A natural language-based method to specify privacy requirements: an evaluation with practitioners

Organisations are becoming concerned with effectively dealing with privacy-related requirements. Existing Requirements Engineering methods based on structured natural language suffer from several limitations both in eliciting and specifying privacy requirements. In our previous study, we proposed a structured natural-language approach called the “Privacy Criteria Method” (PCM), which demonstrates potential advantages over user stories. Our goal is to present a PCM evaluation that focused on the opinions of software practitioners from different companies on PCM’s ability to support the specification of privacy requirements and the quality of the privacy requirements specifications produced by these software practitioners. We conducted a multiple case study to evaluate PCM in four different industrial contexts. We gathered and analysed the opinions of 21 practitioners on PCM usage regarding Coverage, Applicability, Usefulness, and Scalability. Moreover, we assessed the syntactic and semantic quality of the PCM artifacts produced by these practitioners. PCM can aid developers in elaborating requirements specifications focused on privacy with good quality. The practitioners found PCM to be useful for their companies’ development processes. PCM is considered a promising method for specifying privacy requirements. Some slight extensions of PCM may be required to tailor the method to the characteristics of the company.