Modern business methods apply micro management techniques to all aspects of systems development. We investigate the use of product measures during the intra-release cycles of an application as a means of assessing release readiness. The measures include those derived from the Chidamber and Kemerer metric suite and some coupling measures of our own. Our research uses successive monthly snapshots during systems re-structuring, maintenance and testing cycles over a two year period on a commercial application written in C++. We examine the prevailing trends which the measures reveal at both component class and application level. By applying criteria to the measures we suggest that it is possible to evaluate the maturity and stability of the application thereby facilitating the project manager in making an informed decision on the application's fitness for release.

Many companies face the need to produce frequent releases of their software. Yet many do not manage to release on time and within their development schedule. The reasons for this are numerous. We suggest that an accurate insight into progress of a project towards completion would assist a project manager in balancing the various commercial decisions made through the lifetime of the release. We examine the release cycles of three local companies and find that effort estimation is the predominant means of project monitoring. As a solution to difficulties concerning ongoing project monitoring we suggest using product measures and measurement criteria which would assist a project manager when tracking change and progress within a release. We contrast these measures and measure methods to standard change data and suggest that they are more sensitive to the nature of the development activities undertaken.

Abstract This paper is concerned with enhancing aspects of project‐management techniques in relation to maintenance in commercial object‐oriented software. Maintenance forms a significant proportion of the lifecycle of a product and consequently its costs. Techniques such as lean production attempt to reduce cost by focusing team effort and to pre‐empt issues by the use of early detection and resolution strategies. This study involves a commercial application written in C++ and its evolution over a one‐year period. Software product measures are used in conjunction with Pareto analysis to determine effective predictive class targeting in a release cycle. We assess the capability of the measures and consider how the results can be used by the project team. The study extends the work of others in this field in three ways. First, other studies have analysed procedural code, whereas we use an object‐oriented code base. Second, most studies assess the predictive capability of measures against the number of software issues; we examine these factors, but uniquely test for change density using density modified measures and we also test for revision volume. Finally, we examine the implications of these results and offer a cohesive framework of recommendations placed within the lean management philosophy. Copyright © 2007 John Wiley & Sons, Ltd.

Traditional collaborative filtering generates recommendations for the active user based solely on ratings of items by other users. However, most businesses today have item ontologies that provide a useful source of content descriptors that can be used to enhance the quality of recommendations generated. In this article, we present a novel approach to integrating user rating vectors with an item ontology to generate recommendations. The approach is novel in measuring similarity between users in that it first derives factors, referred to as impacts , driving the observed user behavior and then uses these factors within the similarity computation. In doing so, a more comprehensive user model is learned that is sensitive to the context of the user visit. An evaluation of our recommendation algorithm was carried out using data from an online retailer of movies with over 94,000 movies, 44,000 actors, and 10,000 directors within the item knowledge base. The evaluation showed a statistically significant improvement in the prediction accuracy over traditional collaborative filtering. Additionally, the algorithm was shown to generate recommendations for visitors that belong to sparse sections of the user space, areas where traditional collaborative filtering would generally fail to generate accurate recommendations.

Autonomic computing provides mechanisms for the self-management of computing systems. This paper proposes context prediction as an autonomic mechanism to improve the usability of ubiquitous computing environments; in our case, for behavioral prediction in an environment that supports independent living for ageing people. A purpose of this paper is to stimulate debate on how best to improve ubiquitous computing environments for those who inhabit them; in particular for those ageing people who wish to continue to live independently. We propose a layered and extensible context architecture that provides self-managed and self-configuration capabilities. In particular, in addition to the context provider and context service layers, we propose context prediction and context fusion layers with cross-layer context-quality capabilities. We envisage a distributed peer-to-peer architecture with some form of semantic overlay network facilitating autonomic communications within and beyond the ubiquitous home environment.

Information stored in a database is often subject to uncertainty and imprecision. Probability theory provides a well-known and well understood way of representing uncertainty and may thus be used to provide a mechanism for storing uncertain information in a database. We consider the problem of aggregation using an imprecise probability data model that allows us to represent imprecision by partial probabilities and uncertainty using probability distributions. Most work to date has concentrated on providing functionality for extending the relational algebra with a view to executing traditional queries on uncertain or imprecise data. However, for imprecise and uncertain data, we often require aggregation operators that provide information on patterns in the data. Thus, while traditional query processing is tuple-driven, processing of uncertain data is often attribute-driven where we use aggregation operators to discover attribute properties. The aggregation operator that we define uses the Kullback-Leibler information divergence between the aggregated probability distribution and the individual tuple values to provide a probability distribution for the domain values of an attribute or group of attributes. The provision of such aggregation operators is a central requirement in furnishing a database with the capability to perform the operations necessary for knowledge discovery in databases.

This paper provides an overview of a research and development project on the automated testing of complex telecommunication networks. The paper reports on the current state of the project, which, for example, includes the development of a database for the organisation and the management of the data that is generated in a network test. Along this line the paper also briefly reports on a future direction in the project, a data warehouse for the management and the organisation of data that is gathered in the wider scope of the project. A major focus of the paper is the proposal and description of a decision support system (DSS). The overall aim of the DSS is to support the decision making of test engineers to further improve the automated testing of complex telecommunication networks.

Learning probabilistic networks for expert system applications has seen a great surge of research activity in recent years. The paper reports on the adoption of probabilistic networks for verifying the pass/fail result of automated testing at the software verification stage in the development lifecycle of high-speed telecommunications equipment. The focus is on learning Bayesian belief networks (BBNs) from automated test data on a per test execution basis. This facilitates result classification and assurance, taking advantage of their graphical nature to provide accountability for the decision.

Systems that are subject to uncertainty in their behaviour are often modelled by Bayesian belief networks (BBNs). These are probabilistic models of the system in which the independence relations between the variables of interest are represented explicitly. A directed graph is used, in which two nodes are connected by an edge if one is a 'direct cause' of the other. However the Bayesian paradigm does not provide any direct means for modelling dynamic systems. There has been a considerable amount of research effort in recent years to address this. We review these approaches and propose a new dynamic extension to the BBN. Our discussion then focuses on fault management of complex telecommunications and how the dynamic Bayesian models can assist in the prediction of faults.

It is frequently the case that data mining is carried out in an environment which contains noisy and missing data. This is particularly likely to be true when the data were originally collected for different purposes, as is commonly the case in data warehousing. In this paper we discuss the use of domain knowledge, e.g., integrity constraints or a concept hierarchy, to re-engineer the database and allocate sets to which missing or unacceptable outlying data may belong. Attribute-oriented knowledge discovery has proved to be a powerful approach for mining multi-level data in large databases. Such methods are set-oriented in that attribute values are considered to belong to subsets of the domain. These subsets may be provided directly by the database or derived from a knowledge base using inductive logic programming to re-engineer the database. In this paper we develop an algorithm which allows us to aggregate imprecise data and use it for multi-level rule induction and knowledge discovery. ©2000 John Wiley & Sons, Inc.

This paper compares three different data analysis methods in a subfield of the coronary heart disease risk assessment (CHDRA) area—the identification of increased blood cholesterol levels. A data set containing the cholesterol data of 166 persons is employed as a test case, and analyzed in three experimental investigations. The first analysis method employs a predominantly knowledge-based fuzzy expert system solution to the problem. The second method employs statistical discriminant analysis on the same data, whereas the results of the third analysis are obtained by an artificial neural network. This study evaluates the advantages as well as the disadvantages of each method. Special attention is given to the systems' individual capability for managing the uncertainty involved in the decision-making process. The results achieved in the study provide evidence for the complementary and mutually supporting character of the different approaches. ©2000 John Wiley & Sons, Inc.