Extending Software Architecting Processes with Decision-Making Activities

Abstract. The traditional perspective on software architecture has paid much attention to the architecting process as a means to create the software architecture of the target system, as well as the documentation for describing this by means of several architectural views. Recently, the software architecture research community has faced the need to record, manage, and document the design decisions that lead to the architecture. Because architectures are the result of a set of design decisions, these must be managed properly as a complementary process to the architecting activity. Several types of processes can be defined and used as part of this decision-making activity. In this paper we focus on those activities that concern to the creation and use of architectural design decisions and how these can be managed with a tool support. 1

Modelling architectural decisions under changing requirements

Abstract-One of the approaches for documenting software architecture is to treat it as a set of architectural design decisions. Such decisions are always made in the context of requirements that must be fulfilled and in the context of decisions that were made before. Currently, models for representing architectural decisions are mainly concentrated on showing the decision making process of the initial architectural design. However, decisions that have been made in such a process may need to be changed during further evolution and maintenance of the software architecture, typically in response to the new or changed requirements. A graphical modelling notation for documenting architectural decisions (Maps of Architectural Decisions) has been developed by our team. In this paper, it is presented how this notation could be used to model architectural decisions under changing requirements. It is proposed how one decision change could be effectively propagated through the rest of the architectural decision model and how a rigorous and toolsupported process of updating such models could be organized

Extending Software Architecting Processes with Decision-making Activities 1

Abstract. The traditional perspective on software architecture has paid much attention to architecting as a development process aimed at creating the architecture of a software system, as well as the documentation used to communicate the architecture to the stakeholders by means of several architectural views. Recently, the software architecture research community has faced the need to record, manage, and document the design decisions and the rationale that lead to such architecture. Because architectures are the result of a set of design decisions, this design rationale must be properly recorded and managed as a complementary process to the modelling activity. In this paper we detail different types of decision-making activities aimed at creating and using design decisions and how these can be supported with tool support

Towards Mobile Twin Peaks for App Development

Requirements of mobile apps are often hard to elicit from massive numbers of users, although it is important for the solution architecture to meet them. Mobile Twin Peaks approach is proposed as a process of developing apps concurrently and iteratively that incorporates bidirectional communications within a mobile app. The communications allow both requirements engineers and software architects to reach a consensus on functionalities and quality constraints and to adapt architectural design decisions appropriately. To recommend architectural design decisions to the developers, we aim to obtain architecture- critical requirements from a set of general apps by combining, for example, analytics, ethnographic study, and information retrieval. We argue that the effectiveness of these techniques could be evaluated by experimental case studies and by engaging with industry partners to perform action research

Capturing Software Architecture Knowledge for Pattern-Driven Design

Context: Software architecture is a knowledge-intensive field. One mechanism for storing architecture knowledge is the recognition and description of architectural patterns. Selecting architectural patterns is a challenging task for software architects, as knowledge about these patterns is scattered among a wide range of literature. Method: We report on a systematic literature review, with the aim of building a decision model for the architectural pattern selection problem. Moreover, twelve experienced practitioners at software-producing organizations evaluated the usability and usefulness of the extracted knowledge.\newline Results: An overview is provided of 29 patterns and their effects on 40 quality attributes. Furthermore, we report in which systems the 29 patterns are applied and in which combinations. The practitioners confirmed that architectural knowledge supports software architects with their decision-making process to select a set of patterns for a new problem. We investigate the potential trends among architects to select patterns. Conclusion: With the knowledge available, architects can more rapidly select and eliminate combinations of patterns to design solutions. Having this knowledge readily available supports software architects in making more efficient and effective design decisions that meet their quality concerns

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

Feature-based rationale management system for supporting software architecture adaptation

Each software architecture design is the result of a broad set of design decisions and their justifications, that is, the design rationale. Capturing the design rationale is important for a variety of reasons such as enhancing communication, reuse and maintenance. Unfortunately, it appears that there is still a lack of appropriate methods and tools for effectively capturing and managing the architecture design rationale. In this paper we present a feature-based rationale management approach and the corresponding tool environment ArchiRationale for supporting software architecture adaptation. The approach takes as input an existing architecture and captures the design rationale for adapting the architecture for a given quality concern. For this we define a feature model that includes the possible set of architectural tactics to realize the quality concern. The presented approach captures the rationale for deciding on feature selections and for selecting the corresponding architecture design alternatives. ArchiRationale customizes and integrates the Eclipse plugin tools XFeature, ArchStudio and XQuery to provide tool support for capturing, storing and accessing the design rationale. We illustrate the approach for adapting a software architecture for fault tolerance. © 2012 World Scientific Publishing Company

Architecting fault-tolerant software systems

The increasing size and complexity of software systems makes it hard to prevent or remove all possible faults. Faults that remain in the system can eventually lead to a system failure. Fault tolerance techniques are introduced for enabling systems to recover and continue operation when they are subject to faults. Many fault tolerance techniques are available but incorporating them in a system is not always trivial. We consider the following problems in designing a fault-tolerant system. First, existing reliability analysis techniques generally do not prioritize potential failures from the end-user perspective and accordingly do not identify sensitivity points of a system. \ud Second, existing architecture styles are not well-suited for specifying, communicating and analyzing design decisions that are particularly related to the fault-tolerant aspects of a system. Third, there are no adequate analysis techniques that evaluate the impact of fault tolerance techniques on the functional decomposition of software architecture. Fourth, realizing a fault-tolerant design usually requires a substantial development and maintenance effort. \ud To tackle the first problem, we propose a scenario-based software architecture reliability analysis method, called SARAH that benefits from mature reliability engineering techniques (i.e. FMEA, FTA) to provide an early reliability analysis of the software architecture design. SARAH evaluates potential failures from the end-user perspective to identify sensitive points of a system without requiring an implementation. \ud As a new architectural style, we introduce Recovery Style for specifying fault-tolerant aspects of software architecture. Recovery Style is used for communicating and analyzing architectural design decisions and for supporting detailed design with respect to recovery. \ud As a solution for the third problem, we propose a systematic method for optimizing the decomposition of software architecture for local recovery, which is an effective fault tolerance technique to attain high system availability. To support the method, we have developed an integrated set of tools that employ optimization techniques, state-based analytical models (i.e. CTMCs) and dynamic analysis on the system. The method enables the following: i ) modeling the design space of the possible decomposition alternatives, ii ) reducing the design space with respect to domain and stakeholder constraints and iii ) making the desired trade-off between availability and performance metrics. \ud To reduce the development and maintenance effort, we propose a framework, FLORA that supports the decomposition and implementation of software architecture for local recovery. The framework provides reusable abstractions for defining recoverable units and for incorporating the necessary coordination and communication protocols for recovery

EVALUATION OF CAPTURING ARCHITECTURALLY SIGNIFICANT REQUIREMENTS METHODS

Every software development organization strives for customer satisfaction. It is universally accepted that the success of software development lies in the clear understanding of the client requirements. During requirement elicitation and analysis stage, the system analyst identifies the functional and non-functional requirements from the customer. Security, usability, reliability, performance, scalability and supportability are the significant quality attributes of a software system. These quality attributes are also referred as non-functional requirements. Only a few functional and quality attributes requirement help to identify and shape the software architecture. A software system's architecture is the set of prime design decisions made about the system. If the requirement influences the architectural design decision then, it is referred as Architecturally Significant Requirement (ASR). Identifying and specifying all the possible ASR are important tasks in the requirement elicitation and analysis stage.In this research, general problems that are faced while capturing and specifying ASR in requirement elicitation and analysis is studied. Among the different requirement elicitation techniques, use case diagram has been identified and enhanced to solve the problem of capturing and specifying ASR during the requirement elicitation and analysis phase Â&nbsp