Reconstruction of software component architectures and behaviour models using static and dynamic analysis

Model-Based Performance Prediction (MBPP, [BDIS04a]) is a software engineering discipline which systematically deals with the evaluation of software performance. MBPP’s central idea is to predict the performance of a software system based on performance models. MBPP can be applied at design-time to avoid bottlenecks when designing a software architecture but also for existing software systems. For existing software systems, one is interested in scalability analysis and resource sizing without actually buying expensive hardware and setting up the execution environment for each possible execution scenario. Additionally, when extending an existing software system by a new component, software performance models allow to estimate the impact of the extension and help avoiding the introduction of bottlenecks. Consider the example of a legacy accounting application: When extending such an application by a new reporting component, it should be estimated how the overall performance (e.g. response time) of the system is affected. Applying MBPP requires the presence of up-to-date software performance models. To reason on software architectures, these models must capture the architecture itself as well as the behaviour of each architecture component. Unfortunately, current reverse engineering techniques often aim at the static software architecture and understanding of software systems [CZvD09]. No approach reverse engineers software performance models at an architectural level which are required to enable software performance engineering. Thus, currently performance models must be created manually when aiming at the support of design decisions for software architectures. The contribution of this thesis is a new integrated reverse engineering approach for the reconstruction of parameterised software component architectures and software component behaviour models which can serve as software performance models due to the execution semantics of the target model. This approach allows reverse engineering behaviour models for each component’s service from code using static, dynamic, and statistical analysis techniques. For performance prediction, the Palladio Component Model Approach [BKR09] is used. The new reverse engineering approach reconstructs static architecture information (components, interfaces, and connectors) as well as a performance behaviour model capturing control and data flow for each provided service of a component. The reverse engineered models are semantically rich so they can serve for performance simulation approaches without requiring manual complements. Since these models are highly parameterised (avoiding constants) they not only help understanding the current state of a software system, the reverse engineered models help planning and changing a software system in an efficient way at the model level. The reverse engineered models support a large variety of design decisions at the model level with respect to their performance impact: architectural refactorings, exchanging components, extensions of legacy software systems (e.g. introducing new components), performance optimisations (e.g. introducing caches or distribution), sizing of the hardware environment (e.g.