This research proposes a language independent intra-process framework for object based composition of unmodified code modules. Intuitively, the two major programming models - threads and processes - can be considered as extremes along a sharing axis. Multiple threads through a process share all global state, whereas instances of a process (or independent processes) share no global state. Weaves provide the generalized framework that allows arbitrary (selective) sharing of state between multiple control flows through a process. In the Weaves framework a single process has the same level of complexity as a workstation, with independent "sub-processes", state sharing and scheduling, all of which is achieved without requiring any modification to existing code bases. Furthermore, the framework allows dynamic instantiation of code modules and control flows through them. In effect, weaves create intra-process modules (similar to objects in OOP) from code written in any language. Applications can be built by instantiating Weaves to form Tapestries of dynamically interacting code. The Weaves paradigm allows objects to be arbitrarily shared - it is a true superset of both processes as well as threads, with code sharing and fast context switching time similar to threads. Weaves do not require any special support from either the language or application code - practically any code can be weaved. Weaves also include support for fast automatic checkpointing and recovery with no application support. This paper presents the elements of the Weaves framework and results from our implementation that works by reverse-analyzing source-code independent ELF object files. The current implementation has been validated over Sweep3D, a benchmark for 3D discrete ordinates neutron transport [Koch et al., 1992], and our Open Network Emulator project. Performance results show that the context switch overhead in the Weaves framework is almost identical to threads. / Master of Science

This research looks at identifying and classifying changes in evolving software by making simple textual comparisons between groups of source code files. The two areas investigated are software origin analysis and collusion detection. Textual comparison is attractive because it can be used in the same way for many different programming languages. The research includes the first major study using machine learning techniques in the domain of software origin analysis, which looks at the movement of code in an evolving system. The training set for this study, which focuses on restructured files, is created by analysing 89 software systems. Novel features, which capture abstract patterns in the comparisons between source code files, are used to build models which classify restructured files fromunseen systems with a mean accuracy of over 90%. The unseen code is not only in C, the language of the training set, but also in Java and Python, which helps to demonstrate the language independence of the approach. As well as generating features for the machine learning system, textual comparisons between groups of files are used in other ways throughout the system: in filtering to find potentially restructured files, in ranking the possible destinations of the code moved from the restructured files, and as the basis for a new file comparison tool. This tool helps in the demanding task of manually labelling the training data, is valuable to the end user of the system, and is applicable to other file comparison tasks. These same techniques are used to create a new text-based visualisation for use in collusion detection, and to generate a measure which focuses on the unusual similarity between submissions. This measure helps to overcome problems in detecting collusion in data where files are of uneven size, where there is high incidental similarity or where more than one programming language is used. The visualisation highlights interesting similarities between files, making the task of inspecting the texts easier for the user.