In software engineering, relying on experience can render maintainability expertise into prejudice over time. For example, based on their own experience, some consider JavaScript as inelegant language and hence of lowest maintainability. Such prejudice should not guide decisions without prior empirical validation.
@cite_37 @cite_29 present a similar study on code quality of GitHub code. They investigate the effect of programming languages and their properties as well as the impact of application domains on code quality. In contrast to this study, they operationalize code quality by the number of bug fix commits. Bissyand ' @cite_10 present another similar study on software quality of GitHub repositories. They investigate the impact of programming languages on project success, code quality, and team size. In contrast to this study, they measure code quality by the number of issue reports. We offer the complementary view of static analysis.
@cite_37 @cite_29 @cite_10
2142958724 2759963882 2404987555 1992074508
What is the effect of programming languages on software quality? This question has been a topic of much debate for a very long time. In this study, we gather a very large data set from GitHub (729 projects, 80 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. By triangulating findings from different methods, and controlling for confounding effects such as team size, project size, and project history, we report that language design does have a significant, but modest effect on software quality. Most notably, it does appear that strong typing is modestly better than weak typing, and among functional languages, static typing is also somewhat better than dynamic typing. We also find that functional languages are somewhat better than procedural languages. It is worth noting that these modest effects arising from language design are overwhelmingly dominated by the process factors such as project size, team size, and commit size. However, we hasten to caution the reader that even these modest effects might quite possibly be due to other, intangible process factors, e.g., the preference of certain personality types for functional, static and strongly typed languages. What is the effect of programming languages on software quality? This question has been a topic of much debate for a very long time. 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 versus dynamic typing and allowing versus disallowing type confusion on software quality. By triangulating findings from different methods, and controlling for confounding effects such as team size, project size, and project history, we report that language design does have a significant, but modest effect on software quality. Most notably, it does appear that disallowing type confusion is modestly better than allowing it, and among functional languages, static typing is also somewhat better than dynamic typing. We also find that functional languages are somewhat better than procedural languages. It is worth noting that these modest effects arising from language design are overwhelmingly dominated by the process factors such as project size, team size, and commit size. However, we caution the reader that even these modest effects might quite possibly be due to other, intangible process factors, for example, the preference of certain personality types for functional, static languages that disallow type confusion. Nowadays, most software use multiple programming languages to implement certain functionalities based on the strengths and weaknesses of different languages. Researchers in the past have studied the impact of independent programming languages on software quality, however, there has been little or no research on the impact of multiple languages on the quality of software. Does the use of multiple languages cause more bugs? Are certain languages when used with other languages make software more bug prone? What are the relationships between multi-language usage and various bug categories? In this study, we perform a large scale empirical investigation to provide some answers to these questions. We gather a large dataset consisting of popular projects from GitHub (628 projects, 85 million SLOC, 134 thousand authors, 3 million commits, in 17 languages) to understand the impact of using multiple languages on software quality. We build multiple regression models to study the effects of using different languages on the number of bug fixing commits while controlling for factors such as project age, project size, team size, and the number of commits. Our results show that in general implementing a project with more languages has a significant effect on project quality, as it increases defect proneness. Moreover, we find specific languages that are statistically significantly more defect prone when they are used in a multi-language setting. These include popular languages like C++, Objective-C, and Java. Furthermore, we note that the use of more languages significantly increases bug proneness across all bug categories. The effect is strongest for memory, concurrency, and algorithm bugs. Eleven open source software projects were analyzed to determine if the number of committing developers impacts code quality. We use cyclomatic complexity, lines of code per function, comment density, and maximum nesting as surrogate measures of code quality. We find no significant evidence to suggest that the number of committing developers affects the quality of software.