Towards Generating Essence Kernels Using Genetic Algorithms

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@Article{Sedano:2015:PCS,

• author = "Todd Sedano and Cecile Peraire and Jason Lohn",
• title = "Towards Generating Essence Kernels Using Genetic Algorithms",
• journal = "Procedia Computer Science",
• volume = "62",
• pages = "55--64",
• year = "2015",
• note = "Proceedings of the 2015 International Conference on Soft Computing and Software Engineering (SCSE'15)",
• ISSN = "1877-0509",
• DOI = "doi:10.1016/j.procs.2015.08.410",
• URL = "http://www.sciencedirect.com/science/article/pii/S1877050915025454",
• abstract = "The Software Engineering Method and Theory (SEMAT) community created the Essence kernel as a unifying framework for describing and analysing software engineering endeavours. The Essence kernel is based upon human experience and judgement, not empirical data. Background At Carnegie Mellon University in Silicon Valley, we have collected data from masters of science in software engineering students as they complete a team-based project course as their capstone or practicum project using the Essence kernel. Each week, the team recorded their progress in an Essence Reflection meeting. This data serves as training data for evaluating the Essence kernel and alternative candidate kernels. Objective Generate candidate replacement kernels by using a fitness function based on empirical data. Method Using genetic programming, the kernel genotype is represented as a collection of linear state machines each with a collection of unique check-list items. Operations to evolve the genotypes include randomly moving check list items, splitting states, and deleting states by moving their checklist items to other states. Results Genetic programming created random candidate essence kernels that scored higher fitness scores than the original essence kernel. The purpose of this exploratory work is to demonstrate one way to generate a candidate Essence kernel directly from empirical data, not to recommend a replacement for the original Essence kernel. Reducing the Essence kernel from seven alphas to one alpha results in higher fitness scores. Limitations Given the limited amount of data, the generated kernels may be over-optimized. Additional empirical data is required before recommending replacing the original kernel with a candidate kernel that fits the data. Conclusion The original Essence kernel is highly structured around human notions of order. Genetic algorithms can generate candidate kernels that humans might not normally consider. Based on the analysis of the fitness function, a kernel with a fundamentally different structure might more effectively recommend next steps for a team during Essence Reflection Meetings.",
• keywords = "genetic algorithms, genetic programming, Essence Kernel, Empirical Research",

}

Genetic Programming entries for Todd Sedano Cecile Peraire Jason Lohn

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