Abstract
The ability to predict the quality of a software object can be viewed as a classification problem, where software metrics are the features and expert quality rankings the class labels. Evolutionary computational techniques such as genetic algorithms can be used to find a subset of metrics that provide an optimal classification for the quality of software objects. Genetic algorithms are also parallelizable, in that the fitness function (how well a set of metrics can classify the software objects) can be calculated independently from other possible solutions. A manager-worker parallel version of a genetic algorithm to find optimal metrics has been implemented using MPI and tested on a Beowulf cluster resulting in an efficiency of 0.94. Such a speed-up facilitated using larger populations for longer generations. Sixty-four source code metrics from a 366 class Java-based biomedical data analysis program were used and resulted in classification accuracy of 78.4%.
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Vivanco, R., Pizzi, N. (2004). Finding Effective Software Metrics to Classify Maintainability Using a Parallel Genetic Algorithm. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_159
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DOI: https://doi.org/10.1007/978-3-540-24855-2_159
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