Connecting Software Metrics across Versions to Predict Defects

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models

An Empirical Validation of Object-Oriented Design Metrics for Fault Prediction

Object-oriented design has become a dominant method in software industry and many design metrics of object-oriented programs have been proposed for quality prediction, but there is no well-accepted statement on how significant those metrics are. In this study, empirical analysis is carried out to validate object-oriented design metrics for defects estimation. Approach: The Chidamber and Kemerer metrics suite is adopted to estimate the number of defects in the programs, which are extracted from a public NASA data set. The techniques involved are statistical analysis and neuro-fuzzy approach. Results: The results indicate that SLOC, WMC, CBO and RFC are reliable metrics for defect estimation. Overall, SLOC imposes most significant impact on the number of defects. Conclusions/Recommendations: The design metrics are closely related to the number of defects in OO classes, but we can not jump to a conclusion by using one analysis technique. We recommend using neuro-fuzzy approach together with statistical techniques to reveal the relationship between metrics and dependent variables, and the correlations among those metrics also have to be considered

The impact of developer team sizes on the structural attributes of software

It is established that the internal quality of software is a key determinant of the total cost of ownership of that software. The objective of this research is to determine the impact that the development team’s size has on the internal structural attributes of a codebase and, in doing so, we consider the impact that the team’s size may have on the internal quality of the software that they produce. In this paper we leverage the wealth of data available in the open-source domain by mining detailed data from 1000 projects in Google Code and, coupled with one of the most established of object-oriented metric suites, we isolate and identify the effect that the development team size has on internal structural attributes of the software produced. We will find that some measures of functional decomposition are enhanced when we compare projects authored by fewer developers against those authored by a larger number of developers while measures of cohesion and complexity are degraded