Will Tracz, our esteemed editor and Used-Program salesman, has written an entertaining, non-technical book dealing with the practice (and lack of) of software reuse. Its a collection of essays, mostly rehashed (reused?) and updated from various columns and papers published over the years.. Its a short (a bit over 200 pages) easy reading and enjoyable book (I read most of it in one sitting). Some of the essays discuss what was printed in the past and a discussion of the current status of the points.

Safeware: System Safety and Computers

Software architectures have high costs for change, are complex, and erode during evolution We believe these problems are partially due to knowledge vaporization Currently, almost all the knowledge and information about the design decisions the architecture is based on are implicitly embedded in the architecture, but lack a first-class representation Consequently, knowledge about these design decisions disappears into the architecture, which leads to the aforementioned problems In this paper, a new perspective on software architecture is presented, which views software architecture as a composition of a set of explicit design decisions This perspective makes architectural design decisions an explicit part of a software architecture Consequently, knowledge vaporization is reduced, thereby alleviating some of the fundamental problems of software architecture

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Software Architecture as a Set of Architectural Design Decisions

http://courses.ece.ubc.ca/417/public/Hofmeister.pdf

A general model of software architecture design derived from five industrial approaches

Models can help software engineers to reason about design-time decisions before implementing a system. This paper focuses on models that deal with non-functional properties, such as reliability and performance. To build such models, one must rely on numerical estimates of various parameters provided by domain experts or extracted by other similar systems. Unfortunately, estimates are seldom correct. In addition, in dynamic environments, the value of parameters may change over time. We discuss an approach that addresses these issues by keeping models alive at run time and feeding a Bayesian estimator with data collected from the running system, which produces updated parameters. The updated model provides an increasingly better representation of the system. By analyzing the updated model at run time, it is possible to detect or predict if a desired property is, or will be, violated by the running implementation. Requirement violations may trigger automatic reconfigurations or recovery actions aimed at guaranteeing the desired goals. We illustrate a working framework supporting our methodology and apply it to an example in which a Web service orchestrated composition is modeled through a Discrete Time Markov Chain. Numerical simulations show the effectiveness of the approach.

Model evolution by run-time parameter adaptation

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

The ability to predict the reliability of a software system early in its development, e.g., during architectural design, can help to improve the system's quality in a cost-effective manner. Existing architecture-level reliability prediction approaches focus on system-level reliability and assume that the reliabilities of individual components are known. In general, this assumption is unreasonable, making component reliability prediction an important missing ingredient in the current literature. Early prediction of component reliability is a challenging problem because of many uncertainties associated with components under development. In this paper we address these challenges in developing a software component reliability prediction framework. We do this by exploiting architectural models and associated analysis techniques, stochastic modeling approaches, and information sources available early in the development lifecycle. We extensively evaluate our framework to illustrate its utility as an early reliability prediction approach.

Early prediction of software component reliability

As with any other artifact produced as part of the software life cycle, software architectures evolve and this evolution must be managed. One approach to doing so would be to apply any of a host of existing configuration management systems, which have long been used successfully at the level of source code. Unfortunately, such an approach leads to many problems that prevent effective management of architectural evolution. To overcome these problems, we have developed an alternative approach centered on the use of an integrated architectural and configuration management system model. Because the system model combines architectural and configuration management concepts in a single representation, it has the distinct benefit that all architectural changes can be precisely captured and clearly related to each other---both at the fine-grained level of individual architectural elements and at the coarse-grained level of architectural configurations. To support the use of the system model, we have developed Mae, an architectural evolution environment through which users can specify architectures in a traditional manner, manage the evolution of the architectures using a check-out/check-in mechanism that tracks all changes, select a specific architectural configuration, and analyze the consistency of a selected configuration. We demonstrate the benefits of our approach by showing how the system model and its accompanying environment were used in the context of several representative projects.

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Mae---a system model and environment for managing architectural evolution

In the world of software development everything evolves. So, then, do software architectures. Unlike source code, for which the use of a configuration management (CM) system is the predominant approach to capturing and managing evolution, approaches to capturing and managing architectural evolution span a wide range of disconnected alternatives. This paper contributes a novel architecture evolution environment, called Mae, which brings together a number of these alternatives. The environment facilitates an incremental design process in which all changes to all architectural elements are integrally captured and related. Key to the environment is a rich system model that combines architectural concepts with those from the field of CM. Not only does this system model form the basis for Mae, but in precisely capturing architectural evolution it also facilitates automated support for several innovative capabilities that rely on the integrated nature of the system model. This paper introduces three of those: the provision of design guidance at the architectural level, the use of specialized software connectors to ensure run-time reliability during component upgrades, and the creation of component-level patches to be applied to deployed system configurations.

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Taming architectural evolution

Modern society relies heavily on complex software systems for everyday activities. Dependability of these systems thus has become a critical feature that determines which products are going to be successfully and widely adopted. In this paper, we present an approach to modeling reliability of software systems at the architectural level. Dynamic Bayesian Networks are used to build a stochastic reliability model that relies on standard models of software architecture, and does not require implementation-level artifacts. Reliability values obtained via this approach can aid the architect in evaluating design alternatives. The approach is evaluated using sensitivity and uncertainty analysis.

A Bayesian model for predicting reliability of software systems at the architectural level

Over the past decade, a number of architecture description languages (ADLs) have been proposed to facilitate modeling and analysis of software architecture. While each claims to have various benefits, to date, there have been few studies to assess the relative merits of these approaches. In this paper, we describe our experience using two ADLs to model a system initially described in UML, and compare their effectiveness in identifying system design flaws. We also describe the techniques we used for extracting architectural models from a UML system description.

/pdf/understanding-tradeoffs-among-different-architectural-3y33s2ts0d.pdf

Roshanak Roshandel

Papers

Early prediction of software component reliability

Mae---a system model and environment for managing architectural evolution

Taming architectural evolution

A Bayesian model for predicting reliability of software systems at the architectural level

Understanding tradeoffs among different architectural modeling approaches