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Requirements Specification-by-Example Using a Multi-objective Evolutionary Algorithm

机译:要求使用多目标进化算法的规范 - 示例

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A task at the beginning of the software development process is the creation of a requirements specification. The requirements specification is usually created by a software engineering expert. We try to substitute this expert by a domain expert (the user) and formulate the problem of creating requirements specifications as a search-based software engineering problem. The domain expert provides only examples of event sequences that describe the behavior of the required software program. These examples are represented by simple sequence diagrams and are divided into two subsets: positive examples of required program behavior and negative examples of prohibited program behavior. The task is then to synthesize a generalized requirements specification that usefully describes the required software. We approach this problem by applying a genetic algorithm and evolve deterministic finite automata (DFAs). These DFAs take the sequence diagrams as input that should be either accepted (positive example) or rejected (negative example). The problem is neither to find the minimal nor the most general automaton. Instead, the user should be provided with several appropriate automata from which the user can select, or which help the user to refine the examples given initially. We present the context of our research ("On-The-Fly Computing"), present our approach, report results indicating its feasibility, and conclude with a discussion.
机译:软件开发过程开始的任务是创建要求规范。要求规范通常由软件工程专家创建。我们尝试通过域专家(用户)替换本专家,并制定创建需求规范作为基于搜索的软件工程问题的问题。域专家仅提供描述所需软件程序行为的事件序列的示例。这些示例由简单的序列图表示,并且被分成两个子集:所需程序行为的正示例和违禁程序行为的否定示例。然后,该任务是合成有用描述所需软件的广义需求规范。我们通过应用遗传算法和演变确定性有限自动机(DFA)来解决这个问题。这些DFA将序列图作为输入应该被接受(正示例)或被拒绝(否定示例)。问题既不是最小的也不是最普通的自动机。相反,用户应该提供几种适当的自动机,用户可以从中选择,或者帮助用户最初提供给定的示例。我们介绍了我们的研究背景(“飞行计算”),呈现我们的方法,报告结果表明其可行性,并在讨论结束。

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