In spite of much research effort, there is no universally applicable software reliability growth ... more In spite of much research effort, there is no universally applicable software reliability growth model which can be trusted to give accurate predictions of reliability in all circumstances. Worse, we are not even in a position to be able to decide a priori which of the many models is most suitable in a particular context. Our own recent work has tried to resolve this problem by developing techniques whereby, for each program, the accuracy of various models can be analyzed. A user is thus enabled to select that model which is giving the most accurate reliability predictions for the particular program under examination. One of these ways of analyzing predictive accuracy, which we call the u-plot, in fact allows a user to estimate the relationship between the predicted reliability and the true reliability. In this paper we show how this can be used to improve reliability predictions in a very general way by a process of recalibration. Simulation results show that the technique gives improved reliability predictions in a large proportion of cases. However, a user does not need to trust the efficacy of recalibration, since the new reliability estimates produced by the technique are truly predictive and so their accuracy in a particular application can be judged using the earlier methods. The generality of this approach would therefore suggest that it be applied as a matter of course whenever a software reliability model is used. Indeed, although this work arose from the need to address the poor performance of soffware reliability models, it is likely to have applicability in other areas such as reliability growth modeling for hardware.
Evaluation of competing software reliability predictions. AA ABDEL-GHALY, PY CHAN, B LITTLEWOOD I... more Evaluation of competing software reliability predictions. AA ABDEL-GHALY, PY CHAN, B LITTLEWOOD IEEE Transactions on Software Engineering 12, 950-967, 1986. Different software reliability models can produce very different ...
Data mining systems aim to discover patterns and extract useful information from facts recorded i... more Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases.
Very large databases with skewed class distributions and non-unlform cost per error are not uncom... more Very large databases with skewed class distributions and non-unlform cost per error are not uncommon in real-world data mining tasks. We devised a multi-classifier meta-learning approach to address these three issues. Our empirical results from a credit card fraud detection task indicate that the approach can significantly reduce loss due to illegitimate transactions.
In spite of much research effort, there is no universally applicable software reliability growth ... more In spite of much research effort, there is no universally applicable software reliability growth model which can be trusted to give accurate predictions of reliability in all circumstances. Worse, we are not even in a position to be able to decide a priori which of the many models is most suitable in a particular context. Our own recent work has tried to resolve this problem by developing techniques whereby, for each program, the accuracy of various models can be analyzed. A user is thus enabled to select that model which is giving the most accurate reliability predictions for the particular program under examination. One of these ways of analyzing predictive accuracy, which we call the u-plot, in fact allows a user to estimate the relationship between the predicted reliability and the true reliability. In this paper we show how this can be used to improve reliability predictions in a very general way by a process of recalibration. Simulation results show that the technique gives improved reliability predictions in a large proportion of cases. However, a user does not need to trust the efficacy of recalibration, since the new reliability estimates produced by the technique are truly predictive and so their accuracy in a particular application can be judged using the earlier methods. The generality of this approach would therefore suggest that it be applied as a matter of course whenever a software reliability model is used. Indeed, although this work arose from the need to address the poor performance of soffware reliability models, it is likely to have applicability in other areas such as reliability growth modeling for hardware.
Evaluation of competing software reliability predictions. AA ABDEL-GHALY, PY CHAN, B LITTLEWOOD I... more Evaluation of competing software reliability predictions. AA ABDEL-GHALY, PY CHAN, B LITTLEWOOD IEEE Transactions on Software Engineering 12, 950-967, 1986. Different software reliability models can produce very different ...
Data mining systems aim to discover patterns and extract useful information from facts recorded i... more Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases.
Very large databases with skewed class distributions and non-unlform cost per error are not uncom... more Very large databases with skewed class distributions and non-unlform cost per error are not uncommon in real-world data mining tasks. We devised a multi-classifier meta-learning approach to address these three issues. Our empirical results from a credit card fraud detection task indicate that the approach can significantly reduce loss due to illegitimate transactions.
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Papers by Patrick Chan