In this study, we gather a very large data set from GitHub
(728 projects, 63 Million SLOC, 29,000 authors, 1.5 million commits, in 17 languages) in an attempt to shed some empirical light
on this question. This reasonably large sample size allows us to use
a mixed-methods approach, combining multiple regression modeling with visualization and text analytics, to study the effect of language features such as static
v.s.
dynamic typing, strong
v.s.
weak
typing on software quality.
From conclusion:
The data indicates functional languages are better
than procedural languages; it suggests that strong typing is better
than weak typing; that static typing is better than dynamic; and that
managed memory usage is better than unmanaged. Further, that
the defect proneness of languages in general is not associated with
software domains. Also, languages are more related to individual
bug categories than bugs overall.
1 comment
[ 2.7 ms ] story [ 14.8 ms ] threadIn this study, we gather a very large data set from GitHub (728 projects, 63 Million SLOC, 29,000 authors, 1.5 million commits, in 17 languages) in an attempt to shed some empirical light on this question. This reasonably large sample size allows us to use a mixed-methods approach, combining multiple regression modeling with visualization and text analytics, to study the effect of language features such as static v.s. dynamic typing, strong v.s. weak typing on software quality.
From conclusion:
The data indicates functional languages are better than procedural languages; it suggests that strong typing is better than weak typing; that static typing is better than dynamic; and that managed memory usage is better than unmanaged. Further, that the defect proneness of languages in general is not associated with software domains. Also, languages are more related to individual bug categories than bugs overall.