Effort Estimation in Capturing Architectural Knowledge

Global software development (GSD) is considered as the coordinated activities of software development that are geographically and temporally distributed. The management of architectural knowledge, specifically, architectural design decisions (ADDs), becomes important in GSD due to the geographical, temporal, and cultural challenges in global environments. Based on our previous work on ADD management in localized software development, we present five ADD paradigms for GSD projects with different organizational structures. We also investigate the benefits and the challenges of these ADD paradigms by conducting an evaluation of the paradigms using extensive archived semi-structured interview data from industrial GSD projects. We aim to provide a fundamental framework for managing ADD documentation and evolution in GSD, as well as offer useful insights into managing architectural knowledge in a global setting.

There is growing interest in continuous delivery practices to enable rapid and reliable deployment. While practices are important, we suggest architectural design decisions are equally important for projects to achieve goals such continuous integration (CI) build, automated testing and reduced deployment-cycle time. Architectural design decisions that conflict with deploy ability goals can impede the team's ability to achieve the desired state of deployment and may result in substantial technical debt. To explore this assertion, we interviewed three project teams striving to practicing continuous delivery. In this paper, we summarize examples of the deploy ability goals for each project as well as the architectural decisions that they have made to enable deploy ability. We present the deploy ability goals, design decisions, and deploy ability tactics collected and summarize the design tactics derived from the interviews in the form of an initial draft version hierarchical deploy ability tactic tree.

Visualization of architectural design decision (ADD) and its rationale, as a kind of traceability information, is supposed to facilitate the understanding of architecture design and the reasoning behind the design rationale, which is supposed to improve the architecting process and gets better architecture design results. But the lack of empirical evaluation that supports this statement prevents industrial practitioners from using ADD visualization in their daily architecting activities. In this paper, we conducted a controlled experiment to investigate how visualization of ADD can improve the understanding of architecture design by using Compendium, an open source tool for rationale visualization. The preliminary results show that visualization of ADD and its rationale does not increase the total time for reading architecture document (including visualization diagrams of ADDs) plus designing task, and it improves the understanding of architects on existing architecture design with better new design results.

An effective software design should ensure that all the quality requirements of a system of interest are supported. As the architecture of a software system can be seen as a set of architectural design decisions (ADDs), the identification of design issues and the choice of design alternatives heavily impact the quality of software systems. This is especially challenging in service orientation for which students need to change their mindset from system design to service-oriented design. In this paper, we present a template driven approach for documenting quality-driven ADDs in an educational setting. We next report on our experience with the usage of the template in a service-oriented software design Master course given over the past three years. We found that the template not only offers a framework guiding the students in their design but also encourages them to make sound ADDs, which leads to a service-oriented design with higher quality.

Designing and maintaining a software system’s architecture typically involve making numerous design decisions, each potentially affecting the system’s functional and nonfunctional properties. Understanding these design decisions can help inform future decisions and implementation choices and can avoid introducing regressions and architectural inefficiencies later. Unfortunately, design decisions are rarely well documented and are typically a lost artifact of the architecture creation and maintenance process. The loss of this information can thus hurt development. To address this shortcoming, we develop RecovAr, a technique for automatically recovering design decisions from the project’s readily available history artifacts, such as an issue tracker and version control repository. RecovAr uses state-of-the-art architectural recovery techniques on a series of version control commits and maps those commits to issues to identify decisions that affect system architecture. While some decisions can still be lost through this process, our evaluation on Hadoop and Struts, two large open-source systems with over 8 years of development each and, on average, more than 1 million lines of code, shows that RecovAr has the recall of 75% and a precision of 77%. Our work formally defines architectural design decisions and develops an approach for tracing such decisions in project histories. Additionally, the work introduces methods to classify whether decisions are architectural and to map decisions to code elements. Finally, our work contributes a methodology engineers can follow to preserve design-decision knowledge in their projects.

In software architectures, architectural design decisions (ADDs) strongly influence the quality of the resulting software system. Wrong decisions lead to low-quality systems and are difficult to repair later on in the development process. As of today, little is known about the impact of certain ADDs for the development of architectures for blockchain-based systems. Thus, it is difficult to predict the outcome of certain ADDs when developing architectures for such systems. In the following, we propose a simulation-based approach for blockchain architectures in which the impact of certain ADDs on certain quality attributes can be simulated. To this end, we first implemented a simulation environment for blockchain architectures. The simulation environment was then used to execute a series of experiments from which we derived a set of hypotheses about the impact of certain ADDs on quality attributes for blockchain architectures. Finally, we tested the hypotheses using statistical analyses and derived an empirical model for blockchain architectures based on the outcome of the analysis. The model can be used by architects to predict the effect of certain decisions in the design of blockchain architectures before implementing them.

The architecture of a software-intensive system is the composition of architectural design decisions. These decisions are an important part of Architectural Knowledge (AK). Failure to document architectural design decisions can lead to AK vaporization and higher maintenance costs. To reduce AK vaporization, we propose to apply the Repertory Grid Technique (RGT) to make tacit knowledge about architecture decisions explicit. An architect can use the RGT to elicit decision alternatives and concerns, and to rank each alternative against concerns. To validate our approach, we conducted a survey with graduate students. In the survey, participants documented decisions using the RGT. We compared these decisions with decisions documented using a basic decision template. Our results suggest that RGT leads to less AK vaporization, compared to conventional ways of documenting decisions.Keywordsarchitectural knowledgerepertory gridAK vaporizationsurvey

Significant challenges arise when capturing architectural knowledge and design decisions for a software project, resulting in high capturing effort and few captured decisions. These challenges stem from the fact that software development is a group activity that involves different situations, goals, methodologies and needs of the software development organization. A projectpsilas documentation processes should be flexible to suit the needs and goals of an organization and allow multiple approaches. We propose three approaches for capturing design decisions to increase the knowledge retention and decision capture rates in an organization: 1) formal elicitation; 2) lightweight top-down capture; and 3) lightweight bottom-up capture. Customizable capturing methods are suggested to implement the approaches and a tool is created for each approach. Industry feedback and preliminary findings show the feasibility of the proposed approaches.

In software architecture design, we explore design alternatives and make decisions about adoption or rejection of a design from a web of complex and often uncertain information. Different architectural design decisions may lead to systems that satisfy the same set of functional requirements but differ in certain quality attributes. In this paper, we propose a Bayesian Network based approach to rational architectural design. Our Bayesian Network helps software architects record and make design decisions. We can perform both qualitative and quantitative analysis over the Bayesian Network to understand how the design decisions influence system quality attributes, and to reason about rational design decisions. We use the KWIC (Key Word In Context) example to illustrate the principles of our approach.

Microservice APIs are often identified and designed based on Domain-Driven Design (DDD). To help in the continuous analysis of mappings of domain model elements to APIs and API endpoints, we aim to automate the assessment of conformance to Architectural Design Decision (ADD) options. The ADDs, their decision options, and relevant decision drivers studied in this paper originate from an empirical study on the mapping of domain model elements to APIs and API endpoints in practice. We propose automated detectors to detect the decision options of the ADDs taken in a given microservice API model, and an assessment scoring scheme based on the empirical knowledge codified in the ADDs. We evaluate our work, by first manually creating a ground truth for 14 cases in a multi-case study, and then comparing the results of our automated detectors to the ground truth for each of the 14 cases. In the cases we were able to identify 86% of the decision points correctly, and a statistical analysis of our data shows only a negligible effect size for differences to the ground truth.

When engineering complex software systems, the key Architectural Design Decisions (ADD) and the reasoning underlying those decisions need to be fully understood by all stakeholders. Achieving such understanding usually requires the use of ADD management tools. Most existing ADD management tools apply prescriptive ADD models and do not provide sufficient customizability. However, forcing architects to follow an ADD model that does not fit their specific needs can cause significant problems (e.g., extra cost is needed, and architects' willingness and motivation can negatively be affected). This research project aims at solving this issue by developing a highly customizable solution, which can enable practitioners to define ADD models according to their preferences and working situations. The detailed needs for ADD model customization will be identified by multiple case studies and semi-structured interviews; the proposed solution will be evaluated using different empirical research methods.

Over the past 10 years software architecture has been perceived as the result of a set of architecture design decisions rather than the elements that form part of the software design. As quality attributes are considered major drivers of the design process to achieve high quality systems, the design decisions that drive the selection and use of specific quality properties and vice versa are closely related. Consequently, quality attributes must play a role for decision making processes and be documented alongside the decisions captured. Consequently, we conduct a systematic literature review to study the importance and impact of the relationships between quality attributes and architecture design decisions and to what extent existing architecture knowledge management methods and tools deal with the decisions that affect the quality of a system. We also report on the challenges and future research paths for architectural knowledge management methods and tools. Our results reveal important explicit relationships between both software artifacts, the role of uncertainty in decision making and empirical studies reporting the use of quality attributes in architecture knowledge management activities.

The benefits of a software system are assessable only relative to the business goals the system has been developed to serve. In turn, these benefits result from interactions between the system's functionality and its quality attributes (such as performance, reliabilty and security). Its quality attributes are, in most cases, dictated by its architectural design decisions. Therefore, we argue in this paper that the software architecture is the crucial artifact to study in making design tradeoffs and in performing cost-benefit analyses. A substantial part of such an analysis is in determining the level of uncertainty with which we estimate both costs and benefits. In this paper we offer an architecture-centric approach to the economic modeling of software design decision making called CBAM (Cost Benefit Analysis Method), in which costs and benefits are traded off with system quality attributes. We present the CBAM, the early results from applying this method in a large-scale case study, and discuss the application of more sophisticated economic models to software decision making.

Software architecture can be represented as a set of design decisions. Exploring and analyzing architectural design decisions are difficult due to how the decisions are represented and displayed. We describe four visualization aspects that apply to architectural design decision exploration and analysis: 1) tabular listing; 2) decision structure visualization; 3) decision chronology visualization; and 4) decision impact visualization. These aspects address some situations where visualization helps people understand and utilize decisions.KeywordsDesign DecisionDecision StructureDecision AttributeDecision InformationNode CircleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

System quality attributes (QAs) are often considered as the most important decision drivers. In this paper, motivated by the decision making in a smart-city software ecosystem, we extend our previous approach that integrates reusable architectural design decisions (ADDs) with the QAs, by integrating tactics that support quality-driven decision making. In addition, we present an approach that enables system evolution, based on controlled and adaptable decision making and utilizing real data obtained during system monitoring. The approach integrates the previous approach that uses tactics with the existing model-driven development paradigm and the corresponding tools.