A metric towards evaluating understandability of state machines: An empirical study

AbstractContext machines are widely used to describe the dynamic behavior of objects, components, and systems. As a communication tool between various stakeholders, it is essential that state machines be easily and correctly comprehensible. Poorly understood state machines can lead to misunderstandings and communication overhead, thus adversely affecting the quality of the final product. Nevertheless, there is a lack of measurement research for state machines. ive s paper, we propose a metric, called SUM, to evaluate the understandability of state machines. SUM is defined on the basis of cohesion and coupling concepts. idate SUM as a state machine understandability indicator, we performed an empirical study using five systems. We constructed five different state machines for each system, resulting in a total of 25 state machines being prepared. Two aspects of understandability, efficiency (UEff) and correctness (UCor), were obtained from 40 participants for the state machines. We then performed correlation and consistency analyses between the SUMs and the measured understandability values. s sults of the correlation analysis indicated that SUM was significantly correlated with UEff (p = 0.003) and UCor (p = 0.027). The consistency analysis results indicated that SUM was positively correlated with UEff in four of the systems and UCor in all five systems. sion results confirm the possibility that SUM can be a useful understandability indicator for SMs. We believe that the proposed metric can be used as a guideline to construct quality state machines.