The impact of software complexity on cost and quality - A comparative analysis between Open source and proprietary software
It addresses the problem of early software quality prediction for developers and managers, but is incremental as it aggregates existing studies without introducing new methods.
This paper conducted a systematic review and meta-analysis of 59 datasets from 57 studies to investigate how software complexity metrics influence quality attributes, finding that fault proneness and maintainability are most studied, with no significant differences between proprietary and open source projects.
Early prediction of software quality is important for better software planning and controlling. In early development phases, design complexity metrics are considered as useful indicators of software testing effort and some quality attributes. Although many studies investigate the relationship between design complexity and cost and quality, it is unclear what we have learned beyond the scope of individual studies. This paper presented a systematic review on the influence of software complexity metrics on quality attributes. We aggregated Spearman correlation coefficients from 59 different data sets from 57 primary studies by a tailored meta-analysis approach. We found that fault proneness and maintainability are most frequently investigated attributes. Chidamber and Kemerer metric suite is most frequently used but not all of them are good quality attribute indicators. Moreover, the impact of these metrics is not different in proprietary and open source projects. The result provides some implications for building quality model across project type