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Fault-threshold prediction with linear programming methodologies
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DOI：10.1023/A:1023006528274.png)
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摘要
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This paper presents a new experimental methodology that operates on a series of programs structural parameters. We calculated some simple metrics on these parameters and then we applied linear programming techniques on them. It was therefore possible to define a model that can predict the risk level of a program, namely how prone it is to containing faults. The new system represents the software files as points on an n-dimensional space (every dimension is one of the structural attributes for each file). Starting from this model the problem to find out the more dangerous files is brought back to the problem to separate two sets in R-n. A solution to this linear programming problem was achieved by using the MSM-T method (multisurface method tree), a greedy algorithm, which iterative divides the space in polyhedral regions till it reaches an empty set. The classification procedure is divided in two steps: the learning phase, which is used to tune the model on the specified environment and the effective selection. It is, therefore, possible to divide the n-dimensional space and find out the risk-regions of the space, which represent the dangerous files. All the process was tested in an industrial application, to validate the soundness of the methodology experimentally. A comparison between linear programming and other risk definition techniques was provided.
Keyword:
predictive metric
linear programming
risk evaluation
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