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In modern software development, maintenance accounts for the majority of the total cost and effort in a software project. Especially burdensome are those tasks which require applying a new technology in order to adapt an application to changed requirements or a different environment. This research explores methodologies, techniques, and approaches for automating such adaptive maintenance tasks. By combining high-level specifications and generative techniques, a new methodology shapes the design of approaches to automating adaptive maintenance tasks in the application domains of high performance computing (HPC) and enterprise software. Despite the vast differences of these domains and their respective requirements, each approach is shown to be effective at alleviating their adaptive maintenance burden. This thesis proves that it is possible to effectively automate tedious and error-prone adaptive maintenance tasks in a diverse set of domains by exploiting high-level specifications to synthesize specialized low-level code. The specific contributions of this thesis are as follows: (1) a common methodology for designing automated approaches to adaptive maintenance, (2) a novel approach to automating the generation of efficient marshaling logic for HPC applications from a high-level visual model, and (3) a novel approach to automatically upgrading legacy enterprise applications to use annotation-based frameworks. The technical contributions of this thesis have been realized in two software tools for automated adaptive maintenance: MPI Serializer, a marshaling logic generator for MPI applications, and Rosemari, an inference and transformation engine for upgrading enterprise applications. This thesis is based on research papers accepted to IPDPS '08 and OOPSLA '08. / Master of Science

One of the fundamental challenges in building Peer-to-Peer (P2P) applications is to locate resources across a dynamic set of nodes without centralised servers. Structured overlay networks solve this challenge by proving a key-based routing (KBR) layer that maps keys to nodes. The performance of KBR is strongly influenced by the dynamic and unpredictable conditions of P2P environments. To cope with such conditions a node must maintain its routing state. Routing state maintenance directly influences both lookup latency and bandwidth consumption. The more vigorously that state information is disseminated between nodes, the greater the accuracy and completeness of the routing state and the lower the lookup latency, but the more bandwidth that is consumed. Existing structured P2P overlays provide a set of configuration parameters that can be used to tune the trade-off between lookup latency and bandwidth consumption. However, the scale and complexity of the configuration space makes the overlays difficult to optimise. Further, it is increasingly difficult to design adaptive overlays that can cope with the ever increasing complexity of P2P environments. This thesis is motivated by the vision that adaptive P2P systems of tomorrow, would not only optimise their own parameters, but also generate and adapt their own design. This thesis studies the effects of using an adaptive technique to automatically adapt state dissemination cost and lookup latency in structured overlays under churn. In contrast to previous adaptive approaches, this work investigates the algorithmic adaptation of the fundamental data dissemination protocol rather than tuning the parameter values of a protocol with fixed design. This work illustrates that such a technique can be used to design parameter-free structured overlays that outperform other structured overlays with fixed design such as Chord in terms of lookup latency, bandwidth consumption and lookup correctness. A large amount of experimentation was performed, more than the space allows to report. This thesis presents a set of key findings. The full set of experiments and data is available online at: http://trombone.cs.st-andrews.ac.uk/thesis/analysis.