JEqualityGen: Generating Equality and Hashing Methods

Manually implementing equals (for object comparisons) and hashCode (for object hashing) methods in large software projects is tedious and error-prone. This is due to many special cases, such as field shadowing, comparison between different types, or cyclic object graphs. Here, we present JEqualityGen, a source code generator that automatically derives implementations of these methods. JEqualityGen proceeds in two states: it first uses source code reflection in MetaAspectJ to generate aspects that contain the method implementations, before it uses weaving on the bytecode level to insert these into the target application. JEqualityGen generates not only correct, but efficient source code that on a typical large-scale Java application exhibits a performance improvement of more than two orders of magnitude in the equality operations generated, compared to an existing system based on runtime reflection. JEqualityGen achieves this by generating runtime profiling code that collects data. This enables it to generate optimised method implementations in a second round

Transparent and adaptive application partitioning using mobile objects

The dynamic nature and heterogeneity of modern execution environments such as mobile, ubiquitous, and grid computing, present major challenges for the development and efficient execution of the applications targeted for these environments. In particular, applications tailored to run in a specific environment will show different and most likely sub-optimal behaviour when executed on a different and/or dynamic environment. Consequently, there has been growing interests in the area of application adaptation which aims to enable applications to cope with the varying execution environments. Adaptive application partitioning, a specific form of non-functional adaptation involving distribution of mobile objects across multiple host machines, is of particular interest to this thesis due to the diversity of its uses. In this approach, certain runtime information (known as context) is used to allow an object-oriented application to adaptively (re)adjust the placement of its objects during its execution, for purposes such as improving application performance and reliability as well as balancing resource utilisation across machines. Promoting the adoption of such adaptation requires a process that requires minimal human involvement in both the execution and the development of the relevant application. These challenges establish the main goals and contributions of this work, which include: 1) Proposing an effective application partitioning solution via the adoption of a decentralised adaptation strategy known as local adaptation. 2) Enabling adaptive application partitioning which does not require human intervention, through automatic collection of required information/context. 3) Proposing a solution for transparently injecting the required adaptation functionality into regular object-oriented applications allowing significant reduction of the associated development cost/effort. The proposed solutions have been implemented in a Java-based adaptation framework called MobJeX. This implementation, which was used as a test bed for the empirical experiments undertaken in this study, can be used to facilitate future research relevant to this particular study