A Basis for Analyzing Software Architecture Analysis Methods

A software architecture is a key asset for any organization that builds complex software-intensive systems. Because of an architecture's central role as a project blueprint, organizations should analyze the architecture before committing resources to it. An analysis helps to ensure that sound architectural decisions are made. Over the past decade a large number of architecture analysis methods have been created, and at least two surveys of these methods have been published. This paper examines the criteria for analyzing architecture analysis methods, and suggests a new set of criteria that focus on the essence of what it means to be an architecture analysis method. These criteria could be used to compare methods, to help understand the suitability of a method, or to improve a method. We then examine two methods—the Architecture Tradeoff Analysis Method and Architecture-level Modifiability Analysis—in light of these criteria, and provide some insight into how these methods can be improved. Rick Kazman is a Senior Member of the Technical Staff at the Software Engineering Institute of Carnegie Mellon University and Professor at the University of Hawaii. His primary research interests are software architecture, design and analysis tools, software visualization, and software engineering economics. He also has interests in human-computer interaction and information retrieval. Kazman has created several highly influential methods and tools for architecture analysis, including the SAAM and the ATAM. He is the author of over 80 papers, and co-author of several books, including “Software Architecture in Practice”, and “Evaluating Software Architectures: Methods and Case Studies”. Len Bass is a Senior Member of the Technical Staff at the Software Engineering Institute (SEI). He has written two award winning books in software architecture as well as several other books and numerous papers in a wide variety of areas of computer science and software engineering. He is currently working on techniques for the methodical design of software architectures and to understand how to support usability through software architecture. He has been involved in the development of numerous different production or research software systems ranging from operating systems to database management systems to automotive systems. Mark Klein is Senior Member of the Technical Staff of the Software Engineering Institute. He has over 20 years of experience in research on various facets of software engineering, dependable real-time systems and numerical methods. Klein's most recent work focuses on the analysis of software architectures, architecture tradeoff analysis, attribute-driven architectural design and scheduling theory. Klein's work in real-time systems involved the development of rate monotonic analysis (RMA), the extension of the theoretical basis for RMA, and its application to realistic systems. Klein's earliest work involved research in high-order finite element methods for solving fluid flow equations arising in oil reservoir simulation. He is the co-author two books: “A Practitioner's Handbook for Real-Time Analysis: Guide to Rate Monotonic Analysis for Real-Time Systems” and “Evaluating Software Architecture: Methods and Case Studies”. Anthony J. Lattanze is an Associate Teaching Professor at the Institute for Software Research International (ISRI) at Carnegie Mellon University (CMU) and a senior member of the technical staff at the Software Engineering Institute (SEI). Anthony teaches courses in CMUs Masters of Software Engineering Program in Software Architecture, Real-Time/Embedded Systems, and Software Development Studio. His primary research interest is in the area software architectural design for embedded, software intensive systems. Anthony consults and teaches throughout industry in the areas of software architecture design and architecture evaluation. Prior to Carnegie Mellon, Mr. Lattanze was the Chief of Software Engineering for the Technology Development Group at the United States Flight Test Center at Edwards Air Force Base, CA. During his tenure at the Flight Test Center, he was involved with a number of software and systems engineering projects as a software and systems architect, project manager, and developer. During this time as he was involved with the development, test, and evaluation of avionics systems for the B-2 Stealth Bomber, F-117 Stealth Fighter, and F-22 Advanced Tactical Fighter among other systems. Linda Northrop is the director of the Product Line Systems Program at the Software Engineering Institute (SEI) where she leads the SEI work in software architecture, software product lines and predictable component engineering. Under her leadership the SEI has developed software architecture and product line methods that are used worldwide, a series of five highly-acclaimed books, and Software Architecture and Software Product Line Curricula. She is co-author of the book, “Software Product Lines: Practices and Patterns,” and a primary author of the SEI Framework for Software Product Line Practice.     

Real-Time Reflexion Modelling in architecture reconciliation: A multi case study

ContextReflexion Modelling is considered one of the more successful approaches to architecture reconciliation. Empirical studies strongly suggest that professional developers involved in real-life industrial projects find the information provided by variants of this approach useful and insightful, but the degree to which it resolves architecture conformance issues is still unclear.ObjectiveThis paper aims to assess the level of architecture conformance achieved by professional architects using Reflexion Modelling, and to determine how the approach could be extended to improve its suitability for this task.MethodAn in vivo, multi-case-study protocol was adopted across five software systems, from four different financial services organizations. Think-aloud, video-tape and interview data from professional architects involved in Reflexion Modelling sessions were analysed qualitatively.ResultsThis study showed that (at least) four months after the Reflexion Modelling sessions less than 50% of the architectural violations identified were removed. The majority of participants who did remove violations favoured changes to the architectural model rather than to the code. Participants seemed to work off two specific architectural templates, and interactively explored their architectural model to focus in on the causes of violations, and to assess the ramifications of potential code changes. They expressed a desire for dependency analysis beyond static-source-code analysis and scalable visualizations.ConclusionThe findings support several interesting usage-in-practice traits, previously hinted at in the literature. These include (1) the iterative analysis of systems through Reflexion models, as a precursor to possible code change or as a focusing mechanism to identify the location of architecture conformance issues, (2) the extension of the approach with respect to dependency analysis of software systems and architectural modelling templates, (3) improved visualization support and (4) the insight that identification of architectural violations in itself does not lead to their removal in the majority of instances.     

Application of knowledge-based approaches in software architecture: A systematic mapping study

ContextKnowledge management technologies have been employed across software engineering activities for more than two decades. Knowledge-based approaches can be used to facilitate software architecting activities (e.g., architectural evaluation). However, there is no comprehensive understanding on how various knowledge-based approaches (e.g., knowledge reuse) are employed in software architecture.ObjectiveThis work aims to collect studies on the application of knowledge-based approaches in software architecture and make a classification and thematic analysis on these studies, in order to identify the gaps in the existing application of knowledge-based approaches to various architecting activities, and promising research directions.MethodA systematic mapping study is conducted for identifying and analyzing the application of knowledge-based approaches in software architecture, covering the papers from major databases, journals, conferences, and workshops, published between January 2000 and March 2011.ResultsFifty-five studies were selected and classified according to the architecting activities they contribute to and the knowledge-based approaches employed. Knowledge capture and representation (e.g., using an ontology to describe architectural elements and their relationships) is the most popular approach employed in architecting activities. Knowledge recovery (e.g., documenting past architectural design decisions) is an ignored approach that is seldom used in software architecture. Knowledge-based approaches are mostly used in architectural evaluation, while receive the least attention in architecture impact analysis and architectural implementation.ConclusionsThe study results show an increased interest in the application of knowledge-based approaches in software architecture in recent years. A number of knowledge-based approaches, including knowledge capture and representation, reuse, sharing, recovery, and reasoning, have been employed in a spectrum of architecting activities. Knowledge-based approaches have been applied to a wide range of application domains, among which “Embedded software” has received the most attention.     

Detecting deviations in the code using architecture view-based drift analysis

ContextOne of the key requirements for the code is conformance with the architecture. Architectural drift implies the diverging of the implemented code from the architecture design of the system. Manually checking the consistency between the implemented code and architecture can be intractable and cumbersome for large-scale systems.ObjectiveThis article proposes a holistic, automated architecture drift analysis approach that explicitly focuses on the adoption of architecture views. The approach builds on, complements, and enhances existing architecture conformance analysis methods that do not adopt a holistic approach or fail to address the architecture viewpoints.MethodA model-driven development approach is adopted in which architecture views are represented as specifications of domain-specific languages. The code in its turn, is analyzed, and the architectural view specifications are reconstructed, which are then automatically checked with the corresponding architecture models.ResultsTo illustrate the approach, we have applied a systematic case study research for an architecture drift analysis of the business-to-customer (B2C) system within a large-scale software company.ConclusionThe case study research showed that divergences and absences of architectural elements could be detected in a cost-effective manner with the proposed approach.     

A systematic review of software architecture visualization techniques

ContextGiven the increased interest in using visualization techniques (VTs) to help communicate and understand software architecture (SA) of large scale complex systems, several VTs and tools have been reported to represent architectural elements (such as architecture design, architectural patterns, and architectural design decisions). However, there is no attempt to systematically review and classify the VTs and associated tools reported for SA, and how they have been assessed and applied.ObjectiveThis work aimed at systematically reviewing the literature on software architecture visualization to develop a classification of VTs in SA, analyze the level of reported evidence and the use of different VTs for representing SA in different application domains, and identify the gaps for future research in the area.MethodWe used systematic literature review (SLR) method of the evidence-based software engineering (EBSE) for reviewing the literature on VTs for SA. We used both manual and automatic search strategies for searching the relevant papers published between 1 February 1999 and 1 July 2011.ResultsWe selected 53 papers from the initially retrieved 23,056 articles for data extraction, analysis, and synthesis based on pre-defined inclusion and exclusion criteria. The results from the data analysis enabled us to classify the identified VTs into four types based on the usage popularity: graph-based, notation-based, matrix-based, and metaphor-based VTs. The VTs in SA are mostly used for architecture recovery and architectural evolution activities. We have also identified ten purposes of using VTs in SA. Our results also revealed that VTs in SA have been applied to a wide range of application domains, among which “graphics software” and “distributed system” have received the most attention.ConclusionSA visualization has gained significant importance in understanding and evolving software-intensive systems. However, only a few VTs have been employed in industrial practice. This review has enabled us to identify the following areas for further research and improvement: (i) it is necessary to perform more research on applying visualization techniques in architectural analysis, architectural synthesis, architectural implementation, and architecture reuse activities; (ii) it is essential to pay more attention to use more objective evaluation methods (e.g., controlled experiment) for providing more convincing evidence to support the promised benefits of using VTs in SA; (iii) it is important to conduct industrial surveys for investigating how software architecture practitioners actually employ VTs in architecting process and what are the issues that hinder and prevent them from adopting VTs in SA.     

Two controlled experiments on model-based architectural decision making

ContextIn recent years, architectural design decisions are becoming more and more common for documenting software architectures. Rather than describing the structure of software systems, architectural decisions capture the design rationale and – often reusable – architectural knowledge. Many approaches and tools have been proposed in the literature to support architectural decision making and documentation (for instance, based on models, ontologies, or templates). In this context, the capturing, organization, and effective reuse of architectural knowledge has gained a lot of attention.ObjectiveHowever, there is little empirical evidence about the supportive effect of reusable architectural knowledge on the effectiveness and efficiency of architectural decision making.MethodTo investigate these aspects, we conducted two separate controlled experiments with software architecture students in which we tested the supportive effect of reusable decision models in decision making and documentation.ResultsOur results show that the use of reusable decision models can significantly increase both the efficiency and the effectiveness of novice architects.ConclusionWe can report, that our findings are in line with similar studies and support the claims regarding reusable architectural design decisions in principle.     

Requirements communication and balancing in large-scale software-intensive product development

ContextSeveral industries developing products on a large-scale are facing major challenges as their products are becoming more and more software-intensive. Whereas software was once considered a detail to be bundled, it has since become an intricate and interdependent part of most products. The advancement of software increases the uncertainty and the interdependencies between development tasks and artifacts. A key success factor is good requirements engineering (RE), and in particular, the challenges of effectively and efficiently coordinating and communicating requirements.ObjectiveIn this work we present a lightweight RE framework and demonstrate and evaluate its industrial applicability in response to the needs of a Swedish automotive company for improving specific problems in inter-departmental requirements coordination and communication in large-scale development of software-intensive systems.MethodA case study approach and a dynamic validation were used to develop and evaluate the framework in close collaboration with our industrial partner, involving three real-life cases in an ongoing car project. Experience and feedback were collected through observations when applying the framework and from 10 senior industry professionals in a questionnaire and in-depth follow-up interviews.ResultsThe experience and feedback about using the framework revealed that it is relevant and applicable for the industry as well as a useful and efficient way to resolve real problems in coordinating and communicating requirements identified at the case company. However, other concerns, such as accessibility to necessary resources and competences in the early development phases, were identified when using the method, which allowed for earlier pre-emptive action to be taken.ConclusionOverall, the experience from using the framework and the positive feedback from industry professionals indicated a feasible framework that is applicable in the industry for improving problems related to coordination and communication of requirements. Based on the promising results, our industrial partner has decided upon further validations of the framework in a large-scale pilot program.     

An empirical study on the implementation and evaluation of a goal-driven software development risk management model

ContextBuilding a quality software product in the shortest possible time to satisfy the global market demand gives an enterprise a competitive advantage. However, uncertainties and risks exist at every stage of a software development project. These can have an extremely high influence on the success of the final software product. Early risk management practice is effective to manage such risks and contributes effectively towards the project success.ObjectiveDespite risk management approaches, a detailed guideline that explains where to integrate risk management activities into the project is still missing. Little effort has been directed towards the evaluation of the overall impact of a risk management method. We present a Goal-driven Software Development Risk Management Model (GSRM) and its explicit integration into the requirements engineering phase and an empirical investigation result of applying GSRM into a project.MethodWe combine the case study method with action research so that the results from the case study directly contribute to manage the studied project risks and to identify ways to improve the proposed methodology. The data is collected from multiple sources and analysed both in a qualitative and quantitative way.ResultsWhen risk factors are beyond the control of the project manager and project environment, it is difficult to control these risks. The project scope affects all the dimensions of risk. GSRM is a reasonable risk management method that can be employed in an industrial context. The study results have been compared against other study results in order to generalise findings and identify contextual factors.ConclusionA formal early stage risk management practice provides early warning related to the problems that exists in a project, and it contributes to the overall project success. It is not necessary to always consider budget and schedule constraints as top priority. There exist issues such as requirements, change management, and user satisfaction which can influence these constraints.     

Data stream mining for predicting software build outcomes using source code metrics

ContextSoftware development projects involve the use of a wide range of tools to produce a software artifact. Software repositories such as source control systems have become a focus for emergent research because they are a source of rich information regarding software development projects. The mining of such repositories is becoming increasingly common with a view to gaining a deeper understanding of the development process.ObjectiveThis paper explores the concepts of representing a software development project as a process that results in the creation of a data stream. It also describes the extraction of metrics from the Jazz repository and the application of data stream mining techniques to identify useful metrics for predicting build success or failure.MethodThis research is a systematic study using the Hoeffding Tree classification method used in conjunction with the Adaptive Sliding Window (ADWIN) method for detecting concept drift by applying the Massive Online Analysis (MOA) tool.ResultsThe results indicate that only a relatively small number of the available measures considered have any significance for predicting the outcome of a build over time. These significant measures are identified and the implication of the results discussed, particularly the relative difficulty of being able to predict failed builds. The Hoeffding Tree approach is shown to produce a more stable and robust model than traditional data mining approaches.ConclusionOverall prediction accuracies of 75% have been achieved through the use of the Hoeffding Tree classification method. Despite this high overall accuracy, there is greater difficulty in predicting failure than success. The emergence of a stable classification tree is limited by the lack of data but overall the approach shows promise in terms of informing software development activities in order to minimize the chance of failure.     

Management of enterprise-wide systems integration programs

Automation and Information Systems play both tactical and strategic roles in the success of a business enterprise. They also pose an extremely challenging set of management issues concerned with the selection and management of technologies, management of the workforce to achieve successful implementation and operational use, and the integration of automation and information systems with business operations to capture the full benefits. Systems Integration programs involve high levels of technical and management risk commensurate with the significant business benefits that may be achieved. New and emerging automation and information technologies for Computer Integrated Manufacturing/Distribution, image processing, real-time transaction processing, and customer service cut across all organizational and functional units of an enterprise and require full system integration to capture their potential benefits. A wave of re-engineering programs are underway within commercial enterprises and the government to modify their business processes to take full competitive advantage of these technologies. There are numerous examples of both well managed and hopelessly mangled systems integration programs in both the public and private sectors that lead to the need for better management approaches. The management of software development has been a particularly troublesome area. This article presents a general model of the management structure to implement a systems integration program, using an enterprise-wide Information Systems Architecture (ISA) as a roadmap, and supported by a defined set of measures and metrics. Standards, although no panacea for systems integratio, play an important role in the architectural framework. The Information System Architecture approach described in this article is comprehensive in covering application software and data architecture as well as the computing and communications hardware infrastructure and other automation technologies that support the overall business process. The objective of the accompanying management model is to define a management structure, its essential functions, and methods that support a traditional systems development approach, as well as new approaches using prototyping and evolutionary development. Any of these management approaches require an ISA that is comprehensive in supporting business objectives, resilient in accommodating changing business conditions and technologies, and useful in producing a practical information architecture that can be applied to business operations. Examples of the ISA approach that support systems management are discussed for representative types of enterprise in the manufacturing sector and the service sector:

1. The Manufacturing/Distribution Enterprise example uses automated manufacturing and materials handling systems supported by MRPII software to integrate engineering, distribution, and financial systems; and
2. The Financial Services Enterprise example applies automated transaction processing using image processing and document management systems to achieve improved products and a more productive workflow with enhanced response to customers and/or clients.

     

Performance on agile teams: Relating iteration objectives and critical decisions to project management success factors

ContextWhile project management success factors have long been established via the golden triangle, little is known about how project iteration objectives and critical decisions relate to these success factors. It seems logical that teams’ iteration objectives would reflect project management success factors, but this may not always be the case. If not, how are teams’ objectives for iterations differing from the golden triangle of project management success factors?ObjectiveThis study identifies iteration objectives and the critical decisions that relate to the golden triangle of project management success factors in agile software development teams working in two-week iterations.MethodThe author conducted semi-structured interviews with members across three different agile software development teams using a hybrid of XP and Scrum agile methodologies. Iteration Planning and Retrospective meetings were also observed. Interview data was transcribed, coded and reviewed by the researcher and two independently trained research assistants. Data analysis involved organizing the data to identify iteration objectives and critical decisions to identify whether they relate to project management success factors.ResultsAgile teams discussed four categories of iteration objectives: Functionality, Schedule, Quality and Team Satisfaction. Two of these objectives map directly to two aspects of the golden triangle: schedule and quality. The agile teams’ critical decisions were also examined to understand the types of decisions the teams would have made differently to ensure success, which resulted in four categories of such decisions: Quality, Dividing Work, Iteration Amendments and Team Satisfaction.ConclusionThis research has contributed to the software development and project management literature by examining iteration objectives on agile teams and how they relate to the golden triangle of project management success factors to see whether these teams incorporate the golden triangle factors in their objectives and whether they include additional objectives in their iterations. What’s more, this research identified four critical decisions related to the golden triangle. These findings provide important insight to the continuing effort to better assess project management success, particularly for agile teams.     

Coproduction in successful software development projects

ContextCoproduction of new products has been deemed successful in organizational partnerships by adding to the quality and scope of the product. Techniques that involve users during the development of software tend to mimic this environment, but differ in the type of product and internal client roles. The question is thus, whether coproduction improves the outcomes of a software development project as it has in other disciplines.ObjectiveThis paper evaluates how the coproduction relationship between software developers and users improves the outcomes of a development project. Coproduction is believed to improve outcomes when available knowledge is accessible and applicable to the objective of the development project. Should the relationships hold, coproduction approaches to development can be approached with confidence and improvements made by attention to the development and deployment of expertise.MethodA quantitative questionnaire related to the coproduction environment was developed for four variables to include coproduction, applying expertise, locating expertise, and project success. 128 users from development teams responded to the survey and represent a variety of industries, individual characteristics, and project sizes.ResultsExpertise is crucial to the success of a software development project and coproduction improves the ability to access and apply the needed expertise. In addition, coproduction directly improves outcomes.ConclusionCoproduction can be an effective approach to the development of systems in terms of meeting project goals. Additionally, the assembly of expertise on the team is an important contributor to successful outcomes that may be enhanced through effective selection of team members. The ability to locate the available expertise is crucial, indicating the value of team building functions to promote awareness of expertise location.     

Learning from optimization: A case study with Apache Ant

ContextSoftware architecture degrades when changes violating the design-time architectural intents are imposed on the software throughout its life cycle. Such phenomenon is called architecture erosion. When changes are not controlled, erosion makes maintenance harder and negatively affects software evolution.ObjectiveTo study the effects of architecture erosion on a large software project and determine whether search-based module clustering might reduce the conceptual distance between the current architecture and the design-time one.MethodTo run an exploratory study with Apache Ant. First, we characterize Ant’s evolution in terms of size, change dispersion, cohesion, and coupling metrics, highlighting the potential introduction of architecture and code-level problems that might affect the cost of changing the system. Then, we reorganize the distribution of Ant’s classes using a heuristic search approach, intending to re-emerge its design-time architecture.ResultsIn characterizing the system, we observed that its original, simple design was lost due to maintenance and the addition of new features. In optimizing its architecture, we found that current models used to drive search-based software module clustering produce complex designs, which maximize the characteristics driving optimization while producing class distributions that would hardly be acceptable to developers maintaining Ant.ConclusionThe structural perspective promoted by the coupling and cohesion metrics precludes observing the adequate software module clustering from the perspective of software engineers when considering a large open source system. Our analysis adds evidence to the criticism of the dogma of driving design towards high cohesion and low coupling, at the same time observing the need for better models to drive design decisions. Apart from that, we see SBSE as a learning tool, allowing researchers to test Software Engineering models in extreme situations that would not be easily found in software projects.     

EQUITAS: A tool-chain for functional safety and reliability improvement in automotive systems

To support advanced features such as hybrid engine control, intelligent energy management, and advanced driver assistance systems, automotive embedded systems must use advanced technologies. As a result, systems are becoming distributed and include dozens of Electronic Control Units (ECU). On the one hand, this tendency raises the issue of robustness and reliability, due to the increase in the error ratio with the integration level and the clock frequency. On the other hand, due to a lack of automation, software Validation and Verification (V&V) tends to swallow up 40% to 50% of the total development cost. The ``Enhanced Quality Using Intensive Test Analysis on Simulators'' (EQUITAS¹) project aims (1) to improve reliability and functional safety and (2) to limit the impact of software V&V on embedded systems costs and time-to-market. These two achievements are obtained by (1) developing a continuous tool-chain to automate the V&V process, (2) improving the relevance of the test campaigns by detecting redundant tests using equivalence classes, (3) providing assistance for hardware failure effect analysis (FMEA) and finally (4) assessing the tool-chain under the ISO 26262 requirements.     

Suitability assessment framework of agent-based software architectures

ContextA common distributed intelligent system architecture is Multi Agent Systems (MASs). Creating systems with this architecture has been recently supported by Agent Oriented Software Engineering (AOSE) methodologies. But two questions remain: how do we determine the suitability of a MAS implementation for a particular problem? And can this be determined without AOSE expertise?ObjectiveGiven the relatively small number of software engineers that are AOSE experts, many problems that could be better solved with a MAS system are solved using more commonly known but not necessarily as suitable development approaches (e.g. object-oriented). The paper aims to empower software engineers, who are not necessarily AOSE experts, in deciding whether or not they should advocate the use of an MAS technology for a given project.MethodThe paper will construct a systematic framework to identify key criteria in a problem requirement definition to assess the suitability of a MAS solution. The criteria are first identified using an iterative process. The features are initially identified from MAS implementations, and then validated against related work. This is followed by a statistical analysis of 25 problems that characterise agent-oriented solutions previously developed to group features into key criteria.ResultsKey criteria were sufficiently prominent using factor analysis to construct a framework which provides a process that identifies within the requirements the criteria discovered. This framework is then evaluated for assessing suitability of a MAS architecture, by non-AOSE experts, on two real world problems: an electricity market simulation and a financial accounting system.ConclusionSubstituting a software engineer’s personal inclination to (or not to) use a MAS, our framework provides an objective mechanism. It can supplant current practices where the decision to use a MAS architecture for a given problem remains an informal process. It was successfully illustrated on two real world problems to assess the suitability of a MAS implementation. This paper will potentially facilitate the take up of MAS technology.     

Architecture for embedded open software ecosystems

Software is prevalent in embedded products and may be critical for the success of the products, but manufacturers may view software as a necessary evil rather than as a key strategic opportunity and business differentiator. One of the reasons for this can be extensive supplier and subcontractor relationships and the cost, effort or unpredictability of the deliverables from the subcontractors are experienced as a major problem.The paper proposes open software ecosystem as an alternative approach to develop software for embedded systems, and elaborates on the necessary quality attributes of an embedded platform underlying such an ecosystem. The paper then defines a reference architecture consisting of 17 key decisions together with four architectural patterns, and provides the rationale why they are essential for an open software ecosystem platform for embedded systems in general and automotive systems in particular.The reference architecture is validated through a prototypical platform implementation in an industrial setting, providing a deeper understanding of how the architecture could be realised in the automotive domain.Four potential existing platforms, all targeted at the embedded domain (Android, OKL4, AUTOSAR and Robocop), are evaluated against the identified quality attributes to see how they could serve as a basis for an open software ecosystem platform with the conclusion that while none of them is a perfect fit they all have fundamental mechanisms necessary for an open software ecosystem approach.     

Integrating a software architecture-centric method into object-oriented analysis and design

The choice of methodology for the development of the architecture for software systems has a direct effect on the suitability of that architecture. If the development process is driven by the user’s functional requirements, we would expect the architecture to appropriately reflect those requirements. We would also expect other aspects not captured in the functional specification to be absent from the architecture. The same phenomenon is true in development approaches that stress the importance of systemic quality attributes or other non-functional requirements; those requirements are prominent in the resulting architecture, while other requirement types not stressed by the approach are absent. In other words, the final architecture reflects the focus of the development approach. An ideal approach, therefore, is one that incorporates all goals, expectations, and requirements: both business and technical. To accomplish this we have incorporated, into a single architectural development process, generalized Object-Oriented Analysis and Design (OOAD) methodologies with the software architecture-centric method, the Quality Attribute Workshop (QAW) and Attribute Driven Design (ADD). OOAD, while relatively intuitive, focuses heavily on functional requirements and has the benefit of semantic closeness to the problem domain making it an intuitive process with comprehendible results. Architecture-centric approaches, on the other hand, provide explicit and methodical guidance to an architect in creating systems with desirable qualities and goals. They provide minimal guidance in determining fine-grained architecture, however. The integrated approach described in this paper maximizes the benefits of the respective processes while eliminating their flaws and was applied in a eight university, global development research project with great success. A case study from that experiment is included here to demonstrate the method.     

Understanding the attitudes,knowledge sharing behaviors and task performance of core developers: A longitudinal study

ContextPrior research has established that a few individuals generally dominate project communication and source code changes during software development. Moreover, this pattern has been found to exist irrespective of task assignments at project initiation.ObjectiveWhile this phenomenon has been noted, prior research has not sought to understand these dominant individuals. Previous work considering the effect of team structures on team performance has found that core communicators are the gatekeepers of their teams’ knowledge, and the performance of these members was correlated with their teams’ success. Building on this work, we have employed a longitudinal approach to study the way core developers’ attitudes, knowledge sharing behaviors and task performance change over the course of their project, based on the analysis of repository data.MethodWe first used social network analysis (SNA) and standard statistical analysis techniques to identify and select artifacts from ten different software development teams. These procedures were also used to select central practitioners among these teams. We then applied psycholinguistic analysis and directed content analysis (CA) techniques to interpret the content of these practitioners’ messages. Finally, we inspected these core developers’ activities as recorded in system change logs at various points in time during systems’ development.ResultsAmong our findings, we observe that core developers’ attitudes and knowledge sharing behaviors were linked to their involvement in actual software development and the demands of their wider project teams. However, core developers appeared to naturally possess high levels of insightful characteristics, which became evident very early during teamwork.ConclusionsProject performance would likely benefit from strategies aimed at surrounding core developers with other competent communicators. Core developers should also be supported by a wider team who are willing to ask questions and challenge their ideas. Finally, the availability of adequate communication channels would help with maintaining positive team climate, and this is likely to mitigate the negative effects of distance during distributed developments.     

Mining Patterns from Change Logs to Support Reuse-Driven Evolution of Software Architectures

Modern software systems are subject to a continuous evolution under frequently varying requirements and changes in systems’ operational environments. Lehman’s law of continuing change demands for long-living and continuously evolving software to prolong its productive life and economic value by accommodating changes in existing software. Reusable knowledge and practices have proven to be successful for continuous development and evolution of the software effectively and efficiently. However, challenges such as empirical acquisition and systematic application of the reusable knowledge and practices must be addressed to enable or enhance software evolution. We investigate architecture change logs — mining histories of architecture-centric software evolution — to discover change patterns that 1) support reusability of architectural changes and 2) enhance the efficiency of the architecture evolution process. We model architecture change logs as a graph and apply graph-based formalism (i.e., graph mining techniques) to discover software architecture change patterns. We have developed a prototype that enables tool-driven automation and user decision support during software evolution. We have used the ISO-IEC-9126 model to qualitatively evaluate the proposed solution. The evaluation results suggest that the proposed solution 1) enables the reusability of frequent architectural changes and 2) enhances the efficiency of architecture-centric software evolution process. The proposed solution promotes research efforts to exploit the history of architectural changes to empirically discover knowledge that can guide architecture-centric software evolution.