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The AspectOPTIMA project aims to build an aspect-oriented framework that provides run-time support for transactions. The previously established decomposition of the ACID (atomicity, consistency, isolation, durability) properties into ten well-defined reusable aspects had one limitation: it didn't take into account the concerns of transaction life-time support, resulting in the creation of a cross-cutting concern among the aspects. This thesis removes the cross-cutting concern by integrating the transactional life cycle management issues such as determining the transaction boundaries, maintaining a well-defined state and managing the involvement of the participants. The integration process results in the creation of new aspects that serve as building blocks for various transactional models. The thesis also demonstrates how these base aspects can be configured and composed in different ways to design customized transaction models with different concurrency control and recovery strategies.

This study proposes a new framework that can effectively apply unit testing to concurrent programs, which are difficult to develop and debug. Test-driven development, a practice enabling developers to detect bugs early by incorporating unit testing into the development process, has become wide-spread, but it has only been effective for programs with a single thread of control. The order of operations in different threads is essentially non-deterministic, making it more complicated to reason about program properties in concurrent programs than in single-threaded programs. Because hardware, operating systems, and compiler optimizations influence the order in which operations in different threads are executed, debugging is problematic since a problem often cannot be reproduced on other machines. Multicore processors, which have replaced older single-core designs, have exacerbated these problems because they demand the use of concurrency if programs are to benefit from new processors. The existing tools for unit testing programs are either flawed or too costly. JUnit, for instance, assumes that programs are single-threaded and therefore does not work for concurrent programs; ConTest and rstest predate the revised Java memory model and make incorrect assumptions about the operations that affect synchronization. Approaches such as model checking or comprehensive schedule- based execution are too costly to be used frequently. All of these problems prevent software developers from adopting the current tools on a large scale. The proposed framework (i) improves JUnit to recognize errors in all threads, a necessary development without which all other improvements are futile, (ii) places some restrictions on the programs to facilitate automatic testing, (iii) provides tools that reduce programmer mistakes, and (iv) re-runs the unit tests with randomized schedules to simulate the execution under different conditions and on different ma- chines, increasing the probability that errors are detected. The improvements and restrictions, shown not to seriously impede programmers, reliably detect problems that the original JUnit missed. The execution with randomized schedules reveals problems that rarely occur under normal conditions. With an effective testing tool for concurrent programs, developers can test pro- grams more reliably and decrease the number of errors in spite of the proliferation of concurrency demanded by modern processors.

The main goal is to develop decision policies for individual forest stand management. It addresses three major areas of interest in the optimal management of individual forest stands: incorporating a two-species growth and yield model into a single stand management model, incorporating a comprehensive list of management options into a single stand management model, and incorporating uncertainty into a single stand management model. Dynamic programming (DP) is a natural framework to study forest management with uncertainty. The forest stand management problem, as modelled in this thesis, has a large dimensional state space with a mix of discrete and continuous state variables. The DP model used to study this problem is solved by value iteration with the objective of understanding infinite horizon policies. However, since some of the state variables are continuous, all states can’t be examined in an attempt to create the cost-to-go function. Therefore, the cost-to-go function value is calculated at a given stage of the algorithm at a finite set of state points and then the cost-to-go values are approximated on the continuous portion of the state space using a continuous function. All of this is done with random processes impacting state transitions. With the mixed-species growth model developed in this thesis, a comprehensive list of management options can be incorporated into the DP model and, with the addition of uncertainty from sources such as market prices and natural disasters, near optimal stand management policies are developed. Solving the DP model with the required level of detail lead to the development of insight into function fitting on continuous state spaces and to the development of cost-to-go function approximation bounds. Studying the policies shows that the addition of uncertainty to the model captures the dynamics between market prices and stand definitions, and leads to policies that are better suited to decision making in a stochastic environment, when compared with policies that are developed with a deterministic model. Enough precision is built into the DP model to give answers to typical questions forest managers would ask.

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