The impact of software complexity on cost and quality - A comparative analysis between Open source and proprietary software

TL;DR

This paper systematically reviews how software complexity metrics influence quality attributes like fault proneness and maintainability, comparing open source and proprietary projects, and finds similar impacts across both types.

Contribution

It provides a comprehensive meta-analysis of complexity metrics' effects on quality attributes across diverse studies and project types, highlighting the relevance of specific metrics.

Findings

Fault proneness and maintainability are most affected by complexity metrics.

Chidamber and Kemerer metrics are widely used but vary in effectiveness.

Impact of complexity metrics is similar in open source and proprietary software.

Abstract

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