Requirements elicitation is an important phase of any software project: the errors in requirements are more expensive to fix than the errors introduced at later stages of software life cycle. Nevertheless, many projects do not devote sufficient time to requirements. Automated requirements generation can improve the quality of software projects. In this article we have accomplished the first step of the research on this topic: we have applied the vanilla sentence autoencoder to the sentence generation task and evaluated its performance. The generated sentences are not plausible English and contain only a few meaningful words. We believe that applying the model to a larger dataset may produce significantly better results. Further research is needed to improve the quality of generated data.
翻译:要求的产生是任何软件项目的一个重要阶段:要求中的错误比软件生命周期后期错误更昂贵,比在软件使用周期后期出现的错误更昂贵,然而,许多项目没有为要求投入足够的时间。自动要求的产生可以提高软件项目的质量。在本条中,我们完成了关于这个专题的研究的第一步:我们把香草句自动编码器应用于生成句子的任务,并评价其性能。产生的句子不可信,只包含几个有意义的词。我们认为,将模型应用到更大的数据集可能会产生更好的结果。还需要进一步研究,以提高生成数据的质量。