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Nature-inspired Approaches in Software Faults Identification and Debugging

机译:软件故障识别和调试中自然启发的方法

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摘要

This paper considers software faults identification along the phases from design to testing and debugging. The following subjects are reviewed and extended: bio-inspired concepts for structuring resilient systems, genetic strategies in test data generation, Ant Colony Optimisation (ACO) algorithms for data flow analyzing and testing, artificial immune systems (AIS) based mutation testing, and fault tolerant approaches inspired by immunity principles in order to increase the software dependability. Data collected during software development cycle can be used to understand the software project evolution and its reliability.
机译:本文从设计到测试和调试的各个阶段都考虑了软件故障的识别。审查和扩展了以下主题:用于构建弹性系统的受生物启发的概念,测试数据生成中的遗传策略,用于数据流分析和测试的蚁群优化(ACO)算法,基于人工免疫系统(AIS)的突变测试以及故障受免疫原理启发的容忍方法,以提高软件的可靠性。在软件开发周期中收集的数据可用于了解软件项目的发展及其可靠性。

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