How TypeScript Compiler Options Influence Code Quality: A Correlation Study with Git Repositories
DOI:
https://doi.org/10.5753/jserd.2026.5985Keywords:
JavaScript, TypeScript, Compiler Configurations, Code Quality, Static Analysis, GitHub MiningAbstract
TypeScript is widely adopted in modern software development to improve code reliability through static typing. However, its effectiveness in promoting code quality depends heavily on the compiler options configured within each project. Despite its popularity, little is known about how these compiler configurations influence code quality in practice. This study investigates the relationship between TypeScript compiler options and code quality metrics in real-world software projects. We analyzed over 4,000 public GitHub repositories that use TypeScript, through an automated four-stage pipeline comprising data mining, compiler configuration extraction, static quality assessment, and statistical correlation analysis. Projects were selected based on predefined criteria, and their tsconfig.json files were parsed to extract compiler settings. SonarQube was employed to evaluate bugs and code smells, aggregated into a composite Static Issues Index (SII), which was then correlated with specific compiler options. The results show that stricter compiler settings, such as noImplicitAny and noUnusedParameters, tend to correlate with fewer bugs and code smells. However, some options behave counterintuitively at scale; for instance, enabling noFallthroughCasesInSwitch and noUncheckedIndexedAccess correlated positively with bug counts in our dataset, suggesting the presence of confounding factors rather than a direct causal effect. These findings provide large-scale empirical evidence that compiler configurations in TypeScript are associated with measurable differences in static code quality. The study offers practical guidance for developers seeking to enforce best practices through TypeScript's configuration mechanisms and contributes an empirical perspective to the software engineering community.
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Copyright (c) 2026 Thomaz Zandonotto, Alex Roehrs, Kleinner Silva Farias de Oliveira, Jorge Luis Victória Barbosa, Cristiano André da Costa, Rodrigo da Rosa Righi
This work is licensed under a Creative Commons Attribution 4.0 International License.
