A lightweight, graph-theoretic model of class-based similarity to support object-oriented code reuse.

The work presented in this thesis is principally concerned with the development of a method and set of tools designed to support the identification of class-based similarity in collections of object-oriented code. Attention is focused on enhancing the potential for software reuse in situations where a reuse process is either absent or informal, and the characteristics of the organisation are unsuitable, or resources unavailable, to promote and sustain a systematic approach to reuse. The approach builds on the definition of a formal, attributed, relational model that captures the inherent structure of class-based, object-oriented code. Based on code-level analysis, it relies solely on the structural characteristics of the code and the peculiarly object-oriented features of the class as an organising principle: classes, those entities comprising a class, and the intra and inter-class relationships existing between them, are significant factors in defining a two-phase similarity measure as a basis for the comparison process. Established graph-theoretic techniques are adapted and applied via this model to the problem of determining similarity between classes. This thesis illustrates a successful transfer of techniques from the domains of molecular chemistry and computer vision. Both domains provide an existing template for the analysis and comparison of structures as graphs. The inspiration for representing classes as attributed relational graphs, and the application of graph-theoretic techniques and algorithms to their comparison, arose out of a well-founded intuition that a common basis in graph-theory was sufficient to enable a reasonable transfer of these techniques to the problem of determining similarity in object-oriented code. The practical application of this work relates to the identification and indexing of instances of recurring, class-based, common structure present in established and evolving collections of object-oriented code. A classification so generated additionally provides a framework for class-based matching over an existing code-base, both from the perspective of newly introduced classes, and search "templates" provided by those incomplete, iteratively constructed and refined classes associated with current and on-going development. The tools and techniques developed here provide support for enabling and improving shared awareness of reuse opportunity, based on analysing structural similarity in past and ongoing development, tools and techniques that can in turn be seen as part of a process of domain analysis, capable of stimulating the evolution of a systematic reuse ethic

Extracting software modules as communities

Component Based Software Engineering (CBSE) is a development discipline based on the availability of software components, that are described and indexed for internal or external, present or future, reuse. Although the creation of reusable components is requested to be designed from scratch, this is often time consuming and expensive. An alternative is to extract such components from pre-existing OO software. In this work, we compare two different community detection algorithms to perform components extraction from existing software. Considering 'components' as 'communities', the aim is to evaluate how independent, yet cohesive, the components are, when extracted by community detection algorithms. Using a small sample of 3 Java systems, we show how the components can be extracted based on structural information. Furthermore, we consolidate the extracted components using semantic information, to ensure their cohesion. We use three document representation techniques to evaluate the internal cohesion of components. The results show that both algorithms perform well with each having their own strengths. Leiden extracts less cohesive, but better separated, and better clustered components that depend less on similar ones. Infomap, on the other side, creates more cohesive, slightly overlapping clusters that are more likely to depend more on other semantically similar components. </p

Tool-supported identification of functional concerns in object-oriented code

Concern identification aims to find the implementation of a functional concern in existing source code. In this work, concerns are described, using the Hierarchic Concern Model, as gray-boxes containing subconcerns, inputs, and outputs. The inputs and outputs are used as concern seeds to identify data-oriented abstractions of concern implementations, called concern skeletons. The identification approach is based on context free language reachability and supported by a tool, called CoDEx

Using Structural and Semantic Information to Identify Software Components

Component Based Software Engineering (CBSE) seeks to promote the reuse of software by using existing software modules into the development process. However, the availability of such a reusable component is not immediate and is costly and time consuming. As an alternative, the extraction from preexisting OO software can be considered.In this work, we evaluate two community detection algorithms for the task of software components identification. Considering 'components' as 'communities', the aim is to evaluate how independent, yet cohesive, the components are when extracted by structurally informed algorithms.We analyze 412 Java systems and evaluate the cohesion of the extracted communities using four document representation techniques. The evaluation aims to find which algorithm extracts the most semantically cohesive, yet separated communities.The results show a good performance in both algorithms, however, each has its own strengths. Leiden extracts less cohesive, but better separated, and better clustered components that depend more on similar ones. Infomap, on the other side, creates more cohesive, slightly overlapping clusters that are less likely to depend on other semantically similar components

Extraction of Key-Frames from an Unstable Video Feed

The APOLI project deals with Automated Power Line Inspection using Highly-automated Unmanned Aerial Systems. Beside the Real-time damage assessment by on-board high-resolution image data exploitation a postprocessing of the video data is necessary. This Master Thesis deals with the implementation of an Isolator Detector Framework and a Work ow in the Automotive Data and Time-triggered Framework(ADTF) that loads a video direct from a camera or from a storage and extracts the Key Frames which contain objects of interest. This is done by the implementation of an object detection system using C++ and the creation of ADTF Filters that perform the task of detection of the objects of interest and extract the Key Frames using a supervised learning platform. The use case is the extraction of frames from video samples that contain Images of Isolators from Power Transmission Lines