Bug prediction modeling using complexity of code changes

Abstract

Researchers have proposed and implemented a plethora of bug prediction approaches in terms of different mathematical models for measuring the reliability growth of the software and to predict the latent bugs lying dormant in the software. During the last four decades, software reliability growth models (SRGM) have been successfully used to measure the reliability growth of closed source software. The SRGM developed were based on either calendar time or on testing effort. In late 90s, due to the advancement in communication and internet technologies, the development of open source software gets an edge and is proven to be very successful in different fields. Recently, researchers have measured the latent bugs in the open source software using an SRGM which has been developed for closed source software and concluded that the existing SRGM can well predict the latent bugs, but, still, it needs more investigation. In open source software, the source codes are frequently changes (the complexity of code changes) to meet the new feature introduction, feature enhancement and bug repair. In this paper, we have developed two complexity of code changes/entropy based bug prediction models namely (i) time vs entropy and (ii) entropy vs bugs. We have compared the proposed models with the existing time vs bugs SRGM. The empirical work has been carried out using three subsystems of Mozilla project. The statistical significance of different approaches has been tested using a non-parametric Kolmogorov–Smirnov (K–S) test. The bug prediction approaches have been compared on the basis of various performance measures namely R-Square (R2), Adjusted R-Square (adj. R2), Bias, variation and root mean square prediction errors. We found that the potential complexity of code changes based bug prediction approach i.e. time vs entropy is better over the time vs bugs and entropy vs bugs on the basis of different comparison criteria and statistical test.