Kiana Rostami

Architecture-based Assessment and Planning of Change Requests

Software architecture reflects important decisions on structure, used technology and resources. Architecture decisions influence to a large extent requirements on software quality. During software evolution change requests have to be implemented in a way that the software maintains its quality, as various potential implementations of a specific change request influence the quality properties differently. Software development processes involve various organisational and technical roles. Thus, for sound decision making it is important to understand the consequences of the decisions on the various software engineering artefacts (e.g. architecture, code, test cases, build, or deployments) when analysing the impact of a change request. However, existing approaches do not use sufficient architecture descriptions or are limited to software development without taking management tasks into account. In this paper, we present the tool-supported approach Karlsruhe Architectural Maintainability Prediction (KAMP) to analyse the change propagation caused by a change request in a software system based on the architecture model. Using context information annotated on the architecture KAMP enables project members to assess the effects of a change request on various technical and organisational artefacts and tasks during software life cycle. We evaluate KAMP in an empirical study, which showed that it improves scalability of analysis for information systems due to automatically generated task lists containing more complete and precise context annotations than manually created ones.

I/O Performance Modeling of Virtualized Storage Systems

Server virtualization is a key technology to share physical resources efficiently and flexibly. With the increasing popularity of I/O-intensive applications, however, the virtualized storage used in shared environments can easily become a bottleneck and cause performance and scalability issues. Performance modeling and evaluation techniques applied prior to system deployment help to avoid such issues. In current practice, however, virtualized storage and its effects on the overall system performance are often neglected or treated as a black-box. In this paper, we present a systematic I/O performance modeling approach for virtualized storage systems based on queueing theory. We first propose a general performance model building methodology. Then, we demonstrate our methodology creating I/O queueing models of a real-world representative environment based on IBM System z and IBM DS8700 server hardware. Finally, we present an in-depth evaluation of our models considering both interpolation and extrapolation scenarios as well as scenarios with multiple virtual machines. Overall, we effectively create performance models with less than 11% mean prediction error in the worst case and less than 5% prediction error on average.

Modeling of I/O Performance Interference in Virtualized Environments with Queueing Petri Nets

Virtualization technology allows to share the physical resources used in IT infrastructures for efficient and flexible system operation. Sharing of physical resources, however, comes usually at the cost of performance and poses significant challenges to respect the Quality-of-Service of consolidated data-intensive applications due to the mutual performance interference among the applications. The non-trivial impact of workload consolidation on the I/O performance can be anticipated using explicit performance analysis techniques. In current practice, however, explicit modeling of I/O performance interference effects in virtualized environments is usually avoided due to their complexity. In this paper, we present an explicit performance modeling approach of I/O performance interference in virtualized environments with queueing Petri nets (QPNs). More specifically, we first highlight major challenges when modeling I/O performance in virtualized environments. Then, we create a single-VM I/O performance model calibrated with response time measurements to capture the complex behavior of a representative, real-world environment based on IBM System z and IBM DS8700 server hardware. Finally, we use the I/O performance model to evaluate the I/O performance when the workload is distributed heterogeneously on colocated virtual machines. Overall, we effectively create an I/O performance interference model capturing the I/O performance effects in a multi-VM environment with less than 10% prediction error on average.

Architecture-Based Change Impact Analysis in Information Systems and Business Processes

Business processes as well as software systems face various changes during their lifetime. As they mutually influence each other, business processes and software systems have to be modified in co-evolution. Thus, to adequately predict the change impact, it is important to consider the complex mutual dependencies of both domains. However, existing approaches are limited to analyzing the change propagation in software systems or business processes in isolation. In this paper, we present a tool-supported approach to estimate the change propagation caused by a change request in business processes or software systems based on the software architecture and the process design. We focus on the mutual dependencies regarding the change propagation between both domains. In the evaluation, we apply our approach to a community case study to demonstrate the quality of results in terms of precision, recall, and coverage.

Architecture-Based Assessment and Planning of Software Changes in Information and Automated Production Systems State of the Art and Open Issues

Information and automated production systems are long-living, evolvable systems. Consequently, modifications are performed to correct, improve or adapt the respective system. We introduce different approaches to analyze maintainability of software-intensive systems and propose two different case studies from the information and from the automated production systems domain as a basis to validate approaches on system evolution.

Towards a common classification of changes for information and automated production systems as precondition for maintenance effort estimation

Both information and automated production systems (aPS) evolve during their lifetime, e.g. due to changes in requirements and infrastructure. In order to estimate maintenance effort in information systems the KAMP method is applied. This paper discusses the necessary classification of changes as a prerequisite to apply such a method. Aggravating aPS consist not only of software but also include mechanics and electric/automation hardware. Therefore, the classification has to be enlarged to a multi-disciplinary one. The limitations of this approach for aPS are discussed in detail and demonstrated using three scenarios of a lab size pick and place unit. The paper closes delivering first ideas to cope with these.