Quantitative Methods in Object-Oriented Software Engineering

This paper includes a brief description of the author’s doctoral research work in Quantitative Methods applied to the Object-Oriented Software Engineering field. Previous, current and future research work are outlined. An overview of related work is also included

A Database Approach for Modeling and Querying Video Data

Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].This is an extended version of the article in: 15th International Conference on Data Engineering, Sydney, Australia, 1999

Distributed Memo: A Heterogeneously Distributed and Parallel Software Development Environment

Heterogeneously distributed and parallel computing environments are highly dependent on hardware, data migration, and protocols. The result is significant difficulty in software reuse, portability across platforms, and an increased overall development effort. The appearance of a shared directory of unordered queues can be provided by integrating heterogeneous computers transparently. This integration provides a conducive environment for parallel and distributed application development, by abstracting the issues of hardware and communication. Object oriented technology is exploited to provide this seamless environment

A document based traceability model for test management

Software testing has became more complicated in the emergence of distributed network, real-time environment, third party software enablers and the need to test system at multiple integration levels. These scenarios have created more concern over the quality of software testing. The quality of software has been deteriorating due to inefficient and ineffective testing activities. One of the main flaws is due to ineffective use of test management to manage software documentations. In documentations, it is difficult to detect and trace bugs in some related documents of which traceability is the major concern. Currently, various studies have been conducted on test management, however very few have focused on document traceability in particular to support the error propagation with respect to documentation. The objective of this thesis is to develop a new traceability model that integrates software engineering documents to support test management. The artefacts refer to requirements, design, source code, test description and test result. The proposed model managed to tackle software traceability in both forward and backward propagations by implementing multi-bidirectional pointer. This platform enabled the test manager to navigate and capture a set of related artefacts to support test management process. A new prototype was developed to facilitate observation of software traceability on all related artefacts across the entire documentation lifecycle. The proposed model was then applied to a case study of a finished software development project with a complete set of software documents called the On-Board Automobile (OBA). The proposed model was evaluated qualitatively and quantitatively using the feature analysis, precision and recall, and expert validation. The evaluation results proved that the proposed model and its prototype were justified and significant to support test management

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

HydroShare – A Case Study of the Application of Modern Software Engineering to a Large Distributed Federally-Funded Scientific Software Development Project

HydroShare is an online collaborative system under development to support the open sharing of hydrologic data, analytical tools, and computer models. With HydroShare, scientists can easily discover, access, and analyze hydrologic data and thereby enhance the production and reproducibility of hydrologic scientific results. HydroShare also takes advantage of emerging social media functionality to enable users to enhance information about and collaboration around hydrologic data and models. HydroShare is being developed by an interdisciplinary collaborative team of domain scientists, university software developers, and professional software engineers from ten institutions located across the United States. While the combination of non–co-located, diverse stakeholders presents communication and management challenges, the interdisciplinary nature of the team is integral to the project’s goal of improving scientific software development and capabilities in academia. This chapter describes the challenges faced and lessons learned with the development of HydroShare, as well as the approach to software development that the HydroShare team adopted on the basis of the lessons learned. The chapter closes with recommendations for the application of modern software engineering techniques to large, collaborative, scientific software development projects, similar to the National Science Foundation (NSF)–funded HydroShare, in order to promote the successful application of the approach described herein by other teams for other projects