Paradigms for experimentation and empirical studies in software engineering

Abstract

Abstract The software engineering field requires major advances in order to attain the high standards of quality and productivity that are needed by the complex systems of the future. The immaturity of the field is reflected by the fact that most of its technologies have not yet been analyzed to determine their effects on quality and productivity. Moreover, when these analyses have occured the resulting guidance is not quantitative but only ethereal. One fundamental area of software engineering that is just beginning to blossom is the use of measurement techniques and empirical methods. These techniques need to be adopted by software researchers and practitioners in order to help the field respond to the demands being placed upon it. This paper outlines four paradigms for experimentation and empirical study in software engineering and describes their interrelationships: (1) Improvement paradigm (2) Goal-question-metric paradigm, (3) Experimentation framework paradigm, and (4) Classification paradigm. These paradigms are intended to catalyze the use of measurement techniques and empirical methods in software engineering.