Software Metrics Evaluation Based on Entropy

Software engineering activities in the Industry has come a long way with various improve- ments brought in various stages of the software development life cycle. The complexity of modern software, the commercial constraints and the expectation for high quality products demand the accurate fault prediction based on OO design metrics in the class level in the early stages of software development. The object oriented class metrics are used as quality predictors in the entire OO software development life cycle even when a highly iterative, incremental model or agile software process is employed. Recent research has shown some of the OO design metrics are useful for predicting fault-proneness of classes. In this paper the empirical validation of a set of metrics proposed by Chidamber and Kemerer is performed to assess their ability in predicting the software quality in terms of fault proneness and degradation. We have also proposed the design complexity of object-oriented software with Weighted Methods per Class metric (WMC-CK metric) expressed in terms of Shannon entropy, and error proneness

Reusability Index: A Measure for Assessing Software Assets Reusability

© 2018, Springer International Publishing AG, part of Springer Nature. The reusability of assets is usually measured through reusability indices. However, these indices either do not synthesize their constituent metrics into an aggregate or they do not capture all facets of reusability, such as structural characteristics, external qualities, and their documentation. To alleviate these shortcomings, we introduce a reusability index (REI) as a synthesis of various software metrics that cover a number of related reusability aspects. Furthermore, we evaluate its ability to quantify reuse, by comparing it to existing indices through a case study on 15 reusable open-source assets (i.e., libraries and frameworks). The results of the study suggest that the proposed index presents the highest predictive and discriminative power, it is the most consistent in ranking reusable assets, and the most strongly correlated to their levels of reuse