# Testing a New Structured Tool for Supporting Requirements' Formulation and Decomposition
## 2 Materials and Methods
### Data Analysis  
#### 2.4.1 Metrics for Requirements' Formulation Evaluation  
The very first parameter used to perform a comparison between the control group and the analysis group is the number of generated requirements. However, according to [PERSON] and [PERSON] [25], it is possible to identify other important characteristics of a requirement list:  
* Validity--as the capability of the requirement to discriminate the extent of the achievement of a certain objective;
* Completeness--as the capability of the whole specification to cover all the objectives in the different domains where stakeholders are involved;
* Operationality--as the capability of the requirement to make the achievement of the objective measurable, to avoid subjective evaluations of (partial) solutions;
* Non-redundancy--as the capability of the specification to be free from duplicates;
* Conciseness--as the capability of the specification to contain just the meaningful requirements, without neglecting important facets to be taken into account (not too many, not too few);
* Practicability--as the capability of the requirements to be tested, e.g., with simulations or by exploiting available information. However, Conciseness and Practicability have not been considered in this study, as they do not apply to the specific circumstances of this work. Indeed, the evaluation of conciseness is unfeasible with an aprioristic logic, i.e., without a clear idea of what is the expected result. The evaluation of practicability is also troublesome, since the actual viability of requirements for testing and simulations depends on the expected tests and simulations, which are omitted for the sake of simplifying the design test. Hence, the lack of information about these aspects led to the impossibility to apply this specific metric too. Two of the considered metrics (Validity and Operationality) can be evaluated by relying on the Element-Name-Value (ENV) fractal model [49]. To provide a short explanation of the ENV model, the sentence \"A tomato is a vegetable, round and red\" can be modelled as it follows: \"Tomato\" is the element (E) which has a parameter named (N) color, which has a particular value (V): Red. More specifically, well-formulated requirements, i.e., those where the key parameters E and N are present, are to be considered valid, as they clearly point out the goal to fulfill. Differently, if a specific requirement lacks one of the two parameters (E and N), it is considered here as invalid. Instead, the assessment of requirements' operationality requires the value V to be clarified by the formulation (i.e., if a value is missing or, more in general, not measurable for the requirement as formalized, it is not operational). To give a more specific example, please consider the following requirement extracted (and translated in English) from the case study under consideration. \"The device\" (Element) \"can be powered on\" (Name of the parameter: activation) \"with just one hand\" (Value). This requirement is valid because the Element and the Name are present. Additionally, it is also operational because the Value parameter is present too. With regard to the Non-redundancy metric, it can be applied simply by counting how many times the same (or similar) requirement appears in each specific set, e.g., a requirement that uses different words, but targets the same ENV triad. As for the Completeness metric, the set of stakeholders involved in the lifecycle of the specific product has been identified (see Table 4). The set of stakeholders has been extracted by following an \"a posteriori approach\", i.e., by analyzing the entire set of requirements generated by all students of all groups. Accordingly, a \"complete\" specification generated by a specific subject should take into account each of these classes of stakeholders, or at least the different life cycle phases in which said stakeholders are relevant (last column of Table 4). Subjects were not informed about the metrics applied to the results in order to avoid biased outcomes.