered somehow in the final product of the MDD process. To better understand how NFRs are integrated into the existing MDD approaches, we have initiated the NFR4MDD project, a multinational empirical study, based on interviews with companies working on MDD projects. Our project aims at surveying the state of the practice for this topic. In this paper, we summarize our research protocol and present the current status of our study. The discussion will focus on the peculiarities of our study's context and organization involving about 20 researchers from 8 European countries.

Self-adaptive techniques have been introduced in the last few years to tackle the growing complexity of software systems, where a major complexity factor leans on their dynamic nature subject to sudden and unpredictable changes that can heavily impact on the software architecture quality. Non-functional models, as generated from architectural descriptions of software, have been proven as effective instruments to support designers meeting non-functional requirements since the early architectural phases. However, such models still lack of intrinsic support for adaptable software. Goal of this paper is to extend the modeling capabilities to the case of software adaptation aimed at satisfying performance requirements. In particular, we illustrate how control theory can solve the problem of keeping within pre-defined ranges the indices of a Queueing Network model (such as queue length) through software adaptation actions (such as replacing software services with less resource-demanding ones), while the model is subject to disturbances (such as workload and/or operational profile variations). For this goal we first introduce a library of Modelica components that represent Queueing Network (QN) elements with adaptable parameters (that can be used as knobs for adaptation actions). Then we use such components to build ``adaptable`` QN models that are subject to disturbances. Finally, in the same framework, we introduce controllers that drive the QN adaptation. We demonstrate the soundness of our approach on a simple representative example in two ways: (i) on one end, we provide a formal proof of controller performance guarantees, and (ii) on the other end we show the sensitivity over time of software adaptation actions to different (types and intensities of) disturbances.

Managing a software architecture after the deployment phase is a very complex task due to frequent changes in the software requirements and environment. The software architecture must evolve in order to tackle such changes. The goal of this paper is to provide support for the decisions that software architects make after deployment. This approach is based on an optimization model whose solution suggests the "best" actions to be taken according to a given change scenario (i.e., a set of new requirements that induce changes in the structural and behavioral aspects of the software architecture). The model aims to minimize the costs while keeping the reliability and the performance of the software architecture within certain thresholds. The approach has been implemented as a framework named SHEPhERd, which is composed of a UML case tool, a model builder and a model solver. We show how SHEPhERd works on a smartphone mobile application example, and we highlight its potential to drive architectural decisions through sensitivity analysis. The achieved results are compared with those obtained by two groups of (human) maintainers composed of experts and non-experts with respect to the system and the execution environment, and we show that SHEPhERd outperforms the human judgment-based approaches. We introduce an optimization-based approach for choosing evolution actions.It provides support for the decisions that architects make after deployment.It suggests the "best" actions to be taken according to a set of new requirements.It minimizes the evolution cost under reliability and performance constraints.

The detection of causes of performance problems in software systems and the identification of refactoring actions that can remove the problems are complex activities (even in small/medium scale systems). It has been demonstrated that software models can nicely support these activities, especially because they enable the introduction of automation in the detection and refactoring steps. In our recent work we have focused on performance antipattern-based detection and refactoring of software models. However performance antipatterns suffer from the numerous thresholds that occur in their representations and whose binding has to be performed before the detection starts (as for many pattern/antipattern categories).

Performance is a nonfunctional software attribute that plays a crucial role in wide application domains spreading from safety-critical systems to e-commerce applications. Software risk can be quantified as a combination of the probability that a software system may fail and the severity of the damages caused by the failure. In this paper, we devise a methodology for estimation of performance-based risk factor, which originates from violations, of performance requirements, (namely, performance failures). The methodology elaborates annotated UML diagrams to estimate the performance failure probability and combines it with the failure severity estimate which is obtained using the functional failure analysis. We are thus able to determine risky scenarios as well as risky software components, and the analysis feedback can be used to improve the software design. We illustrate the methodology on an e-commerce case study using step-by step approach, and then provide a brief description of a case study based on large real system.

The separation of functional and non-functional analysis of software systems often prevents from considering design solutions that would be evident if an integrated analysis would be possible. Lack of analysis integration is in fact leading software developers to loose precious feedback that would improve the quality of the software product or, even better, would avoid late software inconsistencies with respect to functional or non-functional requirements. In this paper we introduce a framework for software analysis integration. The framework core is XML-based and consists of software models and formal relations among models. The relations help to automatically propagate the analysis feedback among software models. We specify how different analysis methodologies can be integrated in our framework. We also show how the Eclipse platform represents a natural implementation environment for such a framework.

Modern programming languages and hardware technologies require (and effectively enable) ever more software to be distributed. Different paradigms (client–server, mobility-based, etc.) can be adopted to design distributed software, and deciding the “best” paradigm is a typical choice to be made in the very early software design phases. Several factors should drive this choice, that can also change depending on the software application domain. Within this framework, we focus on a class of attributes related to the performance, and the contribution of this paper is twofold: we extend an existing architecture description language (ADL) to model the physical and logical mobility of components; we introduce a methodology that, starting from a software architecture described using this extended notation, generates a performance model (namely a Markov decision process (MDP)) that allows the designer to evaluate the convenience of introducing logical mobility into a software application. The insights gained by applying our methodology do not consist only in deciding whether a mobile paradigm is effective for the performance improvement of a given application, but also how to use the paradigm, that is where a component should move, based on a certain type of performance indices.

This project has focused on the problem of filling the gap between non-functional analysis of software models and software development artifacts. Its main motivation stems from the fact that, still today, non-functional analysis and software development activities are quite loosely interconnected, thus the additional value that they can lead to each other is often lost. Instead it is commonly recognized that a tighter interconnection of practices, instruments and notations would give rise to more accurate analyses and more effective feedback from analysis results back to software artifacts. The confidence that advanced model-driven techniques can be suitable within this context came from previously successful collaborations among members of our research group that includes researchers from the Model-Driven Engineering and from the Non-functional Analysis areas. The seed of this project's activities was our first experiments on bidirectional transformations between software artifacts and performance models. Our intent was to understand whether this type of transformations could close the loop in both directions that are: transforming software artifacts into analyzable performance models (forward path), interpreting performance analysis results in order to remove problems from performance models and propagate the changes back to software artifacts (backward path). Starting from that point, the project has addressed three main research questions that, along the project development, have emerged as key points in the backward path context, which is the most problematic one. They can be summarized as follows: (1) Is it more advantageous working on the performance side or on the software side to solve problems identified with performance analysis? Can synergy be found among techniques adopted on these sides? (2) How can advanced model-driven techniques help in the model refactoring that is required to remove performance problems detected from the analysis?

Configurable software systems allow stakeholders to derive variants by selecting software and/or hardware features. Performance analysis of feature-based systems has been of large interest in the last few years, however a major research challenge is still to conduct such analysis before achieving full knowledge of the system, namely under a certain degree of uncertainty. In this paper we present an approach to analyze the correlation between selection of features embedding uncertain parameters and system performance. In particular, we provide best and worst case performance bounds on the basis of selected features and, in cases of wide gaps among these bounds, we carry on a sensitivity analysis process aimed at taming the uncertainty of parameters. The application of our approach to a case study in the e-health domain demonstrates how to support stakeholders in the identification of system variants that meet performance requirements.

Application domains in which early performance evaluation is needed are becoming more complex. In addition to traditional measures of complexity due, for example, to the number of components, their interactions, complicated control coordination and schemes, emerging applications may require adaptive response and reconfiguration the impact of externally observable (security) parameters. In this paper we introduce an approach for effective modeling and analysis of performance and security tradeoffs. The approach identifies a suitable allocation of resources that meet performance requirements, while maximizing measurable security effects. We demonstrate this approach through the analysis of performance sensitivity of a Border Inspection Management System (BIMS) with changing security mechanisms (e.g. biometric system parameters for passenger identification). The final result is a model-based approach that allows us to take decisions about BIMS performance and security mechanisms on the basis of rates of traveler arrivals and traveler identification security guarantees. We describe the experience gained when applying this approach to daily flight arrival schedule of a real airport.

Lifecycle validation of software performance (or prediction of the product ability to satisfy the user performancerequirements) is based on the automatic derivation of software performance models from CASE documents or rapid prototypes. This paper deals with the CASE document alternative. After a brief overview of existing automatic derivation methods, it introduces a method that unifies existing techniques that use CASE documents. The method is step-wise clear, can be used from the early phases of the software lifecycle, is distributed-software oriented, and can be easily incorporated into modern (e.g., UML-based) CASE tools. The method enables the software designer with no specific knowledge of performance theory to predict at design time the performance of various final product alternatives. The designer does only need to feed the CASE documents into the performance model generator. The paper carries on an application case study that deals with the development of distributed software, where the method is used to predict the performance of different distributed architectures the designer could select at preliminary design time to obtain the best performing final product. The method can be easily incorporated into modern object-oriented software development environments to encourage software designers to introduce lifecycle performance validation into their development best practices. © 2001 Elsevier Science B.V. All rights reserved.

The analysis of non-functional properties of Service-Based Systems (SBSs) is a complex task, mostly because it requires models that encompass the composition of service properties into architectural properties. For example, the reliability of a SBS is given by the composition of service and interconnection reliabilities. Although several approaches have been introduced in the last few years to address these issues, the tradeoff analysis among non-functional properties of software services has not yet been studied enough. The goal of this paper is to introduce a set of optimization models that allow quantifying the costs of service failure repair/mitigation actions aimed at keeping the whole SBS reliability over a certain threshold. On the basis of our previous work in this area, we first introduce an optimization model aimed at selecting either in-house built or provided services with the goal of minimizing the SBS cost while guaranteeing a certain level of reliability. Thereafter we strengthen the reliability constraints, and we build two different optimization models that aim to solve the same problem under new constraints, where one model starts from the solution obtained in the original model and tries to improve it, while the other one looks for an optimal solution in the whole search space. Finally, we introduce a fourth model, based on stochastic optimization, with the goal of rather searching for solutions that explicitly take into account the stochastic nature of the problem and search for new repair/mitigation actions cheaper than the ones identified by the other models. Each optimization model has been experimented on about 300 variations of a nominal model. The experimental results show the efficacy of our optimization models to quantify the costs of different failure repairing/mitigation actions in different contexts.

Several approaches have been introduced in the last few years to tackle the problem of interpreting model-based performance analysis results and translating them into architectural feedback. Typically the interpretation can take place by browsing either the software model or the performance model. In this paper, we compare two approaches that we have recently introduced for this goal: one based on the detection and solution of performance antipatterns, and another one based on bidirectional model transformations between software and performance models. We apply both approaches to the same example in order to illustrate the differences in the obtained performance results. Thereafter, we raise the level of abstraction and we discuss the pros and cons of working on the software side and on the performance side., In Proceedings FESCA 2013, arXiv:1302.4780

The problem of capturing performance problems is critical in the software design, mostly because the results of performance analysis (i.e. mean values, variances, and probability distributions) are difficult to be interpreted for providing feedback to software designers. Support to the interpretation of performance analysis results that helps to fill the gap between numbers and design alternatives is still lacking. The aim of this talk is to present the work that has been done in the last few years on filling such gap. The work is centered on software performance antipatterns, that are recurring solutions to common mistakes (i.e. bad practices) affecting performance. Such antipatterns can play a key role in the software performance domain, since they can be used in the investigation of performance problems as well as in the formulation of solutions in terms of design alternatives.

While the performance analysis of a software architecture is a quite well-assessed task nowadays, the issue of interpreting the performance results for providing feedback to software architects is still very critical. This is mostly due to the gap between results representation (i.e. mean values, variances, and/or probability distributions) and expected feedback (i.e. architectural alternatives). Performance antipatterns represent effective instruments to tackle this issue, because they document common mistakes leading to performance problems as well as their solutions. In this paper we present a model-driven approach that enables performance antipatterns to arise in the context of an ADL-based software architecture. Such approach automatically detects them in AEmilia, i.e. an ADL allowing the performance evaluation of software systems. The approach has been applied to a case study, and experimental results demonstrate its effectiveness.

Identifying and removing the causes of poor performance in software systems are complex problems due to a variety of factors to take into account. Nowadays these problems are usually tackled after the software deployment only with human-based means, which frequently boil down to developer skills and previous experiences. Performance antipatterns can be used to cope with these problems since they capture typical design patterns that are known leading to performance problems, as well as refactoring actions that can be taken to remove them.The goal of this paper is to introduce an approach that allows the refactoring of architectural models, based on antipatterns, that aims at providing performance improvement. To this end, we use a Role-Based Modeling Language to represent: (i) antipattern problems as Source Role Models (SRMs), and (ii) antipattern solutions as Target Role Models (TRMs). Hence, SRM-TRM pairs represent new instruments in the hands of developers to achieve architectural model refactorings aimed at removing sources of performance problems. Model refactoring for antipattern removal can be in fact obtained by replacing an SRM with the corresponding TRM. This approach has been applied to a case study in the e-commerce domain, whose experimental results demonstrate its effectiveness.

The problem of capturing performance problems is critical in the software design, mostly because the results of performance analysis (i.e. mean values, variances, and probability distributions) are difficult to be interpreted for providing feedback to software designers. Support to the interpretation of performance analysis results that helps to fill the gap between numbers and design alternatives is still lacking. The aim of this chapter is to present the work that has been done in the last few years on filling such gap. The work is centered on software performance antipatterns, that are recurring solutions to common mistakes (i.e. bad practices) affecting performance. Such antipatterns can play a key role in the software performance domain, since they can be used in the investigation of performance problems as well as in the formulation of solutions in terms of design alternatives.

International audience; The abundance of software that will be available in the next future will promote the production of appropriate integration means (architectures, connectors, integration patterns). The produced software will need to be able to evolve, react and adapt quickly to a continuously changing environment, while guaranteeing dependability through (on-the-fly) validation. The strongest adversary to this view is the lack of knowledge on the software, notably on its structure, behaviour, and execution context. Despite the possibility to extract observational models from existing software, a producer will always operate with software artefacts that exhibit a degree of uncertainty in terms of their functional and non functional characteristics. Uncertainty can only be controlled by making it explicit and by using it to drive the production process itself. This calls for a production process that explores available software and assesses its degree of uncertainty in relation to the opportunistic goal G, assists the producer in creating the appropriate integration means towards G, and validates the quality of the integrated system with respect to the goal G and the current context. In this paper we discuss how goal-oriented software systems can be opportunistically created by integrating under uncertainty existing pieces of software.

In this chapter, we describe the most important network protocols supporting modern applications in mobile cellular networks, wireless sensor networks (WSN) and mobile ad hoc networks (MANETs). We first focus on the handover procedure in mobile cellular networks and the network failures due this procedure. The current solutions to enable seamless handover in the existing networks technologies are presented. We then present, in the context of WSN, a framework for model-based design and performance analysis of WSN software applications. WSN are often used for applications with strict non-functional requirements. They, thus, require solid modelling approaches for non-functional attributes of WSN software applications. We finally discuss approaches and cross-layer protocols addressing the problems of security, wireless communications, and network topology changes in MANETs before presenting a framework which aims to study both the benefits of increased awareness between software layers on the same node (inter-layer awareness) and between the same layer over different nodes (intra-layer awareness), and the combination of new/existing protocols designed with a cross-layer criterion.

Border management applications are subject to intrinsic tradeoffs between the level of immigration / security risk that the border controls can tolerate and the performance (expressed by waiting time, throughput, etc.) that users are willing to accept for the personal identification process. The design of these systems should, therefore, be guided of such tradeoffs from the very early phases of the development lifecycle. In a previous paper we have studied this tradeoff through a model-driven approach, by combining performance models and security risk models. In this paper we briefly summarize the approach, and provide a perspective view on the open issues that should be addressed in the future.

The generation of performance models from architectural models has been tackled with well-founded approaches in the last decade, whereas there is a clear lack of automation in the backward path that brings the analysis results back to the software architecture. It is common to iteratively modify a (generated) performance model until performance indices meet the requirements. However, propagating the performance model modifications back to the original architectural model is a complex problem. In this paper we make a first step in this direction, in that we use the JTL language for specifying a bidirectional model transformation between UML models and Queueing Networks, so working towards an automated round-trip process between software architectural models and performance models.

In this chapter some advanced issues of software performance have been collected. They address different aspects of this discipline, not necessarily related to each other. The chapter is not meant to be a structured overview of the main open issues in the field, rather it is an anthology of issues that we have faced in the last few years.

This chapter is aimed at illustrating performance modeling and analysis issues within the software lifecycle. After having introduced software and performance modeling notations, here the goal is to illustrate their role within the software development process. In Chap. 5 we will describe in more details several approaches that, based on model transformations, can be used to implement the integration of software performance analysis and software development process. After briefly introducing the most common software lifecycle stages, we present our unifying view of software performance analysis as integrated within a software development process (i.e. the Q-Model).

In the next future we will be surrounded by a virtually infinite number of software applications that provide computational software resources in the open Globe. This will radically change the way software will be produced and used. Users will be keen on producing their own piece of software, by also reusing existing software, to better satisfy their needs, therefore with a goal oriented, opportunistic use in mind. The produced software will need to be able to evolve, react and adapt to a continuously changing environment, while guaranteeing dependability. The strongest adversary to this view is the lack of knowledge on the software's structure, behavior, and execution context. Despite the possibility to extract observational models from existing software, a producer will always operate with software artifacts that exhibit a degree of uncertainty in terms of their functional and non functional characteristics. We believe that uncertainty can only be controlled by making it explicit and by using it to drive the production process itself. In this paper, we introduce a novel paradigm of software production process that explores available software and assesses its degree of uncertainty in relation to the opportunistic goal G, assists the producer in creating the appropriate integration means towards G, and validates the quality of the integrated system with respect to G and the current context.

This chapter focuses on the transformational approaches from software system specifications to performance models. These transformations aim at filling the gap between the software development process and the performance analysis by generating performance model ready to be validated from the software models. Three approaches are discussed in detail presenting their foundations and their application to an e-commerce case study. Moreover, the chapter briefly reviews representatives of other transformational approaches present in the literature. All the presented approaches are discussed with respect to a set of relevant dimensions such as software specification, performance model, evaluation methods and level of automated support for performance prediction.