# On the Impact of Programming Languages on Code Quality

## TL;DR

This study replicates and critically reanalyzes prior research on programming languages and code defects, finding fewer significant associations and emphasizing the importance of rigorous statistical methods.

## Contribution

It provides a thorough reanalysis of previous findings, identifying flaws and refining conclusions about language-defect associations in software projects.

## Key findings

- Only four languages show significant defect associations
- Effect sizes for these associations are very small
- Original claims about eleven languages are not fully supported

## Abstract

This paper is a reproduction of work by Ray et al. which claimed to have uncovered a statistically significant association between eleven programming languages and software defects in projects hosted on GitHub. First we conduct an experimental repetition, repetition is only partially successful, but it does validate one of the key claims of the original work about the association of ten programming languages with defects. Next, we conduct a complete, independent reanalysis of the data and statistical modeling steps of the original study. We uncover a number of flaws that undermine the conclusions of the original study as only four languages are found to have a statistically significant association with defects, and even for those the effect size is exceedingly small. We conclude with some additional sources of bias that should be investigated in follow up work and a few best practice recommendations for similar efforts.