Entropies as measures of software information

This paper investigates the rise of entropies as measures of software information content. Several entropies, including the well-known Shannon entropy,, are characterised by, their mathematical properties. Based on these characterizations, the entropies, which are suitable for measuring software systems, are rigorously chosen. By, treating a software system as an information source, the function calls in procedural systems or method invocations in object-oriented systems resemble the emission of symbols from an information source. Thus, the probabilities, required for computing the entropies, are obtained using an empirical distribution of function calls or method invocations. Application of the suggested measures on procedural and object-oriented programs is further explained using two small examples. Because a rigorous definition of information measures does not guarantee their usefulness in practice, an evaluation case study, is performed. In particular, the aim of this study, is to practically, evaluate the intuitiveness and scalability of the measures on a real soft-ware system totaling about 460,000 lines of code. In addition to being intuitive and meaningful, the case study, results highlight differences between the information measures. Thus, the family, of measures presented in this paper can satisfy, different measurement requirements.