Assessing the quality of the requirements specification by applying GQM approach and using NLP tools

Software requirements are quite difficult to measure in terms of quality without reviews and subjective opinions of stakeholders. Quality assessment of specifications in an automated way saves project resources and prevents future latent defects in software. Requirements quality can be evaluated based on a huge variety of attributes, but their meaning is quite vague without any mapping to specific measurement metrics. Application of goal-question-metric (GQM) approach in the quality model helps to choose the most important quality attributes and create a mapping with metrics, which can be collected and calculated automatically. Text of software requirements written in natural language can be analyzed by NLP tools due to identify weak signle words and phrases, which make statements ambiguous. Metrics for such quality attributes as ambiguity, singularity, subjectivity, completeness, and readability are proposed in this work. The quality model was implemented in a prototype by adopting natural language processing techniques for requirements written in the Russian language with the support of external API.