SAM 2015 - 2nd International Workshop on Software Architecture and Metrics

Software engineers of complex software systems face the challenge of how best to assess the achievement of quality attributes and other key drivers, how to reveal issues and risks early, and how to make decisions about architecture and system evolution. There is an increasing need to provide ongoing quantifiable insight into the quality of the system being developed to manage the pace of software delivery and technology churn.
Additionally, it is highly desirable to improve feedback between development and deployment through measurable means for intrinsic quality, value, and cost. While there is body of work focusing on code quality and metrics, their applicability at the design and architecture level and at scale are inconsistent and not proven. We are interested in exploring whether architecture can assist with better contextualizing existing system and code quality and metrics approaches. Furthermore, we ask whether we need additional architecture-level metrics to make progress and whether something as complex and subtle as software architecture can be quantified.
The goal of this workshop is to discuss progress on architecture and metrics, measurement, and analysis; to gather empirical evidence on the use and effectiveness of metrics; and to identify priorities for a research agenda. The workshop addresses both academic researchers and industrial practitioners for an exchange of ideas and collaboration.
Choose a Topic
We are seeking papers on practical experiences and research approaches to evaluate and manage architecture through metrics including, but not limited to, the following topics:
Proposing and validating new metrics
architecture quality, value, cost, and uncertainty
architecture properties: understandability, maintainability, evolvability, concern dispersion, and modularization
architecture models and views: completeness, consistency, and violation of reference models or patterns
traceability: the connection between architecture and other artifacts, such as requirements and code
architecture knowledge and decision models: confidence, completeness, relevance, and coverage
Creating and validating tools and techniques
eliciting and visualizing architecture metrics
composing architecture metrics by aggregating or combining code-level metrics
associating multiple views and quality concerns with metrics
Using architecture and metrics
application to software evolution, maintenance, refactoring, or software aging
analytics on software architecture data for managers and software engineers to make better decisions
support for project management with data such as velocity, scrap and rework rates, and uncertainty
use by product management for the software business case
input to economic models: technical debt management, real option analysis, and valuation
Principles and practices
Creating principles for industrial software architecture metrics
Executing empirical studies on how architecture metrics are used in practice and their effectiveness