Classifying architectural constraints as a basis for software quality assessment

Architectural styles and patterns have been studied since the inception of software architecture as a discipline. We generalise architectural styles, patterns and similar concepts by introducing the notion of architectural constraints. An architectural constraint is a vehicle for the reuse of architectural design knowledge and for the improvement of software quality. It may be used for improving architectural analyses of quality characteristics of the software system to be realised. We present the method for surveying the literature on architectural constraint concepts, and provide a taxonomy covering various definitions of architectural styles and patterns.     

Semi-automated architectural abstraction specifications for supporting software evolution

In this paper we present an approach for supporting the semi-automated architectural abstraction of architectural models throughout the software life-cycle. It addresses the problem that the design and implementation of a software system often drift apart as software systems evolve, leading to architectural knowledge evaporation. Our approach provides concepts and tool support for the semi-automatic abstraction of architecture component and connector views from implemented systems and keeping the abstracted architecture models up-to-date during software evolution. In particular, we propose architecture abstraction concepts that are supported through a domain-specific language (DSL). Our main focus is on providing architectural abstraction specifications in the DSL that only need to be changed, if the architecture changes, but can tolerate non-architectural changes in the underlying source code. Once the software architect has defined an architectural abstraction in the DSL, we can automatically generate architectural component views from the source code using model-driven development (MDD) techniques and check whether architectural design constraints are fulfilled by these models. Our approach supports the automatic generation of traceability links between source code elements and architectural abstractions using MDD techniques to enable software architects to easily link between components and the source code elements that realize them. It enables software architects to compare different versions of the generated architectural component view with each other. We evaluate our research results by studying the evolution of architectural abstractions in different consecutive versions of five open source systems and by analyzing the performance of our approach in these cases.     

Advanced quality prediction model for software architectural knowledge sharing

In the field of software architecture, a paradigm shift is occurring from describing the outcome of architecting process to describing the Architectural Knowledge (AK) created and used during architecting. Many AK models have been defined to represent domain concepts and their relationships, and they can be used for sharing and reusing AK across organizations, especially in geographically distributed contexts. However, different AK domain models can represent concepts that are different, thereby making effective AK sharing challenging. In order to understand the mapping quality from one AK model to another when more than one AK model coexists, AK sharing quality prediction based on the concept differences across AK models is necessary. Previous works in this area lack validation in the actual practice of AK sharing. In this paper, we carry out validation using four AK sharing case studies. We also improve the previous prediction models. We developed a new advanced mapping quality prediction model, this model (i) improves the prediction accuracy of the recall rate of AK sharing quality; (ii) provides a better balance between prediction effort and accuracy for AK sharing quality.     

Using software evolution to focus architectural recovery

Ideally, a software project commences with requirements gathering and specification, reaches its major milestone with system implementation and delivery, and then continues, possibly indefinitely, into an operation and maintenance phase. The software system's architecture is in many ways the linchpin of this process: it is supposed to be an effective reification of the system's technical requirements and to be faithfully reflected in the system's implementation. Furthermore, the architecture is meant to guide system evolution, while also being updated in the process. However, in reality developers frequently deviate from the architecture, causing architectural erosion, a phenomenon in which the initial, “as documented' architecture of an application is (arbitrarily) modified to the point where its key properties no longer hold. Architectural recovery is a process frequently used to cope with architectural erosion whereby the current, “as implemented” architecture of a software system is extracted from the system's implementation. In this paper we propose a light-weight approach to architectural recovery, called Focus, which has three unique facets. First, Focus uses a system's evolution requirements to isolate and incrementally recover only the fragment of the system's architecture affected by the evolution. In this manner, Focus allows engineers to direct their primary attention to the part of the system that is immediately impacted by the desired change; subsequent changes will incrementally uncover additional parts of the system's architecture. Secondly, in addition to software components, which are the usual target of existing recovery approaches, Focus also recovers the key architectural notions of software connector and architectural style. Finally, Focus does not only recover a system's architecture, but may in fact rearchitect the system. We have applied and evaluated Focus in the context of several off-the-shelf applications and architectural styles to date. We discuss its key strengths and point out several open issues that will frame our future work.     

SHADD: A scenario-based approach to software architectural defects detection

Although a good architecture is not sufficient to guarantee the success of a software product, undoubtedly it is essential to support the product quality. Evaluating software architecture provides early insight into product capabilities and limitations. The earlier in the life cycle the problems are detected, the cheaper it is to fix them. In this paper, at first, a review on well-known scenario-based methods to evaluate software architectures is presented, and their advantages and limitations are discussed. Then, a method named SHADD with different characteristics is introduced to detect architectural defects. Using a scenario-based approach, the proposed method finds out the critical aspects and potential problems threatening the system from the stakeholder’s point of view. Scenarios are then used as a basis for the process of architectural defects detection. SHADD and its elements are specified in a systematic form and an illustration of its application on the architecture of a real system is also presented. The results show that SHADD can be used to detect those architectural defects which may be uncovered during the application of conventional evaluation methods.     

An empirical investigation of architectural prototyping

Architectural prototyping is the process of using executable code to investigate stakeholders’ software architecture concerns with respect to a system under development. Previous work has established this as a useful and cost-effective way of exploration and learning of the design space of a system and in addressing issues regarding quality attributes, architectural risks, and the problem of knowledge transfer and conformance. However, the actual industrial use of architectural prototyping has not been thoroughly researched so far. In this article, we report from three studies of architectural prototyping in practice. First, we report findings from an ethnographic study of practicing software architects. Secondly, we report from a focus group on architectural prototyping involving architects from four companies. And, thirdly, we report from a survey study of 20 practicing software architects and software developers. Our findings indicate that architectural prototyping plays an important and frequent role in resolving problems experimentally, but less so in exploring alternative solutions. Furthermore, architectural prototypes include end-user or business related functionality rather than purely architectural functionality. Based on these observations we provide recommendations for effective industrial architectural prototyping.     

Harmonizing architectural decisions with component view models using reusable architectural knowledge transformations and constraints

Architectural design decisions (ADDs) have been used in recent years for capturing design rationale and documenting architectural knowledge (AK). However, various architectural design views still provide the most common means for describing and communicating architectural design. The evolution of software systems requires that both ADDs and architectural design views are documented and maintained, which is a tedious and time-consuming task in the long run. Also, in lack of a systematic and automated support for bridging between ADDs and architectural design views, decisions and designs tend to become inconsistent over time. In our proposal, we introduce a reusable AK transformation language for supporting the automated transformation of reusable AK knowledge to component-and-connector models, the architectural design view used most commonly today. In addition, reusable consistency checking rules verify the consistency between decisions and designs. We evaluate our approach in an industrial case study and show that it offers high reusability, provides automation, and can, in principle, deal with large numbers of recurring decisions.     

An analytical approach to architecture-based software performance and reliability prediction

Conventional approaches to analyze the behavior of software applications are black box based, that is, the software application is treated as a whole and only its interactions with the outside world are modeled. The black box approaches ignore information about the internal structure of the application and the behavior of the individual parts. Hence, they are inadequate to model the behavior of a realistic software application, which is likely to be made up of several interacting parts. Architecture-based analysis, which seeks to assess the behavior of a software application taking into consideration the behavior of its parts and the interactions among the parts is thus essential. Most of the research in the area of architecture-based analysis has been devoted to developing analytical models, with very little, if any effort being devoted to how these models might be applied to real software applications. In order to apply these models to software applications, methods must be developed to extract the parameters of the analytical models from information collected during the execution of the application. In this paper, we present an experimental approach to extract the parameters of architecture-based models from code coverage measurements obtained during the execution of the application. To facilitate this, we use a coverage analysis tool called automatic test analyzer in C (ATAC), which is a part of Telcordia Software Visualization and Analysis Toolsuite (TSVAT) developed at Telcordia Technologies. We demonstrate the approach by predicting the performance and reliability of an application called Symbolic Hierarchical Automated Reliability Predictor (SHARPE), which has been widely used to solve stochastic models of reliability, performance and performability.     

Automatic inclusion of middleware performance attributes into architectural UML software models

Distributed systems often use a form of communication middleware to cope with different forms of heterogeneity, including geographical spreading of the components, different programming languages and platform architectures, etc. The middleware, of course, impact the architecture and the performance of the system. This paper presents a model transformation framework to automatically include the architectural impact and the overhead incurred by using a middleware layer between several system components. Using this framework, architects can model the system in a middleware-independent fashion. Accurate, middleware-aware models can then be obtained automatically using a middleware model repository. The actual transformation algorithm is presented in more detail. The resulting models can be used to obtain performance models of the system. From those performance models, early indications of the system performance can be extracted.     

Segmented software cost estimation models based on fuzzy clustering

Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees.This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart.     

ModelSet: A labelled dataset of software models for machine learning

Curated collections of models are essential for the success of Machine Learning (ML) and Data Analytics in Model-Driven Engineering (MDE). However, current datasets are either too small or not properly curated. In this paper, we present ModelSet, a dataset composed of 5,466 Ecore models and 5,120 UML models which have been manually labelled to support ML tasks. We describe the structure of the dataset and explain how to use the associated library to develop ML applications in Python. Finally, we present some applications which can be addressed using ModelSet.Tool Website: https://github.com/modelset     

Hierarchical message bus-based software architectural style

As the size and complexity of software systems increase, the design and specification of overall system structure become more significant issues than the choice of algorithms and data structures of computation. An appropriate architecture for a system is a key element of its success. Based on the practice of Jadebird software production line, this paper proposes a software architectural style based on hierarchical message buses, named JB/HMB. In this style, the component model consists of external interfaces, static structure and dynamic behavior, which depicts a component from different aspects. Supported by message buses, components interact with one another by messages, which can be used to describe distributed and concurrent systems well. JB/HMB style supports stepwise decomposition and refinement, and runtime system evolution. Finally, characteristics of JB/HMB style are summarized as a conclusion, and future research directions are specified.     

Lumber yield optimization software validation and performance review

Exhaustive search algorithms in simulation models are used by the secondary wood industry to find the optimal cutting pattern to cut lumber into dimension parts. Finding the optimum cut-up solution is of paramount importance to the industry for controlling product costs. For this purpose, the USDA Forest Service created the ROugh MIll RIP-first simulator (ROMI-RIP), a simulation model providing near optimum lumber cut-up solutions. However, ROMI-RIP was never truly validated nor was its performance directly proven. This study used data derived from a state-of-the-art rough mill to validate the program and to show its performance. Results show that ROMI-RIP outperformed the rough mill by more than 7% yield (71.1% versus 64.0%). Manufacturers of solid-wood products can realize significant yield gains by using ROMI-RIP. Additional benefits include lower production costs and significant savings in raw materials.     

On the use of software design models in software development practice: An empirical investigation

Research into software design models in general, and into the UML in particular, focuses on answering the question how design models are used, completely ignoring the question if they are used. There is an assumption in the literature that the UML is the de facto standard, and that use of design models has had a profound and substantial effect on how software is designed by virtue of models giving the ability to do model-checking, code generation, or automated test generation. However for this assumption to be true, there has to be significant use of design models in practice by developers.This paper presents the results of a survey summarizing the answers of 3785 developers answering the simple question on the extent to which design models are used before coding. We relate their use of models with (i) total years of programming experience, (ii) open or closed development, (iii) educational level, (iv) programming language used, and (v) development type.The answer to our question was that design models are not used very extensively in industry, and where they are used, the use is informal and without tool support, and the notation is often not UML. The use of models decreased with an increase in experience and increased with higher level of qualification. Overall we found that models are used primarily as a communication and collaboration mechanism where there is a need to solve problems and/or get a joint understanding of the overall design in a group. We also conclude that models are seldom updated after initially created and are usually drawn on a whiteboard or on paper.     

Comparison of artificial neural network and regression models for estimating software development effort

Estimating the amount of effort required for developing an information system is an important project management concern. In recent years, a number of studies have used neural networks in various stages of software development. This study compares the prediction performance of multilayer perceptron and radial basis function neural networks to that of regression analysis. The results of the study indicate that when a combined third generation and fourth generation languages data set were used, the neural network produced improved performance over conventional regression analysis in terms of mean absolute percentage error.     

A meta-model for formulating knowledge-based models of software development

In this paper, we introduce a knowledge-based meta-model which serves as a unified resource model for integrating characteristics of major types of objects appearing in software development models (SDMs). The URM consists of resource classes and a web of relations that link different types of resources found in different kinds of models of software development. The URM includes specialized models for software models for software systems, documents, agents, tools, and development processes. The URM has served as the basis for integrating and interoperating a number of process-centered CASE environments. The major benefit of the URM is twofold: First, it forms a higher level of abstraction supporting SDM formulation that subsumes many typical models of software development objects. Hence, it enables a higher level of reusability for existing support mechanisms of these models. Second, it provides a basis to support complex reasoning mechanisms that address issues across different types of software objects. To explore these features, we describe the URM both formally and with a detailed example, followed by a characterization of the process of SDM composition, and then by a characterization of the life cycle of activities involved in an overall model formulation process.     

Selecting software reliability growth models and improving their predictive accuracy using historical projects data

During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for release. SRGMs (software reliability growth models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the model's ability to accurately predict the expected defect inflow profile is useful. For assessing release readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain.Using defect inflow profiles from large software projects from Ericsson, Volvo Car Corporation and Saab, we evaluate commonly used SRGMs for their ability to provide empirical basis for making these decisions. We also demonstrate that using defect intensity growth rate from earlier projects increases the accuracy of the predictions. Our results show that Logistic and Gompertz models are the most accurate models; we further observe that classifying a given project based on its expected shape of defect inflow help to select the most appropriate model.