An empirical quality measure based on complexity values
This article focuses on a quality measure based on complexity values. Using McCabe metric we empirically defined the level of complexity which can be considered dangerous in an industrial software developing context. We experimentally verified that files whose levels of complexity was over the fixed threshold were statistically fault prone. Adopting this kind of measure is thus possible to define specified actions with an a priori analysis (i.e. more rigorous testing sessions, restructure of code, etc.) to enhance the final quality of software. Moreover, the proposed mechanism is intrinsically dynamic. The feedback of the measure diminishes the total number of signalled errors and increases the quality of released software, and so the scale can change in time, according to the modification of the software global environment and the capabilities of the software development staff.
empirical measures methodologies quality metrics